# learning

**Learning Philosophy**:

- The Power of Tiny Gains
- Master Adjacent Disciplines
- T-shaped skills
- Data Scientists Should Be More End-to-End

### Develop a business acumen

- [X] Book: Delivering Happiness
- [X] Book: Good to Great: Why Some Companies Make the Leap...And Others Don't
- [X] Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
- [X] Book: How Google Works
- [X] Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business
- [X] Book: Rework
- [X] Book: The Airbnb Story
- [X] Book: The Personal MBA
- [X] Facebook: Digital marketing: get started
- [X] Facebook: Digital marketing: go further
- [X] Google Analytics for Beginners
- [ ] Google: Fundamentals of Digital Marketing
- [X] Moz: The Beginner's Guide to SEO
- [X] Smartly: Marketing Fundamentals
- [X] Treehouse: SEO Basics
- [ ] Udacity: App Monetization
- [ ] Udacity: App Marketing
- [ ] Udacity: Get Your Startup Started
- [ ] Udacity: How to Build a Startup
- [ ] Youtube: SEO Unlocked
- [X] Welcome to the SEO Unlocked
`0:10:09`

- [X] Introduction to SEO and Why It's Important
`0:10:29`

- [ ] Keyword Research Part 1
`0:19:20`

- [ ] Keyword Research Part 2
`0:09:56`

- [ ] On-page and technical SEO Part 1
`0:22:58`

- [ ] On-page and technical SEO Part 2
`0:12:16`

- [ ] Mastering Technical SEO Audits
`0:16:35`

- [ ] Content Marketing Part 1
`0:24:09`

- [ ] Advanced Content Marketing Tactics
`0:09:54`

- [ ] The 10 Commandments of Content Marketing
`0:19:01`

- [ ] How to Edit Your Content For SEO
`0:10:59`

- [ ] Discover Your Competitive Strategy
`0:09:12`

- [ ] Over 4 Million Backlinks Built With This Simple
Process
`0:11:09`

- [ ] How to Get POWERFUL Backlinks for Faster
Rankings
`0:09:40`

- [ ] Get THOUSANDS of Backlinks On Semi-Autopilot
`0:06:32`

- [ ] How To Get The Most Out Of Google Analytics
`0:07:45`

- [ ] How to Setup Google Search Console
`0:09:21`

- [ ] How to Use Advanced Features in Google
Analytics
`0:10:52`

- [ ] A Deep Dive Into Branding, Data &
Experience
`0:14:03`

- [ ] How To Create A Compelling Brand
`0:05:52`

- [ ] Designing Your Customer Experience & Case
Studies
`0:07:32`

- [X] Welcome to the SEO Unlocked
- [ ] Youtube: Webinars From The Future | Session One: Design Thinking
- [ ] Youtube: Webinars From The Future | Session Two: Interaction Design

### Be able to frame a ML problem

- [X] AWS: Types of Machine Learning Solutions
- [X] Article: Apply Machine Learning to your Business
- [X] Book: AI Superpowers: China, Silicon Valley, and the New World Order
- [X] Book: A Human's Guide to Machine Intelligence
- [X] Book: The Future Computed
- [ ] Book: Machine Learning Yearning by Andrew Ng
- [X] Book: Prediction Machines: The Simple Economics of Artificial Intelligence
- [ ] Book: Building Machine Learning Powered Applications: Going from Idea to Product
- [X] Coursera: AI For Everyone
- [ ] Datacamp: Case Studies in Statistical Thinking
- [X] Datacamp: Data Science for Everyone
- [X] Datacamp: Machine Learning with the Experts: School Budgets
- [X] Datacamp: Machine Learning for Everyone
- [ ] Datacamp: Analyzing Police Activity with pandas
- [X] Datacamp: Data Science for Managers
- [X] Facebook: Field Guide to Machine Learning
- [ ] Google: Art and Science of Machine Learning
- [ ] Google: How Google does Machine Learning
- [X] Google: Introduction to Machine Learning Problem Framing
- [ ] Microsoft: Define an AI strategy to create business value
- [ ] Microsoft: Discover ways to foster an AI-ready culture in your business
- [ ] Microsoft: Identify guiding principles for responsible AI in your business
- [ ] Microsoft: Introduction to AI technology for business leaders
- [X] Pluralsight: How to Think About Machine Learning Algorithms
- [ ] Udacity: Problem Solving with Advanced Analytics
- [X] Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
- [X] Youtube: Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2
- [X] Youtube: How does YouTube recommend videos? - AI
EXPLAINED!
`0:33:53`

- [X] Youtube: How does Google Translate's AI work?
`0:15:02`

- [X] Youtube: Data Science in Finance
`0:17:52`

- [X] Youtube: The Age of AI
- [X] How Far is Too Far? | The Age of A.I.
`0:34:39`

- [X] Healed through A.I. | The Age of A.I.
`0:39:55`

- [X] Using A.I. to build a better human | The Age of
A.I.
`0:44:27`

- [X] Love, art and stories: decoded | The Age of
A.I.
`0:38:57`

- [X] The 'Space Architects' of Mars | The Age of
A.I.
`0:30:10`

- [X] Will a robot take my job? | The Age of A.I.
`0:36:14`

- [X] Saving the world one algorithm at a time | The Age
of A.I.
`0:46:37`

- [X] How A.I. is searching for Aliens | The Age of
A.I.
`0:36:12`

- [X] How Far is Too Far? | The Age of A.I.
- [ ] Youtube: Gradient Dissent Podcast
- [ ] DeepChem creator Bharath Ramsundar on using deep
learning for molecules and medicine discovery
`0:55:11`

- [X] ML Research and Production Pipelines with Chip
Huyen
`0:43:07`

- [ ] Product Management for AI with Peter Skomoroch
`1:28:14`

- [X] Slow down and change one thing at a time -
Advancing AI research with Josh Tobin
`0:48:19`

- [ ] Societal Impacts of Artificial Intelligence with
Miles Brundage
`1:02:25`

- [ ] Deep Reinforcement Learning and Robotics with Peter
Welinder
`0:54:22`

- [X] Machine learning across industries with Vicki
Boykis
`0:34:02`

- [ ] Designing ML models for millions of consumer robots
- Angela Bassa and Danielle Dean
`0:52:38`

- [ ] Building trustworthy AI systems and combating
potential malicious use – A conversation w/ Jack Clark
`0:55:56`

- [X] Rachael Tatman - Conversational A.I. and
Linguistics
`0:36:51`

- [ ] Nicolas Koumchatzky - Machine Learning in
Production for Self Driving Cars
`0:44:56`

- [ ] Brandon Rohrer - Machine Learning in Production for
Robots
`0:34:31`

- [ ] DeepChem creator Bharath Ramsundar on using deep
learning for molecules and medicine discovery

### Understand data ethics better

- [ ] Practical
Data Ethics
- [ ] Lesson 1: Disinformation
- [ ] Lesson 2: Bias & Fairness
- [ ] Lesson 3: Ethical Foundations & Practical Tools
- [ ] Lesson 4: Privacy and surveillance
- [ ] Lesson 4 continued: Privacy and surveillance
- [ ] Lesson 5.1: The problem with metrics
- [ ] Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth
- [ ] Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib
- [ ] Lesson 6: Algorithmic Colonialism, and Next Steps

### Be able to annotate data efficiently

- [X] Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
- [X] Youtube: Training a NER Model with Prodigy and Transfer Learning
- [X] Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning

### Be able to manipulate data with Numpy

- [X] Datacamp: Intro to Python for Data Science
- [X] Pluralsight: Working with Multidimensional Data Using NumPy

### Be able to manipulate data with Pandas

- [X] Datacamp: pandas Foundations
- [ ] Datacamp: Pandas Joins for Spreadsheet Users
- [X] Datacamp: Manipulating DataFrames with pandas
- [ ] Datacamp: Merging DataFrames with pandas
- [ ] Datacamp: Data Manipulation with pandas
- [ ] Datacamp: Optimizing Python Code with pandas
- [ ] Datacamp: Streamlined Data Ingestion with pandas
- [ ] Datacamp: Analyzing Marketing Campaigns with pandas
- [ ] Article: Modern Pandas

### Be able to manipulate data in spreadsheets

- [X] Datacamp: Spreadsheet basics
- [ ] Datacamp: Data Analysis with Spreadsheets
- [ ] Datacamp: Intermediate Spreadsheets for Data Science
- [ ] Datacamp: Pivot Tables with Spreadsheets
- [ ] Datacamp: Data Visualization in Spreadsheets
- [ ] Datacamp: Introduction to Statistics in Spreadsheets
- [ ] Datacamp: Conditional Formatting in Spreadsheets
- [ ] Datacamp: Marketing Analytics in Spreadsheets
- [ ] Datacamp: Error and Uncertainty in Spreadsheets
- [X] edX: Analyzing and Visualizing Data with Excel

### Be able to manipulate data in databases

- [X] Codecademy: SQL Track
- [X] Datacamp: Intro to SQL for Data Science
- [ ] Datacamp: Introduction to MongoDB in Python
- [ ] Datacamp: Intermediate SQL
- [ ] Datacamp: Exploratory Data Analysis in SQL
- [ ] Datacamp: Joining Data in PostgreSQL
- [X] Datacamp: Querying with TransactSQL
- [ ] Datacamp: Introduction to Databases in Python
- [ ] Datacamp: Reporting in SQL
- [ ] Datacamp: Applying SQL to Real-World Problems
- [ ] Datacamp: Analyzing Business Data in SQL
- [ ] Datacamp: Data-Driven Decision Making in SQL
- [ ] Datacamp: Database Design
- [ ] Udacity: SQL for Data Analysis
- [ ] Udacity: Intro to relational database
- [ ] Udacity: Database Systems Concepts & Design

### Be able to use the command line

- [X] Codecademy: Learn the Command Line
- [X] Datacamp: Introduction to Shell for Data Science
- [ ] Datacamp: Data Processing in Shell
- [ ] LaunchSchool: Introduction to Commandline
- [ ] Learn Enough Command Line to be dangerous
- [ ] Thoughtbot: Mastering the Shell
- [ ] Thoughtbot: tmux
- [X] Udacity: Linux Command Line Basics
- [ ] Udacity: Linux Web Servers
- [X] Udacity: Shell Workshop
- [ ] Udacity: Web Tooling & Automation
- [ ] Web Bos: Command Line Power User

### Be able to import data from multiple sources

### Be able to perform feature engineering

- [ ] Article: Preparing data for a machine learning model
- [ ] Article: Feature selection for a machine learning model
- [ ] Article: Learning from imbalanced data
- [ ] Article: Hacker's Guide to Data Preparation for Machine Learning
- [ ] Article: Practical Guide to Handling Imbalanced Datasets
- [ ] Datacamp: Analyzing Social Media Data in Python
- [X] Datacamp: Dimensionality Reduction in Python
- [X] Datacamp: Preprocessing for Machine Learning in Python
- [X] Datacamp: Data Types for Data Science
- [X] Datacamp: Cleaning Data in Python
- [X] Datacamp: Feature Engineering for Machine Learning in Python
- [ ] Datacamp: Importing & Managing Financial Data in Python
- [ ] Datacamp: Manipulating Time Series Data in Python
- [ ] Datacamp: Working with Geospatial Data in Python
- [ ] Datacamp: Analyzing IoT Data in Python
- [ ] Datacamp: Dealing with Missing Data in Python
- [ ] Datacamp: Exploratory Data Analysis in Python
- [X] edX: Data Science Essentials
- [ ] Google: Feature Engineering
- [ ] Udacity: Creating an Analytical Dataset

### Be able to experiment in notebook

### Be able to visualize data

- [ ] Datacamp: Introduction to Data Visualization with Python
- [X] Datacamp: Introduction to Seaborn
- [X] Datacamp: Introduction to Matplotlib
- [ ] Datacamp: Intermediate Data Visualization with Seaborn
- [ ] Datacamp: Visualizing Time Series Data in Python
- [ ] Datacamp: Improving Your Data Visualizations in Python
- [ ] Datacamp: Visualizing Geospatial Data in Python
- [ ] Datacamp: Interactive Data Visualization with Bokeh
- [ ] Udacity: Data Visualization in Tableau
- [ ] Youtube: Jake VanderPlas - Exploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018
- [ ] UWData: Data Visualization Curriculum

### Be able to to read research papers

- [X] Paper: A Neural Probabilistic Language Model
- [ ] Paper: Efficient Estimation of Word Representations in Vector Space
- [X] Paper: Sequence to Sequence Learning with Neural Networks
- [X] Paper: Neural Machine Translation by Jointly Learning to Align and Translate
- [X] Paper: Attention Is All You Need
- [X] Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- [X] Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
- [X] Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDF
- [ ] Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
- [ ] Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- [X] Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
- [X] Paper: Collaborative Filtering for Implicit Feedback Datasets
- [ ] Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
- [X] Paper: Factorization Machines
- [X] Paper: Wide & Deep Learning for Recommender Systems
- [X] Paper: Neural Factorization Machines for Sparse Predictive Analytics
- [X] Paper: Multiword Expressions: A Pain in the Neck for NLP
- [X] Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
- [X] Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
- [X] Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
- [X] Paper: A Simple Framework for Contrastive Learning of Visual Representations
- [X] Paper: Self-Supervised Learning of Pretext-Invariant Representations
- [X] Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- [X] Paper: Self-Labelling via Simultaneous Clustering and Representation Learning
- [ ] Paper: A Survey on Contextual Embeddings
- [X] Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
- [ ] Paper: Shortcut Learning in Deep Neural Networks
- [X] Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia
- [X] Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
- [X] Paper: Zero-shot Text Classification With Generative Language Models
- [X] Paper: How to Fine-Tune BERT for Text Classification?
- [X] Paper: Universal Sentence Encoder
- [X] Paper: Enriching Word Vectors with Subword Information
- [ ] Paper: Deep Learning Based Text Classification: A Comprehensive Review
- [X] Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
- [X] Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
- [ ] Paper: Temporal Ensembling for Semi-Supervised Learning
- [X] Whitepaper: Architecting for the Cloud AWS Best Practices
- [X] Whitepaper: AWS Well-Architected Framework
- [X] Whitepaper: AWS Security Best Practices
- [X] Whitepaper: Blue/Green Deployments on AWS
- [X] Whitepaper: Microservices on AWS
- [X] Whitepaper: Optimizing Enterprise Economics with Serverless Architectures
- [X] Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS
- [X] Whitepaper: Running Containerized Microservices on AWS
- [X] Whitepaper: Serverless Architectures with AWS Lambda

### Be able to model problems mathematically

- [ ] 3Blue1Brown: Essence of Calculus
- [ ] The Essence of Calculus, Chapter 1
`0:17:04`

- [ ] The paradox of the derivative | Essence of
calculus, chapter 2
`0:17:57`

- [ ] Derivative formulas through geometry | Essence of
calculus, chapter 3
`0:18:43`

- [ ] Visualizing the chain rule and product rule |
Essence of calculus, chapter 4
`0:16:52`

- [ ] What's so special about Euler's number e? | Essence
of calculus, chapter 5
`0:13:50`

- [ ] Implicit differentiation, what's going on here? |
Essence of calculus, chapter 6
`0:15:33`

- [ ] Limits, L'Hôpital's rule, and epsilon delta
definitions | Essence of calculus, chapter 7
`0:18:26`

- [ ] Integration and the fundamental theorem of calculus
| Essence of calculus, chapter 8
`0:20:46`

- [ ] What does area have to do with slope? | Essence of
calculus, chapter 9
`0:12:39`

- [ ] Higher order derivatives | Essence of calculus,
chapter 10
`0:05:38`

- [ ] Taylor series | Essence of calculus, chapter 11
`0:22:19`

- [ ] What they won't teach you in calculus
`0:16:22`

- [ ] The Essence of Calculus, Chapter 1
- [ ] 3Blue1Brown: Essence of linear algebra
- [ ] Vectors, what even are they? | Essence of linear
algebra, chapter 1
`0:09:52`

- [ ] Linear combinations, span, and basis vectors |
Essence of linear algebra, chapter 2
`0:09:59`

- [ ] Linear transformations and matrices | Essence of
linear algebra, chapter 3
`0:10:58`

- [ ] Matrix multiplication as composition | Essence of
linear algebra, chapter 4
`0:10:03`

- [ ] Three-dimensional linear transformations | Essence
of linear algebra, chapter 5
`0:04:46`

- [ ] The determinant | Essence of linear algebra,
chapter 6
`0:10:03`

- [ ] Inverse matrices, column space and null space |
Essence of linear algebra, chapter 7
`0:12:08`

- [ ] Nonsquare matrices as transformations between
dimensions | Essence of linear algebra, chapter 8
`0:04:27`

- [ ] Dot products and duality | Essence of linear
algebra, chapter 9
`0:14:11`

- [ ] Cross products | Essence of linear algebra, Chapter
10
`0:08:53`

- [ ] Cross products in the light of linear
transformations | Essence of linear algebra chapter 11
`0:13:10`

- [ ] Cramer's rule, explained geometrically | Essence of
linear algebra, chapter 12
`0:12:12`

- [ ] Change of basis | Essence of linear algebra,
chapter 13
`0:12:50`

- [ ] Eigenvectors and eigenvalues | Essence of linear
algebra, chapter 14
`0:17:15`

- [ ] Abstract vector spaces | Essence of linear algebra,
chapter 15
`0:16:46`

- [ ] Vectors, what even are they? | Essence of linear
algebra, chapter 1
- [ ] 3Blue1Brown: Neural networks
- [X] Article: A Visual Tour of Backpropagation
- [ ] Article: Relearning Matrices as Linear Functions
- [ ] Article: You Could Have Come Up With Eigenvectors - Here's How
- [ ] Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Search
- [ ] Article: Interactive Visualization of Why Eigenvectors Matter
- [ ] Article: Cross-Entropy and KL Divergence
- [ ] Article: Why Randomness Is Information?
- [ ] Article: Basic Probability Theory
- [ ] Book: Basics of Linear Algebra for Machine Learning
- [ ] Datacamp: Foundations of Probability in Python
- [ ] Datacamp: Statistical Thinking in Python (Part 1)
- [ ] Datacamp: Statistical Thinking in Python (Part 2)
- [ ] Datacamp: Statistical Simulation in Python
- [X] edX: Essential Statistics for Data Analysis using Excel
- [ ] Computational Linear Algebra for Coders
- [ ] Khan Academy: Precalculus
- [ ] Khan Academy: Probability
- [ ] Khan Academy: Differential Calculus
- [ ] Khan Academy: Multivariable Calculus
- [ ] Khan Academy: Linear Algebra
- [ ] MIT: 18.06 Linear Algebra (Professor Strang)
- [X] 1. The Geometry of Linear Equations
`0:39:49`

- [X] 2. Elimination with Matrices.
`0:47:41`

- [X] 3. Multiplication and Inverse Matrices
`0:46:48`

- [X] 4. Factorization into A = LU
`0:48:05`

- [X] 5. Transposes, Permutations, Spaces R^n
`0:47:41`

- [X] 6. Column Space and Nullspace
`0:46:01`

- [X] 9. Independence, Basis, and Dimension
`0:50:14`

- [X] 10. The Four Fundamental Subspaces
`0:49:20`

- [X] 11. Matrix Spaces; Rank 1; Small World Graphs
`0:45:55`

- [X] 14. Orthogonal Vectors and Subspaces
`0:49:47`

- [X] 15. Projections onto Subspaces
`0:48:51`

- [X] 16. Projection Matrices and Least Squares
`0:48:05`

- [X] 17. Orthogonal Matrices and Gram-Schmidt
`0:49:09`

- [X] 21. Eigenvalues and Eigenvectors
`0:51:22`

- [ ] 22. Diagonalization and Powers of A
`0:51:50`

- [ ] 24. Markov Matrices; Fourier Series
`0:51:11`

- [ ] 25. Symmetric Matrices and Positive
Definiteness
`0:43:52`

- [ ] 27. Positive Definite Matrices and Minima
`0:50:40`

- [ ] 29. Singular Value Decomposition
`0:40:28`

- [ ] 30. Linear Transformations and Their Matrices
`0:49:27`

- [ ] 31. Change of Basis; Image Compression
`0:50:13`

- [ ] 33. Left and Right Inverses; Pseudoinverse
`0:41:52`

- [X] 1. The Geometry of Linear Equations
- [ ] StatQuest: Statistics Fundamentals
- [ ] StatQuest: Histograms, Clearly Explained
`0:03:42`

- [ ] StatQuest: What is a statistical distribution?
`0:05:14`

- [ ] StatQuest: The Normal Distribution, Clearly
Explained!!!
`0:05:12`

- [ ] Statistics Fundamentals: Population Parameters
`0:14:31`

- [ ] Statistics Fundamentals: The Mean, Variance and
Standard Deviation
`0:14:22`

- [ ] StatQuest: What is a statistical model?
`0:03:45`

- [ ] StatQuest: Sampling A Distribution
`0:03:48`

- [ ] Hypothesis Testing and The Null Hypothesis
`0:14:40`

- [ ] Alternative Hypotheses: Main Ideas!!!
`0:09:49`

- [ ] p-values: What they are and how to interpret
them
`0:11:22`

- [ ] How to calculate p-values
`0:25:15`

- [ ] p-hacking: What it is and how to avoid it!
`0:13:44`

- [ ] Statistical Power, Clearly Explained!!!
`0:08:19`

- [ ] Power Analysis, Clearly Explained!!!
`0:16:44`

- [ ] Covariance and Correlation Part 1: Covariance
`0:22:23`

- [ ] Covariance and Correlation Part 2: Pearson's
Correlation
`0:19:13`

- [ ] StatQuest: R-squared explained
`0:11:01`

- [ ] The Central Limit Theorem
`0:07:35`

- [ ] StatQuickie: Standard Deviation vs Standard
Error
`0:02:52`

- [ ] StatQuest: The standard error
`0:11:43`

- [ ] Bam!!! Clearly Explained!!!
`0:02:49`

- [ ] StatQuest: Technical and Biological Replicates
`0:05:27`

- [ ] StatQuest - Sample Size and Effective Sample Size,
Clearly Explained
`0:06:32`

- [ ] Bar Charts Are Better than Pie Charts
`0:01:45`

- [ ] StatQuest: Boxplots, Clearly Explained
`0:02:33`

- [ ] StatQuest: Logs (logarithms), clearly explained
`0:15:37`

- [ ] StatQuest: Confidence Intervals
`0:06:41`

- [ ] StatQuickie: Thresholds for Significance
`0:06:40`

- [ ] StatQuickie: Which t test to use
`0:05:10`

- [ ] StatQuest: One or Two Tailed P-Values
`0:07:05`

- [ ] The Binomial Distribution and Test, Clearly
Explained!!!
`0:15:46`

- [ ] StatQuest: Quantiles and Percentiles, Clearly
Explained!!!
`0:06:30`

- [ ] StatQuest: Quantile-Quantile Plots (QQ plots),
Clearly Explained
`0:06:55`

- [ ] StatQuest: Quantile Normalization
`0:04:51`

- [ ] StatQuest: Probability vs Likelihood
`0:05:01`

- [ ] StatQuest: Maximum Likelihood, clearly
explained!!!
`0:06:12`

- [ ] Maximum Likelihood for the Exponential
Distribution, Clearly Explained! V2.0
`0:09:39`

- [ ] Why Dividing By N Underestimates the Variance
`0:17:14`

- [ ] Maximum Likelihood for the Binomial Distribution,
Clearly Explained!!!
`0:11:24`

- [ ] Maximum Likelihood For the Normal Distribution,
step-by-step!
`0:19:50`

- [ ] StatQuest: Odds and Log(Odds), Clearly
Explained!!!
`0:11:30`

- [ ] StatQuest: Odds Ratios and Log(Odds Ratios),
Clearly Explained!!!
`0:16:20`

- [ ] Live 2020-04-20!!! Expected Values
`0:33:00`

- [ ] StatQuest: Histograms, Clearly Explained
- [ ] Udacity: Algebra Review
- [ ] Udacity: Differential Equations in Action
- [X] Udacity: Eigenvectors and Eigenvalues
- [ ] Udacity: Linear Algebra Refresher
- [ ] Udacity: Statistics
- [ ] Udacity: Intro to Descriptive Statistics
- [ ] Udacity: Intro to Inferential Statistics
- [ ] Youtube: Principal Component Analysis (PCA) - THE
MATH YOU SHOULD KNOW!
`0:10:06`

- [ ] Youtube: Support Vector Machines - THE MATH YOU
SHOULD KNOW
`0:11:21`

- [ ] Youtube: The Kernel Trick - THE MATH YOU SHOULD
KNOW!
`0:07:29`

- [ ] Youtube: Logistic Regression - THE MATH YOU SHOULD
KNOW!
`0:09:14`

- [ ] Youtube: But what
*is*a Neural Network? - THE MATH YOU SHOULD KNOW!`0:19:07`

### Be able to structure machine learning projects

- [ ] Article: Organizing machine learning projects: project management guidelines
- [ ] Article: Building machine learning products: a problem well-defined is a problem half-solved.
- [ ] Coursera: Structuring Machine Learning Projects
- [X] Datacamp: Conda Essentials
- [ ] Datacamp: Conda for Building & Distributing Packages
- [X] Datacamp: Creating Robust Python Workflows
- [X] Datacamp: Software Engineering for Data Scientists in Python
- [X] Datacamp: Designing Machine Learning Workflows in Python
- [X] Datacamp: Object-Oriented Programming in Python
- [ ] Datacamp: Command Line Automation in Python
- [ ] Datacamp: Introduction to Data Engineering
- [ ] Datacamp: Experimental Design in Python
- [X] Full Stack Deep Learning Bootcamp: March 2019
- [X] Lecture 1: Introduction to Deep Learning
- [X] Lecture 2: Setting Up Machine Learning Projects
- [X] Lecture 3: Introduction to the Text Recognizer Project
- [X] Lecture 4: Infrastructure and Tooling
- [X] Lecture 5: Tracking Experiments
- [X] Lecture 6: Data Management
- [X] Lecture 7: Machine Learning Teams
- [X] Lecture 9: Lukas Biewald
- [X] Lecture 10: Troubleshooting Deep Neural Networks
- [X] Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment
- [X] Lecture 12: Testing and Deployment
- [X] Lecture 13: Research Directions
- [X] Lecture 14: Jeremy Howard
- [X] Lecture 15: Richard Socher
- [X] Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning

- [ ] MIT: The Missing Semester of CS Education
- [ ] Lecture 1: Course Overview + The Shell (2020)
`0:48:16`

- [ ] Lecture 2: Shell Tools and Scripting (2020)
`0:48:55`

- [ ] Lecture 3: Editors (vim) (2020)
`0:48:26`

- [ ] Lecture 4: Data Wrangling (2020)
`0:50:03`

- [ ] Lecture 5: Command-line Environment (2020)
`0:56:06`

- [ ] Lecture 6: Version Control (git) (2020)
`1:24:59`

- [ ] Lecture 7: Debugging and Profiling (2020)
`0:54:13`

- [ ] Lecture 8: Metaprogramming (2020)
`0:49:52`

- [ ] Lecture 9: Security and Cryptography (2020)
`1:00:59`

- [ ] Lecture 10: Potpourri (2020)
`0:57:54`

- [ ] Lecture 11: Q&A (2020)
`0:53:52`

- [ ] Lecture 1: Course Overview + The Shell (2020)
- [X] Treehouse: Object Oriented Python
- [X] Treehouse: Setup Local Python Environment
- [ ] Udacity: Writing READMEs
- [X] Youtube: Weights and Biases Tutorial
- [ ] Youtube: MLOps Tutorials
- [ ] MLOps Tutorial #1: Intro to Continuous Integration
for ML
`17:44:00`

- [ ] MLOps Tutorial #2: When data is too big for Git
`10:51:00`

- [ ] MLOps Tutorial #3: Track ML models with Git &
GitHub Actions
`14:12:00`

- [ ] MLOps Tutorial #1: Intro to Continuous Integration
for ML

### Be able to utilize version control

- [ ] Article: Mastering Git Stash Workflow
- [X] Codecademy: Learn Git
- [X] Code School: Git Real
- [X] Datacamp: Introduction to Git for Data Science
- [ ] Learn enough git to be dangerous
- [ ] Thoughtbot: Mastering Git
- [X] Udacity: GitHub & Collaboration
- [X] Udacity: How to Use Git and GitHub
- [X] Udacity: Version Control with Git

### Be familiar with a breadth of models and algorithms

- [ ] Article: Label Smoothing Explained using Microsoft Excel
- [ ] Article: Naive Bayes classification
- [ ] Article: Linear regression
- [ ] Article: Polynomial regression
- [ ] Article: Logistic regression
- [ ] Article: Decision trees
- [ ] Article: K-nearest neighbors
- [ ] Article: Support Vector Machines
- [ ] Article: Random forests
- [ ] Article: Boosted trees
- [ ] Article: Neural networks: activation functions
- [ ] Article: Neural networks: training with backpropagation
- [ ] Article: Gradient descent
- [ ] Article: Setting the learning rate of your neural network
- [ ] Article: Deep neural networks: preventing overfitting
- [ ] Article: Normalizing your data (specifically, input and batch normalization)
- [ ] Article: Batch Normalization
- [ ] Article: Baidu Deep Voice explained: Part 1 — the Inference Pipeline
- [ ] Article: Baidu Deep Voice explained Part 2 — Training
- [ ] Article: Hacker's Guide to Fundamental Machine Learning Algorithms with Python
- [ ] Article: Are Deep Neural Networks Dramatically Overfitted?
- [ ] Article: Attention? Attention!
- [ ] Article: How to Explain the Prediction of a Machine Learning Model?
- [ ] Article: Neural Network from scratch-part 1
- [ ] Article: Neural Network from scratch-part 2
- [ ] Article: Explain Neural Arithmetic Logic Units (NALU)
- [ ] Article: Predict Bitcoin price with Long sort term memory Networks (LSTM)
- [ ] Article: Graph Neural Networks - An overview
- [ ] Article: Deep Learning Algorithms - The Complete Guide
- [X] AWS: Semantic Segmentation Explained
- [X] AWS: The Elements of Data Science
- [X] AWS: Understanding Neural Networks
- [ ] Book: Pattern Recognition and Machine Learning
- [ ] Coursera: Neural Networks and Deep Learning
- [X] Datacamp: AI Fundamentals
- [X] Datacamp: Kaggle Competition
- [ ] Datacamp: Extreme Gradient Boosting with XGBoost
- [ ] Datacamp: Introduction to PySpark
- [ ] Datacamp: Building Recommendation Engines with PySpark
- [ ] Datacamp: Foundations of Predictive Analytics in Python (Part 1)
- [ ] Datacamp: Foundations of Predictive Analytics in Python (Part 2)
- [X] Datacamp: Ensemble Methods in Python
- [ ] Datacamp: HR Analytics in Python: Predicting Employee Churn
- [ ] Datacamp: Predicting Customer Churn in Python
- [X] Elements of AI
- [X] edX: Principles of Machine Learning
- [X] edX: Data Science Essentials
- [X] edX: Implementing Predictive Analytics with Spark in Azure HDInsight
- [X] DeepMind: Inefficient Data Efficiency
- [ ] DeepMind: DeepMind x UCL | Deep Learning Lecture
Series 2020
- [ ] DeepMind x UCL | Deep Learning Lectures | 1/12 |
Intro to Machine Learning & AI
`1:25:17`

- [ ] DeepMind x UCL | Deep Learning Lectures | 2/12 |
Neural Networks Foundations
`1:24:12`

- [ ] DeepMind x UCL | Deep Learning Lectures | 3/12 |
Convolutional Neural Networks for Image Recognition
`1:20:19`

- [ ] DeepMind x UCL | Deep Learning Lectures | 4/12 |
Advanced Models for Computer Vision
`1:33:37`

- [ ] DeepMind x UCL | Deep Learning Lectures | 5/12 |
Optimization for Machine Learning
`1:30:21`

- [ ] DeepMind x UCL | Deep Learning Lectures | 6/12 |
Sequences and Recurrent Networks
`1:20:27`

- [X] DeepMind x UCL | Deep Learning Lectures | 7/12 |
Deep Learning for Natural Language Processing
`1:32:29`

- [ ] DeepMind x UCL | Deep Learning Lectures | 8/12 |
Attention and Memory in Deep Learning
`1:36:04`

- [ ] DeepMind x UCL | Deep Learning Lectures | 9/12 |
Generative Adversarial Networks
`1:42:26`

- [ ] DeepMind x UCL | Deep Learning Lectures | 10/12 |
Unsupervised Representation Learning
`1:44:40`

- [ ] DeepMind x UCL | Deep Learning Lectures | 11/12 |
Modern Latent Variable Models
`1:28:26`

- [ ] DeepMind x UCL | Deep Learning Lectures | 12/12 |
Responsible Innovation
`1:02:28`

- [ ] DeepMind x UCL | Deep Learning Lectures | 1/12 |
Intro to Machine Learning & AI
- [ ] Fast.ai: Deep Learning for Coder (2020)
- [ ] Google: Launching into Machine Learning
- [ ] Book: Grokking Deep Learning
- [ ] Book: Make Your Own Neural Network
- [ ] MIT: 6.S191: Introduction to Deep Learning
- [ ] MIT Introduction to Deep Learning | 6.S191
`0:52:51`

- [ ] Recurrent Neural Networks | MIT 6.S191
`0:45:28`

- [ ] Convolutional Neural Networks | MIT 6.S191
`0:37:20`

- [ ] Deep Generative Modeling | MIT 6.S191
`0:40:39`

- [ ] Reinforcement Learning | MIT 6.S191
`0:44:11`

- [ ] Deep Learning New Frontiers | MIT 6.S191
`0:38:10`

- [ ] Neurosymbolic AI | MIT 6.S191
`0:41:10`

- [ ] Generalizable Autonomy for Robot Manipulation | MIT
6.S191
`0:47:00`

- [ ] Neural Rendering | MIT 6.S191
`0:36:44`

- [ ] Machine Learning for Scent | MIT 6.S191
`0:38:51`

- [ ] MIT Introduction to Deep Learning | 6.S191
- [X] Pluralsight: Understanding Algorithms for Recommendation Systems
- [X] Pluralsight: Deep Learning: The Big Picture
- [ ] StatQuest: Machine Learning
- [X] A Gentle Introduction to Machine Learning
`0:12:45`

- [X] Machine Learning Fundamentals: Cross Validation
`0:06:04`

- [X] Machine Learning Fundamentals: The Confusion
Matrix
`0:07:12`

- [X] Machine Learning Fundamentals: Sensitivity and
Specificity
`0:11:46`

- [X] Machine Learning Fundamentals: Bias and
Variance
`0:06:36`

- [ ] ROC and AUC, Clearly Explained!
`0:16:26`

- [ ] StatQuest: Fitting a line to data, aka least
squares, aka linear regression.
`0:09:21`

- [ ] StatQuest: Linear Models Pt.1 - Linear
Regression
`0:27:26`

- [ ] StatQuest: Odds and Log(Odds), Clearly
Explained!!!
`0:11:30`

- [ ] StatQuest: Odds Ratios and Log(Odds Ratios),
Clearly Explained!!!
`0:16:20`

- [ ] StatQuest: Logistic Regression
`0:08:47`

- [ ] Logistic Regression Details Pt1: Coefficients
`0:19:02`

- [ ] Logistic Regression Details Pt 2: Maximum
Likelihood
`0:10:23`

- [ ] Logistic Regression Details Pt 3: R-squared and
p-value
`0:15:25`

- [ ] Saturated Models and Deviance
`0:18:39`

- [ ] Deviance Residuals
`0:06:18`

- [ ] Regularization Part 1: Ridge (L2) Regression
`0:20:26`

- [ ] Regularization Part 2: Lasso (L1) Regression
`0:08:19`

- [ ] Ridge vs Lasso Regression, Visualized!!!
`0:09:05`

- [ ] Regularization Part 3: Elastic Net Regression
`0:05:19`

- [ ] StatQuest: Principal Component Analysis (PCA),
Step-by-Step
`0:21:57`

- [ ] StatQuest: PCA main ideas in only 5 minutes!!!
`0:06:04`

- [ ] StatQuest: PCA - Practical Tips
`0:08:19`

- [ ] StatQuest: PCA in Python
`0:11:37`

- [ ] StatQuest: Linear Discriminant Analysis (LDA)
clearly explained.
`0:15:12`

- [ ] StatQuest: MDS and PCoA
`0:08:18`

- [ ] StatQuest: t-SNE, Clearly Explained
`0:11:47`

- [ ] StatQuest: Hierarchical Clustering
`0:11:19`

- [ ] StatQuest: K-means clustering
`0:08:57`

- [ ] StatQuest: K-nearest neighbors, Clearly
Explained
`0:05:30`

- [ ] Naive Bayes, Clearly Explained!!!
`0:15:12`

- [ ] Gaussian Naive Bayes, Clearly Explained!!!
`0:09:41`

- [ ] StatQuest: Decision Trees
`0:17:22`

- [ ] StatQuest: Decision Trees, Part 2 - Feature
Selection and Missing Data
`0:05:16`

- [ ] Regression Trees, Clearly Explained!!!
`0:22:33`

- [ ] How to Prune Regression Trees, Clearly
Explained!!!
`0:16:15`

- [ ] StatQuest: Random Forests Part 1 - Building, Using
and Evaluating
`0:09:54`

- [ ] StatQuest: Random Forests Part 2: Missing data and
clustering
`0:11:53`

- [ ] The Chain Rule
`0:18:23`

- [ ] Gradient Descent, Step-by-Step
`0:23:54`

- [ ] Stochastic Gradient Descent, Clearly
Explained!!!
`0:10:53`

- [ ] AdaBoost, Clearly Explained
`0:20:54`

- [ ] Gradient Boost Part 1: Regression Main Ideas
`0:15:52`

- [ ] Gradient Boost Part 2: Regression Details
`0:26:45`

- [ ] Gradient Boost Part 3: Classification
`0:17:02`

- [ ] Gradient Boost Part 4: Classification Details
`0:36:59`

- [ ] Bam!!! Clearly Explained!!!
`0:02:49`

- [ ] Support Vector Machines, Clearly Explained!!!
`0:20:32`

- [ ] Support Vector Machines Part 2: The Polynomial
Kernel
`0:07:15`

- [ ] Support Vector Machines Part 3: The Radial (RBF)
Kernel
`0:15:52`

- [ ] XGBoost Part 1: Regression
`0:25:46`

- [ ] XGBoost Part 2: Classification
`0:25:17`

- [ ] XGBoost Part 3: Mathematical Details
`0:27:24`

- [ ] XGBoost Part 4: Crazy Cool Optimizations
`0:24:27`

- [ ] StatQuest: Fiitting a curve to data, aka lowess,
aka loess
`0:10:10`

- [ ] Statistics Fundamentals: Population Parameters
`0:14:31`

- [ ] Principal Component Analysis (PCA) clearly
explained (2015)
`0:20:16`

- [ ] Decision Trees in Python from Start to Finish
`1:06:23`

- [X] A Gentle Introduction to Machine Learning
- [ ] Udacity: A Friendly Introduction to Machine Learning
- [ ] Udacity: Intro to Data Analysis
- [ ] Udacity: Intro to Data Science
- [ ] Udacity: Intro to Machine Learning
- [ ] Udacity: Reinforcement Learning
- [ ] Udacity: Deep Learning
- [ ] Udacity: Intro to Artificial Intelligence
- [ ] Udacity: Classification Models
- [X] Youtube: DETR: End-to-End Object Detection with Transformers (Paper Explained)
- [X] Youtube: Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift
- [X] Youtube: How do we check if a neural network has learned a specific phenomenon?
- [X] Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation
- [ ] Youtube: AI fabricates music in a celebrity's voice
(JukeboxAI)
`0:15:54`

- [ ] Youtube: Activation Functions - EXPLAINED!
`0:10:05`

- [ ] Youtube: Batch Normalization - EXPLAINED!
`0:08:48`

- [ ] Youtube: Optimizers - EXPLAINED!
`0:07:22`

- [ ] Youtube: Loss Functions - EXPLAINED!
`0:08:30`

- [ ] Youtube: Boosting - EXPLAINED!
`0:17:31`

- [ ] Youtube: Gradient Descent - THE MATH YOU SHOULD
KNOW
`0:20:08`

- [ ] Youtube: Logistic Regression - VISUALIZED!
`0:18:31`

- [ ] Youtube: Linear Regression and Multiple
Regression
`0:12:54`

- [ ] Youtube: Precision, Recall & F-Measure
`0:13:42`

- [ ] Youtube: Bootstrapping, Bagging and Random
Forests
`0:21:45`

- [ ] Youtube: Deep Mind's AlphaGo Zero - EXPLAINED
`0:11:13`

- [ ] Youtube: Curiosity in AI
`0:06:16`

- [ ] Youtube: DropBlock - A BETTER DROPOUT for Neural
Networks
`0:07:45`

- [ ] Youtube: Neural Voice Cloning
`0:19:56`

- [ ] Youtube: Neural Networks from Scratch in Python
- [ ] Neural Networks from Scratch - P.1 Intro and Neuron
Code
`0:16:59`

- [ ] Neural Networks from Scratch - P.2 Coding a
Layer
`0:15:06`

- [ ] Neural Networks from Scratch - P.3 The Dot
Product
`0:25:17`

- [ ] Neural Networks from Scratch - P.4 Batches, Layers,
and Objects
`0:33:46`

- [ ] Neural Networks from Scratch - P.5 Hidden Layer
Activation Functions
`0:40:05`

- [ ] Neural Networks from Scratch - P.1 Intro and Neuron
Code
- [ ] Youtube: Visualizing Deep Learning
- [X] Youtube: Deep Double Descent

### Be able to implement models in scikit-learn

- [X] Datacamp: Supervised Learning with scikit-learn
- [X] Datacamp: Machine Learning with Tree-Based Models in Python
- [ ] Datacamp: Introduction to Linear Modeling in Python
- [X] Datacamp: Linear Classifiers in Python
- [ ] Datacamp: Generalized Linear Models in Python
- [X] Pluralsight: Building Machine Learning Models in Python with scikit-learn
- [ ] Youtube: Applied Machine Learning 2020
- [ ] Channel Intro - Applied Machine Learning
`0:01:28`

- [ ] Applied ML 2020 - 01 Introduction
`1:16:01`

- [ ] Applied ML 2020 - 02 Visualization and
matplotlib
`1:07:30`

- [ ] Applied ML 2020 - 03 Supervised learning and model
validation
`1:12:00`

- [ ] Applied ML 2020 - 04 - Preprocessing
`1:07:40`

- [ ] Applied ML 2020 - 05 - Linear Models for
Regression
`1:06:54`

- [ ] Applied ML 2020 - 06 - Linear Models for
Classification
`1:07:50`

- [ ] Applied ML 2020 - 07 - Decision Trees and Random
Forests
`1:07:58`

- [ ] Applied ML 2020 - 08 - Gradient Boosting
`1:02:12`

- [ ] Applied ML 2020 - 09 - Model Evaluation and
Metrics
`1:18:23`

- [ ] Applied ML 2020 - 10 - Calibration, Imbalanced
data
`1:16:14`

- [ ] Applied ML 2020 - 11 - Model Inspection and Feature
Selection
`1:15:15`

- [ ] Applied ML 2020 - 12 - AutoML (plus some feature
selection)
`1:25:38`

- [ ] Applied ML 2020 - 13 - Dimensionality reduction
`1:30:34`

- [ ] Applied ML 2020 - 14 - Clustering and Mixture
Models
`1:26:33`

- [ ] Applied ML 2020 - 15 - Working with Text Data
`1:27:08`

- [ ] Applied ML 2020 - 16 - Topic models for text
data
`1:18:34`

- [ ] Applied ML 2020 - 17 - Word vectors and document
embeddings
`1:03:04`

- [ ] Applied ML 2020 - 18 - Neural Networks
`1:19:36`

- [ ] Applied ML 2020 - 19 - Keras and Convolutional
neural nets
`1:16:01`

- [ ] Applied ML 2020 - 20 - Advanced neural networks
`1:36:28`

- [ ] Applied ML 2020 - 21 - Time Series and
Forecasting
`1:12:36`

- [ ] Channel Intro - Applied Machine Learning

### Be able to implement models in Tensorflow and Keras

- [X] Coursera: Introduction to Tensorflow
- [X] Coursera: Convolutional Neural Networks in TensorFlow
- [ ] Coursera: Getting Started With Tensorflow 2
- [ ] Coursera: Customising your models with TensorFlow 2
- [X] Deeplizard: Keras - Python Deep Learning Neural Network API
- [ ] Book: Deep Learning with Python (Page: 276)
- [X] Datacamp: Deep Learning in Python
- [X] Datacamp: Convolutional Neural Networks for Image Processing
- [X] Datacamp: Introduction to TensorFlow in Python
- [X] Datacamp: Introduction to Deep Learning with Keras
- [X] Datacamp: Advanced Deep Learning with Keras
- [ ] Google: Intro to Tensorflow
- [ ] Google: Machine Learning Crash Course
- [X] Pluralsight: Deep Learning with Keras
- [X] Udacity: Intro to TensorFlow for Deep Learning

### Be able to implement models in PyTorch

- [X] Article: An introduction to PyTorch Lightning with comparisons to PyTorch
- [X] Datacamp: Introduction to Deep Learning with PyTorch
- [X] Deeplizard: Neural Network Programming - Deep Learning with PyTorch
- [ ] Udacity: Intro to Deep Learning with PyTorch
- [ ] Youtube: PyTorch Lightning 101
- [X] Youtube: SimCLR with PyTorch Lightning
- [ ] Youtube: PyTorch Performance Tuning Guide
`26:41:00`

- [ ] Youtube: Skin Cancer Detection with PyTorch
- [ ] [PART 1] Skin Cancer Detection with PyTorch
`0:10:21`

- [ ] [PART 2] Skin Cancer Detection with PyTorch
`0:21:57`

- [ ] [PART 3] Skin Cancer Detection with PyTorch
`0:22:24`

- [ ] [PART 1] Skin Cancer Detection with PyTorch

### Be able to apply unsupervised learning algorithms

- [ ] Article: Grouping data points with k-means clustering
- [ ] Article: Soft clustering with Gaussian mixed models (EM)
- [ ] Article: Introduction to autoencoders
- [ ] Article: Variational autoencoders
- [ ] Article: Principal components analysis (PCA)
- [ ] Article: Deep Inside Autoencoders
- [ ] Article: Build a simple Image Retrieval System with an Autoencoder
- [ ] Article: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
- [ ] Article: A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
- [ ] Article: Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)
- [ ] Article: Affinity Propagation Algorithm Explained
- [ ] Article: Algorithm Breakdown: Affinity Propagation
- [ ] Article: From Autoencoder to Beta-VAE
- [ ] Article: Self-Supervised Representation Learning
- [ ] Article: GANs in computer vision - Introduction to generative learning
- [ ] Article: GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation
- [ ] Article: GANs in computer vision - semantic image synthesis and learning a generative model from a single image
- [ ] Article: GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes
- [ ] Article: GANs in computer vision - Conditional image synthesis and 3D object generation
- [ ] Article: Decrypt Generative Adversarial Networks (GAN)
- [ ] Article: How to Generate Images using Autoencoders
- [ ] Article: Deepfakes: Face synthesis with GANs and Autoencoders
- [ ] Berkeley: Deep Unsupervised Learning Spring
2020
- [X] L1 Introduction -- CS294-158-SP20 Deep Unsupervised
Learning -- UC Berkeley, Spring 2020
`1:10:02`

- [X] L2 Autoregressive Models -- CS294-158-SP20 Deep
Unsupervised Learning -- UC Berkeley, Spring 2020
`2:27:23`

- [ ] L3 Flow Models -- CS294-158-SP20 Deep Unsupervised
Learning -- UC Berkeley -- Spring 2020
`1:56:53`

- [ ] L4 Latent Variable Models (VAE) -- CS294-158-SP20
Deep Unsupervised Learning -- UC Berkeley
`2:19:33`

- [ ] Lecture 5 Implicit Models -- GANs Part I --- UC
Berkeley, Spring 2020
`2:32:32`

- [ ] Lecture 6 Implicit Models / GANs part II ---
CS294-158-SP20 Deep Unsupervised Learning -- Berkeley
`2:09:14`

- [X] Lecture 7 Self-Supervised Learning -- UC Berkeley
Spring 2020 - CS294-158 Deep Unsupervised Learning
`2:20:41`

- [ ] L8 Round-up of Strengths and Weaknesses of
Unsupervised Learning Methods -- UC Berkeley SP20
`0:41:51`

- [X] L9 Semi-Supervised Learning and Unsupervised
Distribution Alignment -- CS294-158-SP20 UC Berkeley
`2:16:00`

- [ ] L10 Compression -- UC Berkeley, Spring 2020,
CS294-158 Deep Unsupervised Learning
`3:09:49`

- [X] L11 Language Models -- guest instructor: Alec
Radford (OpenAI) --- Deep Unsupervised Learning SP20
`2:38:19`

- [ ] L12 Representation Learning for Reinforcement
Learning --- CS294-158 UC Berkeley Spring 2020
`2:01:56`

- [X] L1 Introduction -- CS294-158-SP20 Deep Unsupervised
Learning -- UC Berkeley, Spring 2020
- [ ] Datacamp: Customer Segmentation in Python
- [X] Datacamp: Unsupervised Learning in Python
- [ ] Google: Clustering
- [ ] Google: Recommendation Systems
- [X] Udacity: Segmentation and Clustering
- [X] Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)
- [X] Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)
- [X] Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)
- [X] Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning
- [X] Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81
- [X] Youtube: Contrastive Clustering with SwAV
- [ ] Youtube: Variational Autoencoders - EXPLAINED!
`0:17:36`

- [X] Youtube: OptaProAnalyticsForum– Learning to watch football: Self-supervised representations for tracking data
- [X] Youtube: Can a Neural Net tell if an image is mirrored? – Visual Chirality
- [X] Youtube: Deep InfoMax: Learning deep representations by mutual information estimation and maximization
- [ ] Deep Learning Lecture Summer 2020
- [ ] Deep Learning: Unsupervised Learning - Part 1
- [ ] Deep Learning: Unsupervised Learning - Part 2
- [ ] Deep Learning: Unsupervised Learning - Part 3
- [ ] Deep Learning: Unsupervised Learning - Part 4
- [ ] Deep Learning: Unsupervised Learning - Part 5
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 1
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 2
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 3
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 4

- [ ] ECCV 2020: New Frontiers for Learning with Limited
Labels or Data
- [X] Introduction to New Frontiers on Learning with Limited Labels or Data
- [X] Self-Supervised Part and Viewpoint Discovery from Image Collections
- [X] Learning Visual Correspondences across Instances and Video Frames
- [X] Limitless Labels in a Labelless World: Weak Supervision with Noisy Labels
- [ ] Inverting Neural Networks for Data-free Knowledge Transfer
- [ ] Learning Efficiently with Biologically Inspired Feedback

- [ ] Youtube: Self-Supervised Learning - What is Next? -
Workshop at ECCV 2020, August 28th
- [X] Next Challenges for Self-Supervised Learning -
Aäron van den Oord
`0:20:13`

- [X] Perspectives on Unsupervised Representation
Learning - Paolo Favaro
`0:42:41`

- [X] Learning and Transferring Visual Representations
with Few Labels - Carl Doersch
`0:32:53`

- [ ] Multi-view Invariance and Grouping for
Self-Supervised Learning - Ishan Misra
`0:36:31`

- [ ] Representation Learning beyond Instance
Discrimination and Semantic Categorization - Stella Yu
`0:43:09`

- [X] Self-Supervision as a Path to a Post-Dataset Era -
Alexei Alyosha Efros
`0:38:06`

- [ ] Self-Supervision & Modularity: Cornerstones for
Generalization in Embodied Agents - Deepak Pathak
`0:41:56`

- [X] Next Challenges for Self-Supervised Learning -
Aäron van den Oord

### Be able to implement computer vision models

- [ ] Article: What is Focal Loss and when should you use it?
- [ ] Article: Squeeze and Excitation Networks Explained with PyTorch Implementation
- [ ] Article: DenseNet Architecture Explained with PyTorch Implementation from TorchVision
- [ ] Article: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- [ ] Article: Group Normalization
- [ ] Article: A Short Introduction to Generative Adversarial Networks
- [ ] Article: Semi-supervised Learning with GANs
- [ ] Article: Densely Connected Convolutional Networks in Tensorflow
- [ ] Article: Convolutional neural networks
- [ ] Article: Common architectures in convolutional neural networks
- [ ] Article: An overview of semantic image segmentation
- [ ] Article: Evaluating image segmentation models
- [ ] Article: An overview of object detection: one-stage methods
- [ ] Article: A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
- [ ] Article: Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS
- [ ] Article: Object Detection for Dummies Part 2: CNN, DPM and Overfeat
- [ ] Article: Object Detection for Dummies Part 3: R-CNN Family
- [ ] Article: Understanding coordinate systems and DICOM for deep learning medical image analysis
- [ ] Article: Understanding the receptive field of deep convolutional networks
- [ ] Article: Deep learning in medical imaging - 3D medical image segmentation with PyTorch
- [ ] Article: Intuitive Explanation of Skip Connections in Deep Learning
- [ ] Article: Human Pose Estimation
- [ ] Article: YOLO - You only look once (Single shot detectors)
- [ ] Article: Localization and Object Detection with Deep Learning
- [ ] Article: Semantic Segmentation in the era of Neural Networks
- [ ] Article: ECCV 2020: Some Highlights
- [ ] Book: Deep Learning for Computer Vision with Python
- [ ] Book: Practical Python and OpenCV
- [ ] Coursera: Convolutional Neural Networks
- [ ] Datacamp: Biomedical Image Analysis in Python
- [ ] Datacamp: Image Processing in Python
- [X] Google: ML Practicum: Image Classification
- [ ] Stanford: CS231N Winter 2016
- [X] CS231n Winter 2016: Lecture 1: Introduction and
Historical Context
`1:19:08`

- [X] CS231n Winter 2016: Lecture 2: Data-driven
approach, kNN, Linear Classification 1
`0:57:28`

- [ ] CS231n Winter 2016: Lecture 3: Linear
Classification 2, Optimization
`1:11:23`

- [ ] CS231n Winter 2016: Lecture 4: Backpropagation,
Neural Networks 1
`1:19:38`

- [ ] CS231n Winter 2016: Lecture 5: Neural Networks Part
2
`1:18:37`

- [ ] CS231n Winter 2016: Lecture 6: Neural Networks Part
3 / Intro to ConvNets
`1:09:35`

- [ ] CS231n Winter 2016: Lecture 7: Convolutional Neural
Networks
`1:19:01`

- [ ] CS231n Winter 2016: Lecture 8: Localization and
Detection
`1:04:57`

- [ ] CS231n Winter 2016: Lecture 9: Visualization, Deep
Dream, Neural Style, Adversarial Examples
`1:18:20`

- [ ] CS231n Winter 2016: Lecture 10: Recurrent Neural
Networks, Image Captioning, LSTM
`1:09:54`

- [ ] CS231n Winter 2016: Lecture 11: ConvNets in
practice
`1:15:03`

- [ ] CS231n Winter 2016: Lecture 12: Deep Learning
libraries
`1:21:06`

- [ ] CS231n Winter 2016: Lecture 14: Videos and
Unsupervised Learning
`1:17:36`

- [ ] CS231n Winter 2016: Lecture 13: Segmentation, soft
attention, spatial transformers
`1:10:59`

- [ ] CS231n Winter 2016: Lecture 15: Invited Talk by
Jeff Dean
`1:14:49`

- [X] CS231n Winter 2016: Lecture 1: Introduction and
Historical Context
- [ ] Udacity: Introduction to Computer Vision
- [X] Youtube: Deep Residual Learning for Image Recognition (Paper Explained)
- [X] Youtube: Implementing ResNet from scratch
- [ ] Youtube: ConvNets Scaled Efficiently
`0:13:19`

- [ ] Youtube: Building an Image Captioner with Neural
Networks
`0:12:54`

- [ ] Youtube: Evolution of Face Generation | Evolution
of GANs
`0:12:23`

- [ ] Youtube: Autoencoders - EXPLAINED
`0:10:53`

- [ ] Youtube: Unpaired Image-Image Translation using
CycleGANs
`0:16:22`

- [ ] Youtube: AI creates Image Classifiers…by
DRAWING?
`0:09:04`

- [ ] Youtube: The Evolution of Convolution Neural
Networks
`0:24:02`

- [ ] Youtube: Depthwise Separable Convolution - A FASTER
CONVOLUTION!
`0:12:43`

- [ ] Youtube: Mask Region based Convolution Neural
Networks - EXPLAINED!
`0:09:34`

- [ ] Youtube: Sound play with Convolution Neural
Networks
`0:11:57`

- [ ] Youtube: Convolution Neural Networks -
EXPLAINED
`0:19:20`

- [ ] Youtube: Generative Adversarial Networks -
FUTURISTIC & FUN AI !
`0:14:20`

### Be able to implement NLP models

- [ ] Article: The Annotated GPT-2
- [ ] Article: Introduction to recurrent neural networks
- [ ] Article: Aspect-Based Opinion Mining (NLP with Python)
- [X] Article: The Transformer Explained
- [X] Article: Controlling Text Generation with Plug and Play Language Models
- [X] Article: What makes a good conversation?
- [X] Article: NLP for Supervised Learning - A Brief Survey
- [ ] Article: Generating Questions Using Transformers
- [ ] Article: Neural Language Models as Domain-Specific Knowledge Bases
- [ ] Article: Understanding BERT’s Semantic Interpretations
- [ ] Article: Using NLP (BERT) to improve OCR accuracy
- [ ] Article: Hyperparameter Optimization for ?Transformers: A guide
- [ ] Article: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime
- [ ] Article: Learning Word Embedding
- [ ] Article: The Transformer Family
- [ ] Article: Generalized Language Models
- [ ] Article: Document clustering
- [ ] Article: The Unreasonable Effectiveness of Recurrent Neural Networks
- [ ] Article: LSTM Primer With Real Life Application( DeepMind Kidney Injury Prediction )*
- [ ] Article: Making sense of LSTMs by example
- [ ] Article: 3 subword algorithms help to improve your NLP model performance
- [ ] Article: Exploring LSTMs
- [ ] Article: Understanding LSTM Networks
- [ ] Article: 74 Summaries of Machine Learning and NLP Research
- [X] A friendly introduction to Recurrent Neural Networks
- [ ] Coursera: Sequence Models
- [ ] Coursera: Natural Language Processing in TensorFlow
- [ ] CMU: Low-resource NLP Bootcamp 2020
- [ ] CMU Low resource NLP Bootcamp 2020 (1): NLP
Tasks
`1:46:06`

- [ ] CMU Low resource NLP Bootcamp 2020 (2): Linguistics
- Phonology and Morphology
`1:24:08`

- [ ] CMU Low resource NLP Bootcamp 2020 (3): Machine
Translation
`1:55:59`

- [ ] CMU Low resource NLP Bootcamp 2020 (4): Linguistics
- Syntax and Morphosyntax
`2:00:21`

- [ ] CMU Low resource NLP Bootcamp 2020 (5): Neural
Representation Learning
`1:19:57`

- [ ] CMU Low resource NLP Bootcamp 2020 (6):
Multilingual NLP
`2:04:34`

- [ ] CMU Low resource NLP Bootcamp 2020 (7): Speech
Synthesis
`2:22:14`

- [ ] CMU Low resource NLP Bootcamp 2020 (8): Speech
Recognition
`2:16:18`

- [ ] CMU Low resource NLP Bootcamp 2020 (1): NLP
Tasks
- [ ] CMU: Neural Nets for NLP 2020
- [X] CMU Neural Nets for NLP 2020 (1): Introduction
`1:11:38`

- [ ] CMU Neural Nets for NLP 2020 (2): Language
Modeling, Efficiency/Training Tricks
`1:18:31`

- [ ] CMU Neural Nets for NLP 2020 (3): Convolutional
Neural Networks for Text
`0:54:45`

- [ ] CMU Neural Nets for NLP 2020 (4): Recurrent Neural
Networks
`1:11:28`

- [ ] CMU Neural Nets for NLP 2020 (5): Efficiency Tricks
for Neural Nets
`1:05:37`

- [ ] CMU Neural Nets for NLP 2020 (7): Attention
`1:05:26`

- [ ] CMU Neural Nets for NLP 2020 (8): Distributional
Semantics and Word Vectors
`1:10:45`

- [ ] CMU Neural Nets for NLP 2020 (9): Sentence and
Contextual Word Representations
`1:16:19`

- [ ] CMU Neural Nets for NLP 2020 (10): Debugging Neural
Nets (for NLP)
`1:15:26`

- [ ] CMU Neural Nets for NLP 2020 (11): Structured
Prediction with Local Independence Assumptions
`1:08:38`

- [ ] CMU Neural Nets for NLP 2020 (12): Generating Trees
Incrementally
`1:14:13`

- [ ] CMU Neural Nets for NLP 2020 (13): Generating Trees
Incrementally
`0:51:58`

- [ ] CMU Neural Nets for NLP 2020 (14): Search-based
Structured Prediction
`1:06:19`

- [ ] CMU Neural Nets for NLP 2020 (15): Minimum Risk
Training and Reinforcement Learning
`1:09:16`

- [ ] CMU Neural Nets for NLP 2020 (16): Advanced Search
Algorithms
`1:03:02`

- [ ] CMU Neural Nets for NLP 2020 (17): Adversarial
Methods
`1:14:55`

- [ ] CMU Neural Nets for NLP 2020 (18): Models w/ Latent
Random Variables
`1:13:16`

- [ ] CMU Neural Nets for NLP 2020 (19): Unsupervised and
Semi-supervised Learning of Structure
`1:12:47`

- [ ] CMU Neural Nets for NLP 2020 (20): Multitask and
Multilingual Learning
`1:02:46`

- [ ] CMU Neural Nets for NLP 2020 (21): Document Level
Models
`0:52:04`

- [ ] CMU Neural Nets for NLP 2020 (22): Neural Nets +
Knowledge Bases
`1:18:39`

- [ ] CMU Neural Nets for NLP 2020 (23): Machine Reading
w/ Neural Nets
`1:06:11`

- [ ] CMU Neural Nets for NLP 2020 (24): Natural Language
Generation
`1:21:48`

- [ ] CMU Neural Nets for NLP 2020 (25): Model
Interpretation
`1:04:11`

- [X] CMU Neural Nets for NLP 2020 (1): Introduction
- [ ] CMU Multilingual NLP 2020
- [ ] CMU Multilingual NLP (1): Introduction
`1:17:28`

- [ ] CMU Multilingual NLP (2): Typology - The Space of
Language
`0:37:12`

- [ ] CMU Multilingual NLP (1): Introduction
- [X] Datacamp: Advanced NLP with spaCy
- [X] Datacamp: Building Chatbots in Python
- [X] Datacamp: Clustering Methods with SciPy
- [X] Datacamp: Feature Engineering for NLP in Python
- [X] Datacamp: Machine Translation in Python
- [X] Datacamp: Natural Language Processing Fundamentals in Python
- [X] Datacamp: Natural Language Generation in Python
- [X] Datacamp: RNN for Language Modeling
- [X] Datacamp: Regular Expressions in Python
- [X] Datacamp: Sentiment Analysis in Python
- [ ] Datacamp: Spoken Language Processing in Python
- [ ] RNN and LSTM
- [X] Spacy Tutorial
- [ ] Stanford CS224U: Natural Language Understanding |
Spring 2019
- [X] Lecture 1 – Course Overview | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:12:59`

- [ ] Lecture 2 – Word Vectors 1 | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:17:10`

- [ ] Lecture 3 – Word Vectors 2 | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:16:52`

- [ ] Lecture 4 – Word Vectors 3 | Stanford CS224U:
Natural Language Understanding | Spring 2019
`0:38:20`

- [ ] Lecture 5 – Sentiment Analysis 1 | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:10:44`

- [ ] Lecture 6 – Sentiment Analysis 2 | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:03:23`

- [ ] Lecture 7 – Relation Extraction | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:19:04`

- [ ] Lecture 8 – NLI 1 | Stanford CS224U: Natural
Language Understanding | Spring 2019
`1:15:02`

- [ ] Lecture 9 – NLI 2 | Stanford CS224U: Natural
Language Understanding | Spring 2019
`1:15:35`

- [ ] Lecture 10 – Grounding | Stanford CS224U: Natural
Language Understanding | Spring 2019
`1:23:15`

- [ ] Lecture 11 – Semantic Parsing | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:07:05`

- [ ] Lecture 12 – Evaluation Methods | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:18:32`

- [ ] Lecture 13 – Evaluation Metrics | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:11:32`

- [ ] Lecture 14 – Contextual Vectors | Stanford CS224U:
Natural Language Understanding | Spring 2019
`1:14:33`

- [ ] Lecture 15 – Presenting Your Work | Stanford
CS224U: Natural Language Understanding | Spring 2019
`1:15:11`

- [X] Lecture 1 – Course Overview | Stanford CS224U:
Natural Language Understanding | Spring 2019
- [ ] Stanford CS224N: Stanford CS224N: NLP with Deep
Learning | Winter 2019
- [X] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 1 – Introduction and Word Vectors
`1:21:52`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 2 – Word Vectors and Word Senses
`1:20:43`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 3 – Neural Networks
`1:18:50`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 4 – Backpropagation
`1:22:15`

- [X] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 5 – Dependency Parsing
`1:20:22`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 6 – Language Models and RNNs
`1:08:25`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs
`1:13:23`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 8 – Translation, Seq2Seq, Attention
`1:16:56`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 9 – Practical Tips for Projects
`1:22:39`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 10 – Question Answering
`1:21:01`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 11 – Convolutional Networks for NLP
`1:20:18`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 12 – Subword Models
`1:15:30`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 13 – Contextual Word Embeddings
`1:20:18`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 14 – Transformers and Self-Attention
`0:53:48`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 15 – Natural Language Generation
`1:19:37`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 16 – Coreference Resolution
`1:19:20`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 17 – Multitask Learning
`1:11:54`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 18 – Constituency Parsing, TreeRNNs
`1:20:37`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 19 – Bias in AI
`0:56:03`

- [ ] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 20 – Future of NLP + Deep Learning
`1:19:15`

- [X] Stanford CS224N: NLP with Deep Learning | Winter
2019 | Lecture 1 – Introduction and Word Vectors
- [ ] TextBlob Tutorial Series
- [ ] Natural Language Processing Tutorial With TextBlob
-Tokens,Translation and Ngrams
`0:11:01`

- [ ] NLP Tutorial With TextBlob and Python - Parts of
Speech Tagging
`0:05:59`

- [ ] NLP Tutorial With TextBlob & Python -
Lemmatizating
`0:06:32`

- [ ] NLP Tutorial with TextBlob & Python - Sentiment
Analysis(Polarity,Subjectivity)
`0:06:31`

- [ ] Building a NLP-based Flask App with TextBlob
`0:37:30`

- [ ] Natural Language Processing with Polyglot -
Installation & Intro
`0:12:49`

- [ ] Natural Language Processing Tutorial With TextBlob
-Tokens,Translation and Ngrams
- [ ] Treehouse: Regular expression
- [ ] Youtube: fast.ai Code-First Intro to Natural
Language Processing
- [X] What is NLP? (NLP video 1)
`0:22:42`

- [X] Topic Modeling with SVD & NMF (NLP video 2)
`1:06:39`

- [X] Topic Modeling & SVD revisited (NLP video
3)
`0:33:05`

- [X] Sentiment Classification with Naive Bayes (NLP
video 4)
`0:58:20`

- [ ] Sentiment Classification with Naive Bayes &
Logistic Regression, contd. (NLP video 5)
`0:51:29`

- [ ] Derivation of Naive Bayes & Numerical Stability
(NLP video 6)
`0:23:56`

- [ ] Revisiting Naive Bayes, and Regex (NLP video 7)
`0:37:33`

- [ ] Intro to Language Modeling (NLP video 8)
`0:40:58`

- [ ] Transfer learning (NLP video 9)
`1:35:16`

- [ ] ULMFit for non-English Languages (NLP Video 10)
`1:49:22`

- [ ] Understanding RNNs (NLP video 11)
`0:33:16`

- [ ] Seq2Seq Translation (NLP video 12)
`0:59:42`

- [ ] Word embeddings quantify 100 years of gender &
ethnic stereotypes-- Nikhil Garg (NLP video 13)
`0:47:17`

- [ ] Text generation algorithms (NLP video 14)
`0:25:39`

- [ ] Implementing a GRU (NLP video 15)
`0:23:13`

- [ ] Algorithmic Bias (NLP video 16)
`1:26:17`

- [ ] Introduction to the Transformer (NLP video 17)
`0:22:54`

- [ ] The Transformer for language translation (NLP video
18)
`0:55:17`

- [ ] What you need to know about Disinformation (NLP
video 19)
`0:51:21`

- [ ] Article: Zero to Hero with fastai - Beginner
- [ ] Article: Zero to Hero with fastai - Intermediate

- [X] What is NLP? (NLP video 1)
- [ ] NLP Course | For You
- [ ] Word Embeddings
- [ ] Text Classification
- [ ] Language Modeling
- [ ] Seq2seq and Attention

- [X] Youtube: BERT Research Series
- [X] YouTube: Intro to NLP with Spacy
- [X] Talk: Practical NLP for the Real World
- [X] YouTube: Level 3 AI Assistant Conference 2020
- [X] Youtube: Conversation Analysis Theory in Chatbots | Michael Szul
- [X] Youtube: Designing Practical NLP Solutions | Ines Montani
- [X] Youtube: Effective Copywriting for Chatbots | Hans Van Dam
- [X] Youtube: Distilling BERT | Sam Sucik
- [X] Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam
- [X] Youtube: From Research to Production – Our Process at Rasa | Tanja Bunk
- [X] Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol

- [ ] Youtube: RASA Algorithm Whiteboard
- [ ] Introducing The Algorithm Whiteboard
`0:01:16`

- [ ] Rasa Algorithm Whiteboard - Diet Architecture 1:
How it Works
`0:23:27`

- [ ] Rasa Algorithm Whiteboard - Diet Architecture 2:
Design Decisions
`0:15:06`

- [ ] Rasa Algorithm Whiteboard - Diet Architecture 3:
Benchmarking
`0:22:34`

- [ ] Rasa Algorithm Whiteboard - Embeddings 1: Just
Letters
`0:13:48`

- [ ] Rasa Algorithm Whiteboard - Embeddings 2: CBOW and
Skip Gram
`0:19:24`

- [ ] Rasa Algorithm Whiteboard - Embeddings 3: GloVe
`0:19:12`

- [ ] Rasa Algorithm Whiteboard - Embeddings 4:
Whatlies
`0:14:03`

- [ ] Rasa Algorithm Whiteboard - Attention 1: Self
Attention
`0:14:32`

- [ ] Rasa Algorithm Whiteboard - Attention 2: Keys,
Values, Queries
`0:12:26`

- [ ] Rasa Algorithm Whiteboard - Attention 3: Multi Head
Attention
`0:10:55`

- [ ] Rasa Algorithm Whiteboard: Attention 4 -
Transformers
`0:14:34`

- [ ] Rasa Algorithm Whiteboard - StarSpace
`0:11:46`

- [ ] Rasa Algorithm Whiteboard - TED Policy
`0:16:10`

- [ ] Rasa Algorithm Whiteboard - TED in Practice
`0:14:54`

- [ ] Rasa Algorithm Whiteboard - Response Selection
`0:12:07`

- [ ] Rasa Algorithm Whiteboard - Response Selection:
Implementation
`0:09:25`

- [ ] Rasa Algorithm Whiteboard - Countvectors
`0:13:32`

- [ ] Rasa Algorithm Whiteboard - Subword Embeddings
`0:11:58`

- [ ] Rasa Algorithm Whiteboard - Implementation of
Subword Embeddings
`0:10:01`

- [ ] Rasa Algorithm Whiteboard - BytePair Embeddings
`0:12:44`

- [ ] Introducing The Algorithm Whiteboard
- [X] Youtube: A brief history of the Transformer architecture in NLP
- [X] Youtube: The Transformer neural network architecture explained. “Attention is all you need” (NLP)
- [X] Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision
- [X] Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)
- [X] Youtube: Ilya Sutskever - GPT-2
- [X] Youtube: NLP Masterclass | Modeling Fallacies in NLP
- [X] Youtube: What is GPT-3? Showcase, possibilities, and implications
- [X] Youtube: TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP
- [X] Youtube: Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg
- [X] Youtube: Learning "Learning to Rank"
- [X] Youtube: Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]
- [X] Article: How the Embedding Layers in BERT Were Implemented
- [X] Youtube: Easy Data Augmentation for Text Classification
- [X] Youtube: Webinar: Special NLP Session with Hugging Face
- [ ] Youtube: BERT Neural Network - EXPLAINED!
`0:11:36`

- [ ] Youtube: NLP with Neural Networks &
Transformers
`0:10:45`

- [ ] Youtube: Transformer Neural Networks - EXPLAINED!
(Attention is all you need)
`0:13:05`

- [ ] Youtube: LSTM Networks - EXPLAINED!
`0:16:12`

- [ ] Youtube: Recurrent Neural Networks - EXPLAINED!
`0:17:05`

- [ ] Youtube: Attention in Neural Networks
`0:11:18`

- [X] Youtube: Spacy IRL 2019
- [X] Sebastian Ruder: Transfer Learning in Open-Source
Natural Language Processing (spaCy IRL 2019)
`0:31:24`

- [X] Giannis Daras: Improving sparse transformer models
for efficient self-attention (spaCy IRL 2019)
`0:20:13`

- [X] Peter Baumgartner: Applied NLP: Lessons from the
Field (spaCy IRL 2019)
`0:18:44`

- [X] Justina Petraitytė: Lessons learned in helping ship
conversational AI assistants (spaCy IRL 2019)
`0:23:48`

- [X] Yoav Goldberg: The missing elements in NLP (spaCy
IRL 2019)
`0:30:27`

- [X] Sofie Van Landeghem: Entity linking functionality
in spaCy (spaCy IRL 2019)
`0:20:08`

- [X] Guadalupe Romero: Rethinking rule-based
lemmatization (spaCy IRL 2019)
`0:14:49`

- [X] Mark Neumann: ScispaCy: A spaCy pipeline &
models for scientific & biomedical text (spaCy IRL 2019)
`0:18:59`

- [X] Patrick Harrison: Financial NLP at S&P Global
(spaCy IRL 2019)
`0:21:42`

- [X] McKenzie Marshall: NLP in Asset Management (spaCy
IRL 2019)
`0:20:32`

- [X] David Dodson: spaCy in the News: Quartz's NLP
pipeline (spaCy IRL 2019)
`0:20:56`

- [X] Matthew Honnibal & Ines Montani: spaCy and
Explosion: past, present & future (spaCy IRL 2019)
`0:54:13`

- [X] Sebastian Ruder: Transfer Learning in Open-Source
Natural Language Processing (spaCy IRL 2019)
- [ ] Youtube: The Future of Natural Language Processing
- [ ] Youtube: Sentiment Analysis: Key Milestones, Challenges and New Directions
- [ ] Youtube: Simple and Efficient Deep Learning for Natural Language Processing, with Moshe Wasserblat, Intel AI
- [X] Youtube: Why not solve biological problems with a Transformer? BERTology meets Biology
- [ ] Youtube: Introduction to NLP
- [ ] Introduction to NLP | Bag of Words Model
`0:22:23`

- [ ] Introduction to NLP | TF-IDF
`0:10:55`

- [ ] Introduction to NLP | Text Cleaning and
Preprocessing
`0:14:02`

- [ ] Introduction to NLP | Word Embeddings &
Word2Vec Model
`0:23:09`

- [ ] Introduction to NLP | GloVe Model Explained
`0:23:15`

- [ ] Introduction to NLP | GloVe & Word2Vec Transfer
Learning
`0:21:12`

- [ ] Introduction to NLP | How to Train Custom Word
Vectors
`0:13:48`

- [ ] Sarcasm is Very Easy to Detect! GloVe + LSTM
`0:17:07`

- [ ] Text Summarization & Keyword Extraction |
Introduction to NLP
`0:14:59`

- [ ] Introduction to NLP | Bag of Words Model
- [ ] Youtube: Self-attention step-by-step | How to get meaning from text
- [ ] Youtube: Chat Bot with PyTorch
- [ ] Youtube: NLP with Friends Talks
- [ ] NLP with Friends, Featured Friend: Tom McCoy
`0:36:48`

- [ ] NLP with Friends, Featured Friend: Maarten Sap
`0:36:11`

- [ ] NLP with Friends, featured friend: Nitika
Mathur
`1:01:42`

- [ ] NLP with Friends, Featured Friend: Sabrina J
Mielke
`1:01:28`

- [ ] NLP with Friends, Featured Friend: Tom McCoy
- [X] Youtube: Insincere Question Classification with PyTorch

### Be able to model graphs and network data

### Be able to implement models for timeseries and forecasting

- [ ] Datacamp: Machine Learning for Finance in Python
- [X] Datacamp: Introduction to Time Series Analysis in Python
- [ ] Datacamp: Machine Learning for Time Series Data in Python
- [ ] Datacamp: Intro to Portfolio Risk Management in Python
- [ ] Datacamp: Financial Forecasting in Python
- [X] Datacamp: Predicting CTR with Machine Learning in Python
- [X] Datacamp: Intro to Financial Concepts using Python
- [X] Datacamp: Fraud Detection in Python
- [ ] Datacamp: Forecasting Using ARIMA Models in Python
- [ ] Datacamp: Introduction to Portfolio Analysis in Python
- [ ] Datacamp: Credit Risk Modeling in Python
- [ ] Datacamp: Machine Learning for Marketing in Python
- [ ] Udacity: Machine Learning for Trading
- [ ] Udacity: Time Series Forecasting

### Be familiar with Reinforcement Learning

- [X] DeepLizard: Reinforcement Learning - Goal Oriented
Intelligence
- [X] Reinforcement Learning Series Intro - Syllabus
Overview
`0:05:51`

- [X] Markov Decision Processes (MDPs) - Structuring a
Reinforcement Learning Problem
`0:06:34`

- [X] Expected Return - What Drives a Reinforcement
Learning Agent in an MDP
`0:06:47`

- [X] Policies and Value Functions - Good Actions for a
Reinforcement Learning Agent
`0:06:52`

- [X] What do Reinforcement Learning Algorithms Learn -
Optimal Policies
`0:06:21`

- [X] Q-Learning Explained - A Reinforcement Learning
Technique
`0:08:37`

- [X] Exploration vs. Exploitation - Learning the Optimal
Reinforcement Learning Policy
`0:10:06`

- [X] OpenAI Gym and Python for Q-learning -
Reinforcement Learning Code Project
`0:07:52`

- [X] Train Q-learning Agent with Python - Reinforcement
Learning Code Project
`0:08:59`

- [X] Watch Q-learning Agent Play Game with Python -
Reinforcement Learning Code Project
`0:07:22`

- [X] Deep Q-Learning - Combining Neural Networks and
Reinforcement Learning
`0:10:50`

- [X] Replay Memory Explained - Experience for Deep
Q-Network Training
`0:06:21`

- [X] Training a Deep Q-Network - Reinforcement
Learning
`0:09:07`

- [X] Training a Deep Q-Network with Fixed Q-targets -
Reinforcement Learning
`0:07:35`

- [X] Deep Q-Network Code Project Intro - Reinforcement
Learning
`0:06:26`

- [X] Build Deep Q-Network - Reinforcement Learning Code
Project
`0:16:51`

- [X] Deep Q-Network Image Processing and Environment
Management - Reinforcement Learning Code Project
`0:21:53`

- [X] Deep Q-Network Training Code - Reinforcement
Learning Code Project
`0:19:46`

- [X] Reinforcement Learning Series Intro - Syllabus
Overview

### Be able to use managed ML services on the cloud

- [X] AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
- [X] AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker
- [X] AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications
- [X] AWS: Hands-on Rekognition: Automated Video Editing
- [X] AWS: Introduction to Amazon Comprehend
- [X] AWS: Introduction to Amazon Comprehend Medical
- [X] AWS: Introduction to Amazon Elastic Inference
- [X] AWS: Introduction to Amazon Forecast
- [X] AWS: Introduction to Amazon Lex
- [X] AWS: Introduction to Amazon Personalize
- [X] AWS: Introduction to Amazon Polly
- [X] AWS: Introduction to Amazon SageMaker Ground Truth
- [X] AWS: Introduction to Amazon SageMaker Neo
- [X] AWS: Introduction to Amazon Transcribe
- [X] AWS: Introduction to Amazon Translate
- [X] AWS: Introduction to AWS Marketplace - Machine Learning Category
- [X] AWS: Machine Learning Exam Basics
- [X] AWS: Neural Machine Translation with Sockeye
- [X] AWS: Process Model: CRISP-DM on the AWS Stack
- [X] AWS: Satellite Image Classification in SageMaker
- [X] edX: Amazon SageMaker: Simplifying Machine Learning Application Development

### Be able to optimize performance metric

- [ ] Article: Evaluating a machine learning model
- [ ] Article: Hyperparameter tuning for machine learning models
- [ ] Article: Hacker's Guide to Hyperparameter Tuning
- [ ] Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- [X] Datacamp: Model Validation in Python
- [X] Datacamp: Hyperparameter Tuning in Python
- [ ] Google: Testing and Debugging
- [ ] Troubleshooting Deep Neural Networks
- [ ] Youtube: How do GPUs speed up Neural Network
training?
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- [ ] Youtube: Why use GPU with Neural Networks?
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### Be able to optimize models for production

- [ ] Article: Neural Network Pruning
- [ ] Article: FasterAI
- [X] Article: Is the future of Neural Networks Sparse? An Introduction (1/N)
- [X] Article: Sparse Neural Networks (2/N): Understanding GPU Performance.
- [X] Article: Block Sparse Matrices for Smaller and Faster Language Models

### Be able to deploy model to production

- [X] Acloudguru: AWS Certified Machine Learning - Specialty
- [X] Acloudguru: AWS Certified Developer - Associate
- [X] Acloudguru: AWS Certification Preparation Guide
- [X] AWS: Exam Readiness: AWS Certified Developer – Associate
- [X] AWS: Thirty Serverless Architectures in 30 Minutes
- [ ] Article: Deploy a Keras Deep Learning Project to Production with Flask
- [ ] Article: Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application
- [ ] Article: How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
- [ ] Article: Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
- [ ] Article: Deep Learning in Production: Laptop set up and system design
- [ ] Article: Enough Docker to be Dangerous
- [ ] Article: How to properly ship and deploy your machine learning model
- [ ] Luigi
Patruno: ML in Production
- [ ] Video: You trained a machine learning model. Now what?
- [ ] Article: Docker for Machine Learning – Part I
- [ ] Article: Docker for Machine Learning – Part II
- [ ] Article: Docker for Machine Learning – Part III
- [ ] Article: Using Docker to Generate Machine Learning Predictions in Real Time
- [ ] Article: Batch Inference vs Online Inference
- [ ] Article: Storing Metadata from Machine Learning Experiments
- [ ] Article: How Data Leakage Impacts Machine Learning Models
- [ ] Article: An Introduction to Kubernetes for Data Scientists
- [ ] Article: How to Use Kubernetes Pods for Machine Learning
- [ ] Article: Kubernetes Jobs for Machine Learning
- [ ] Article: Kubernetes CronJobs for Machine Learning
- [ ] Article: Kubernetes Deployments for Machine Learning
- [ ] Article: Kubernetes Services for Machine Learning
- [ ] Article: The Ultimate Guide to Model Retraining
- [ ] Article: Top ML Resources: Interview with Eric Colson
- [ ] Article: Top ML Resources: Interview with Veronika Megler, PhD
- [ ] Article: Top ML Resources: Interview with Erik Bernhardsson
- [ ] Article: Top ML Resources: Interview with Rui Carmo
- [ ] Article: Top ML Resources: Interview with Jeremy Jordan
- [ ] Article: 5 Challenges to Running Machine Learning Systems in Production
- [ ] Article: Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learning
- [ ] Article: What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)
- [ ] Article: Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)
- [ ] Article: Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)
- [ ] Article: The Challenges of Online Inference (Deployment Series: Guide 04)
- [ ] Article: Online Inference for ML Deployment (Deployment Series: Guide 05)
- [ ] Article: Model Registries for ML Deployment (Deployment Series: Guide 06)
- [ ] Article: Test-Driven Machine Learning Development (Deployment Series: Guide 07)
- [ ] Article: A/B Testing Machine Learning Models (Deployment Series: Guide 08)
- [ ] Article: Lessons Learned from 15 Years of Monitoring Machine Learning in Production
- [ ] Article: Why is it Important to Monitor Machine Learning Models?
- [ ] Article: Maximizing Business Impact with Machine Learning

- [ ] Codecademy: Deploy a Website
- [ ] Datacamp: Parallel Computing with Dask
- [X] Datacamp: Cloud Computing for Everyone
- [X] Django Best Practices
- [X] Pluralsight: Docker and Containers: The Big Picture
- [X] Pluralsight: Docker and Kubernetes: The Big Picture
- [X] Pluralsight: AWS Developer: The Big Picture
- [X] Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)
- [X] Pluralsight: AWS VPC Operations
- [X] Pluralsight: Building Applications Using Elastic Beanstalk
- [ ] Servers for Hackers Series
- [ ] The Hacker's Guide to Scaling Python
- [ ] Udacity: HTTP & Web Servers
- [ ] Udacity: Intro to DevOps
- [ ] Udacity: Developing Scalable Apps in Python
- [ ] Udacity: Configuring Linux Web Servers
- [ ] Udacity: Scalable Microservices with Kubernetes
- [X] Udemy: AWS Concepts
- [X] Udemy: Serverless Concepts
- [X] Udemy: AWS Certified Developer - Associate 2018
- [ ] Udacity: Authentication & Authorization: OAuth
- [ ] Udacity: Designing RESTful APIs
- [ ] Udacity: Client-Server Communication
- [X] Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPI
- [X] Youtube: FastAPI from the ground up

### Be able to perform A/B testing

- [ ] Datacamp: Customer Analytics & A/B Testing in Python
- [ ] Udacity: A/B Testing
- [ ] Udacity: A/B Testing for Business Analysts
- [ ] Youtube: A/B Testing - Simply Explained
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- [ ] Youtube: Hypothesis testing with Applications in
Data Science
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### Be able to write unit tests

- [X] Article: Effective testing for machine learning systems
- [X] Datacamp: Unit Testing for Data Science in Python
- [X] Pluralsight: Test-driven Development: The Big Picture
- [ ] Test Driven Development with Python
- [ ] Thoughtbot: Fundamentals of TDD
- [ ] Treehouse: Python Testing
- [ ] Udacity: Software Analysis & Testing
- [ ] Udacity: Software Testing
- [ ] Udacity: Software Debugging

### Be proficient in Python

- [ ] Article: No Really, Python's Pathlib is Great
- [X] Book: A Byte of Python
- [X] Book: Learn Python The Hard way
- [ ] Book: Python 201
- [ ] Book: Python Anti-Patterns
- [ ] Book: Real Python
- [ ] Book: The Python 3 Standard Library By Example
- [ ] Book: Writing Idiomatic Python 3
- [X] Codecademy: Learn Python
- [X] Cognitiveclass.ai: Python for Data Science
- [X] Datacamp: Python for R Users
- [X] Datacamp: Python for Spreadsheet Users
- [ ] Datacamp: Python for MATLAB Users
- [X] Datacamp: Importing Data in Python (Part 1)
- [X] Datacamp: Intermediate Python for Data Science
- [X] Datacamp: Python Data Science Toolbox (Part 1)
- [X] Datacamp: Python Data Science Toolbox (Part 2)
- [X] Datacamp: Intro to Python for Finance
- [X] Datacamp: Writing Efficient Python Code
- [X] Datacamp: Writing Functions in Python
- [ ] Datacamp: Working with Dates and Times in Python
- [X] Datacamp: Object-Oriented Programming in Python
- [X] edX: Introduction to Python for Data Science
- [X] edX: Programming with Python for Data Science
- [X] Google's Python Class
- [X] Treehouse: Python Basics
- [ ] Treehouse: Python collections
- [ ] Treehouse: Date and Time
- [ ] Treehouse: CSV And JSON
- [ ] Treehouse: Functional Programming with Python
- [ ] Treehouse: Python Decorators
- [ ] Treehouse: Write Better Python
- [ ] Thoughtbot: Regular Expressions
- [X] TheNewBoston: Python Programming Tutorials
- [ ] Udacity: Introduction to Python Programming
- [ ] Udacity: Programming Foundations with Python
- [ ] Udacity: What is Programming?

### Be familiar with compiled languages

### Have a general understanding of other parts of the stack

- [ ] Book: Refactoring UI
- [X] Codecademy: Learn HTML
- [ ] Codecademy: Learn Color Design
- [X] Codecademy: Learn SASS
- [X] Codecademy: Make a website
- [X] Codecademy: Learn ReactJS: Part I
- [X] Codecademy: Learn ReactJS: Part II
- [X] Codecademy: Learn JavaScript
- [X] Codecademy: Jquery Track
- [X] Codecademy: Learn Ruby
- [X] Code School: Fundamentals of Design
- [X] Code School: Blasting Off with Bootstrap
- [X] (ES6) - Beau teaches JavaScript
- [X] Pluralsight: UX Fundamentals
- [X] Pluralsight: HTML, CSS, and JavaScript: The Big Picture
- [X] Pluralsight: CSS Positioning
- [X] Pluralsight: Introduction to CSS
- [X] Pluralsight: CSS: Specificity, the Box Model, and Best Practices
- [X] Pluralsight: CSS: Using Flexbox for Layout
- [X] Pluralsight: Using The Chrome Developer Tools
- [ ] Thoughtbot: Design for Developers
- [X] Treehouse: HTML
- [X] Treehouse: Javascript Booleans
- [X] Udacity: ES6 - JavaScript Improved
- [X] Udacity: Intro to Javascript
- [X] Udacity: Object Oriented JS 1
- [X] Udacity: Object Oriented JS 2
- [X] Udemy: Understanding Typescript

### Be familiar with fundamental Computer Science concepts

- [X] Codecademy: Big O
- [ ] Crashcourse: Computer Science
- [X] Grokking Algorithms
- [ ] Khan Academy: Data Structures
- [ ] Udacity: Intro to Algorithms
- [ ] Udacity: Intro to Computer Science
- [ ] Udacity: Intro to Theoretical Computer Science
- [ ] Udacity: Programming Languages
- [ ] Udacity: Networking for Web Developers

### Be able to apply proper software engineering process

- [ ] Launch School: Agile Planning
- [X] Pluralsight: Product Owner Fundamentals
- [X] Pluralsight: Scrum Master Fundamentals - Foundations
- [X] Pluralsight: Security Awareness: Basic Concepts and Terminology
- [X] Pluralsight: Secure Software Development
- [X] Pluralsight: Clean Architecture: Patterns, Practices, and Principles
- [ ] Thoughtbot: Software Development Process
- [ ] Thoughtbot: Refactoring
- [ ] Udacity: Design of Computer Programs
- [ ] Udacity: Product Design
- [ ] Udacity: Rapid Prototyping
- [ ] Udacity: Software Architecture and Design
- [ ] Udacity: Software Development Process
- [ ] Udacity: Full Stack Foundations

### Be able to efficiently use a text editor

### Be able to communicate and collaborate well

- [ ] Google: Technical Writing
- [X] Book: Emotional Intelligence
- [X] Book: How to Win Friends & Influence People
- [X] Book: Influence: The Psychology of Persuasion
- [X] Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't
- [X] Book: Multipliers: How the Best Leaders Make Everyone Smarter
- [X] Book: Soft Skills: The software developer's life manual
- [X] Book: The New One Minute Manager
- [X] Youtube: Building a psychologically safe workplace | Amy Edmondson | TEDxHGSE

### Be familiar with the hiring pipeline

- [ ] Datacamp: Preparing for Statistics Interview Questions in Python
- [ ] Datacamp: Preparing for Coding Interview Questions in Python
- [X] Udacity: Optimize your GitHub
- [X] Udacity: Strengthen Your LinkedIn Network & Brand
- [X] Udacity: Data Science Interview Prep
- [X] Udacity: Full-Stack Interview Prep
- [ ] Udacity: Refresh Your Resume
- [ ] Udacity: Craft Your Cover Letter
- [ ] Udacity: Technical Interview
- [ ] Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers