Machine Learner

This list of resources is specifically targeted at Web Developers and Data Scientists…. so do with it what you will…This list borrows…
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Everything You Need To Become A Machine Learner
This list of resources is specifically targeted at Web Developers and Data Scientists…. so do with it what you will…This list borrows heavily from multiple lists created by : sindresorhus​
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Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.
Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”
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- \[📖\] Delivering Happiness - \[📖\] Good to Great: Why Some Companies Make the Leap…And Others Don’t - \[📖\] Hello, Startup: A Programmer’s Guide to Building Products, Technologies, and Teams - \[📖\] How Google Works - \[📖\] Learn to Earn: A Beginner’s Guide to the Basics of Investing and Business - \[📖\] Rework - \[📖\] The Airbnb Story - \[📖\] The Personal MBA - \[ \] Facebook: Digital marketing: get started - \[ \] Facebook: Digital marketing: go further - \[ \] Google Analytics for Beginners - \[ \] Moz: The Beginner’s Guide to SEO - \[ \] Smartly: Marketing Fundamentals - \[ \] Treehouse: SEO Basics - \[🅤\]ꭏ App Monetization - \[🅤\]ꭏ App Marketing - \[🅤\]ꭏ How to Build a Startup​
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Natural language processing
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Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.
### Neural networks
Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.
In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.
Be familiar with how Machine Learning is applied at other companies
[📰] How Facebook uses super-efficient AI models to detect hate speech​
[📰] Recent Advances in Google Translate​
[📰] Cannes: HowMachine Learning saves us $1.7M a year on document previews​
[📰] Machine Learning @ Monzo in 2020​
[📰] How image search works at Dropbox​
[ ] Real-world AI Case Studies​
[ ] Andrej Karpathy on AI at Tesla (Full Stack Deep Learning — August 2018)​
[ ] Jai Ranganathan at Data Science at Uber (Full Stack Deep Learning — August 2018)​
[ ] John Apostolopoulos of Cisco discusses “Machine Learning in Networking” 0:48:44
[ ] Joaquin Candela, Director of Applied Machine Learning , Facebook in conversation with Esteban Arcaute 0:52:27
[ ] Eric Colson, Chief Algorithms Officer, Stitch Fix 0:53:57
[ ] Claudia Perlich, Advisor to Dstillery and Adjunct Professor NYU Stern School of Business 0:51:59
[ ] Jeff Dean, Google Senior Fellow and SVP Google AI — Deep Learning to Solve Challenging Problems 0:58:45
[ ] James Parr, Director of Frontier Development Lab (NASA), FDL Europe & CEO, Trillium Technologies 0:55:46
[ ] Daphne Koller, Founder & CEO of Insitro — In Conversation with Carlos Bustamante 0:49:29
[ ] Eric Horvitz, Microsoft Research — AI in the Open World: Advances, Aspirations, and Rough Edges 0:56:11
[ ] Tony Jebara, Netflix — Machine Learning for Recommendation and Personalization 0:55:20
[💻] Analyzing Police Activity with pandas​
[💻] HR Analytics in Python: Predicting Employee Churn​
[💻] Predicting Customer Churn in Python​
[📺 ] How does YouTube recommend videos? — AI EXPLAINED! 0:33:53
[📺 ] How does Google Translate’s AI work? 0:15:02
[📺 ] Data Science in Finance 0:17:52
[📺 ] The Age of AI​
[ ] How Far is Too Far? | The Age of A.I. 0:34:39
[ ] Healed through A.I. | The Age of A.I. 0:39:55
[ ] Using A.I. to build a better human | The Age of A.I. 0:44:27
[ ] Love, art and stories: decoded | The Age of A.I. 0:38:57
[ ] The ‘Space Architects’ of Mars | The Age of A.I. 0:30:10
[ ] Will a robot take my job? | The Age of A.I. 0:36:14
[ ] Saving the world one algorithm at a time | The Age of A.I. 0:46:37
[ ] How A.I. is searching for Aliens | The Age of A.I. 0:36:12
[📺 ] Using Intent Data to Optimize the Self-Solve Experience​
[📺 ] Trillions of Questions, No Easy Answers: A (home) movie about how Google Search works​
[📺 ] Google Machine Learning System Design Mock Interview​
[📺 ] Netflix Machine Learning Mock Interview: Type-ahead Search​
[📺 ] Machine Learning design: Search engine for Q&A​
[📺 ] Engineering Systems for Real-Time Predictions @DoorDash​
[📺 ] How Gmail Uses Iterative Design, Machine Learning and AI to Create More Assistive Features​
[📺 ] Wayfair Data Science Explains It All: Human-in-the-loop Systems​
[📺 ] Leaving the lab: Building NLP applications that real people can use​
[📺 ] Machine Learning at Uber (Natural Language Processing Use Cases)​
[📺 ] Google Wave: Natural Language Processing​
[📺 ] Natural Language Understanding in Alexa​
[📺 ] The Machine Learning Behind Alexa’s AI Systems​
[📺 ] Ines Montani Keynote — Applied NLP Thinking​
[📺 ] Lecture 9: Lukas Biewald​
[📺 ] Lecture 13: Research Directions​
[📺 ] Lecture 14: Jeremy Howard​
[📺 ] Lecture 15: Richard Socher​
[📺 ] Machine learning across industries with Vicki Boykis 0:34:02
[📺 ] 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
[📺 ] [CVPR’21 WAD] Keynote — Andrej Karpathy, Tesla​
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Be able to frame anMachine Learning problem
· [ ] AWS: Types of Machine Learning Solutions​
· [📰] Apply Machine Learning to your Business​
· [📰] Resilience and Vibrancy: The 2020 Data & AI Landscape​
· [📰] Software 2.0​
· [📰] Highlights from ICML 2020​
· [📰] A Peek at Trends in Machine Learning​
· [📰] How to deliver on Machine Learning projects​
· [📰] Data Science as a Product​
· [📰] Customer service is full of machine learning problems​
· [📰] Choosing Problems in Data Science and Machine Learning​
· [📰] Why finance is deploying natural language processing​
· [📰] The Last 5 Years In Deep Learning​
· [📰] Always start with a stupid model, no exceptions.​
· [📰] Most impactful AI trends of 2018: the rise ofMachine Learning Engineering​
· [📰] Building machine learning products: a problem well-defined is a problem half-solved.​
· [📰] Simple considerations for simple people building fancy neural networks​
· [📰] Maximizing Business Impact with Machine Learning​
· [📖] AI Superpowers: China, Silicon Valley, and the New World Order​
· [📖] A Human’s Guide to Machine Intelligence​
· [📖] The Future Computed​
· [📖] Machine Learning Yearning by Andrew Ng​
· [📖] Prediction Machines: The Simple Economics of Artificial Intelligence​
· [📖] Building Machine Learning Powered Applications: Going from Idea to Product​
· [ ] Coursera: AI For Everyone​
· [💻] Data Science for Everyone​
· [💻] Machine Learning with the Experts: School Budgets​
· [💻] Machine Learning for Everyone​
· [💻] Data Science for Managers​
· [ ] Facebook: Field Guide to Machine Learning​
· [G] Introduction to Machine Learning Problem Framing​
· [ ] Pluralsight: How to Think About Machine Learning Algorithms​
· [ ] State of AI Report 2020​
· [📺 ] Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019​
· [📺 ] Hugging Face, Transformers | NLP Research and Open Source | Interview with Julien Chaumond​
· [📺 ] Vincent Warmerdam — Playing by the Rules-Based-Systems | PyData Eindhoven 2020​
· [📺 ] Building intuitions before building models​
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Be familiar with data ethics
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[📰] How to Detect Bias in AI​
[ ] Netflix: Coded Bias​
[ ] Netflix: The Great Hack​
[ ] Netflix: The Social Dilemma​
[ ] 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​
[📺 ] Lecture 9: Ethics (Full Stack Deep Learning — Spring 2021) 1:04:50
[📺 ] SE4AI: Ethics and Fairness 1:18:37
[📺 ] SE4AI: Security 1:18:24
[📺 ] SE4AI: Safety 1:17:37
Be able to import data from multiple sources
[ ] Docs: Beautiful Soup Documentation​
[💻] Importing Data in Python (Part 2)​
[💻] Web Scraping in Python​
Be able to setup data annotation efficiently
[📰] Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”​
[📰] We need Synthetic Data​
[📰] Weak Supervision for Online Discussions​
[📰] Machine Learning Infrastructure Tools for Data Preparation​
[📰] Exploring the Role of Human Raters in Creating NLP Datasets​
[📰] Inter-Annotator Agreement (IAA)​
[📰] How to compute inter-rater reliability metrics (Cohen’s Kappa, Fleiss’s Kappa, Cronbach Alpha, Krippendorff Alpha, Scott’s Pi, Inter-class correlation) in Python​
[📺 ] Snorkel: Dark Data and Machine Learning — Christopher Ré​
[📺 ] Training a NER Model with Prodigy and Transfer Learning​
[📺 ] Training a New Entity Type with Prodigy — annotation powered by active learning​
[📺 ] ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations​
[📺 ] Active Learning: Why Smart Labeling is the Future of Data Annotation | Alectio​
[📺 ] Lecture 8: Data Management (Full Stack Deep Learning — Spring 2021) 0:59:42
[📺 ] Lab 6: Data Labeling (Full Stack Deep Learning — Spring 2021) 0:05:06
[📺 ] Lecture 6: Data Management​
[📺 ] SE4AI: Data Quality 1:07:15
[📺 ] SE4AI: Data Programming and Intro to Big Data Processing 0:33:04
[📺 ] SE4AI: Managing and Processing Large Datasets 1:21:27
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Be able to manipulate data with Numpy
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[📰] A Visual Intro to NumPy and Data Representation​
[📰] Good practices with numpy random number generators​
[📰] NumPy Illustrated: The Visual Guide to NumPy​
[📰] NumPy Fundamentals for Data Science and Machine Learning​
[💻] Intro to Python for Data Science​
[ ] Pluralsight: Working with Multidimensional Data Using NumPy​
Be able to manipulate data with Pandas
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- **\[📰\]** Visualizing Pandas’ Pivoting and Reshaping Functions - **\[📰\]** A Gentle Visual Intro to Data Analysis in Python Using Pandas - **\[📰\]** Comprehensive Guide to Grouping and Aggregating with Pandas - **\[📰\]** 8 Python Pandas Value_counts() tricks that make your work more efficient - **\[💻\]** pandas Foundations - **\[💻\]** Pandas Joins for Spreadsheet Users - **\[💻\]** Manipulating DataFrames with pandas - **\[💻\]** Merging DataFrames with pandas - **\[💻\]** Data Manipulation with pandas - **\[💻\]** Optimizing Python Code with pandas - **\[💻\]** Streamlined Data Ingestion with pandas - **\[💻\]** Analyzing Marketing Campaigns with pandas - **\[ \]** edX: Implementing Predictive Analytics with Spark in Azure HDInsight - **\[📰\]** Modern Pandas - **\[ \]** Modern Pandas (Part 1) - **\[ \]** Modern Pandas (Part 2) - **\[ \]** Modern Pandas (Part 3) - **\[ \]** Modern Pandas (Part 4) - **\[ \]** Modern Pandas (Part 5) - **\[ \]** Modern Pandas (Part 6) - **\[ \]** Modern Pandas (Part 7) - **\[ \]** Modern Pandas (Part 8)​
Be able to manipulate data in spreadsheets
[💻] Spreadsheet basics​
[💻] Data Analysis with Spreadsheets​
[💻] Intermediate Spreadsheets for Data Science​
[💻] Pivot Tables with Spreadsheets​
[💻] Data Visualization in Spreadsheets​
[💻] Introduction to Statistics in Spreadsheets​
[💻] Conditional Formatting in Spreadsheets​
[💻] Marketing Analytics in Spreadsheets​
[💻] Error and Uncertainty in Spreadsheets​
[ ] edX: Analyzing and Visualizing Data with Excel​
Be able to manipulate data in databases
[ ] Codecademy: SQL Track​
[💻] Intro to SQL for Data Science​
[💻] Introduction to MongoDB in Python​
[💻] Intermediate SQL​
[💻] Exploratory Data Analysis in SQL​
[💻] Joining Data in PostgreSQL​
[💻] Querying with TransactSQL​
[💻] Introduction to Databases in Python​
[💻] Reporting in SQL​
[💻] Applying SQL to Real-World Problems​
[💻] Analyzing Business Data in SQL​
[💻] Data-Driven Decision Making in SQL​
[💻] Database Design​
[🅤]ꭏ SQL for Data Analysis​
[🅤]ꭏ Intro to relational database​
[🅤]ꭏ Database Systems Concepts & Design​
Be able to use Linux
Resources:
​Bash Proficiency In Under 15 Minutes​
​Cheat sheet and in-depth explanations located below main article contents… The UNIX shell program interprets user…​
​These Are The Bash Shell Commands That Stand Between Me And Insanity​
​I will not profess to be a bash shell wizard… but I have managed to scour some pretty helpful little scripts from Stack…​
​Bash Commands That Save Me Time and Frustration​
​Here’s a list of bash commands that stand between me and insanity.​
​Life Saving Bash Scripts Part 2​
​I am not saying they’re in any way special compared with other bash scripts… but when I consider that you can never…​
​What Are Bash Aliases And Why Should You Be Using Them!​
​A Bash alias is a method of supplementing or overriding Bash commands with new ones. Bash aliases make it easy for…​
​BASH CHEAT SHEET​
​My Bash Cheatsheet Index:​
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​holy grail of learning bash
[📰] Streamline your projects using Makefile​
[📰] Understand Linux Load Averages and Monitor Performance of Linux​
[📰] Command-line Tools can be 235x Faster than your Hadoop Cluster​
[ ] Calmcode: makefiles​
[ ] Calmcode: entr​
[ ] Codecademy: Learn the Command Line​
[💻] Introduction to Shell for Data Science​
[💻] Introduction to Bash Scripting​
[💻] Data Processing in Shell​
[ ] 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
[ ] Thoughtbot: Mastering the Shell​
[ ] Thoughtbot: tmux​
[🅤]ꭏ Linux Command Line Basics​
[🅤]ꭏ Shell Workshop​
[🅤]ꭏ Configuring Linux Web Servers​
[ ] Web Bos: Command Line Power User​
[📺 ] GNU Parallel​
Be able to perform feature selection and engineering
[📰] Tips for Advanced Feature Engineering​
[📰] Preparing data for a machine learning model​
[📰] Feature selection for a machine learning model​
[📰] Learning from imbalanced data​
[📰] Hacker’s Guide to Data Preparation for Machine Learning​
[📰] Practical Guide to Handling Imbalanced Datasets​
[💻] Analyzing Social Media Data in Python​
[💻] Dimensionality Reduction in Python​
[💻] Preprocessing for Machine Learning in Python​
[💻] Data Types for Data Science​
[💻] Cleaning Data in Python​
[💻] Feature Engineering for Machine Learning in Python​
[💻] Importing & Managing Financial Data in Python​
[💻] Manipulating Time Series Data in Python​
[💻] Working with Geospatial Data in Python​
[💻] Analyzing IoT Data in Python​
[💻] Dealing with Missing Data in Python​
[💻] Exploratory Data Analysis in Python​
[ ] edX: Data Science Essentials​
[🅤]ꭏ Creating an Analytical Dataset​
[📺 ] AppliedMachine Learning 2020–04 — Preprocessing 1:07:40
[📺 ] AppliedMachine Learning 2020–11 — Model Inspection and Feature Selection 1:15:15
Be able to experiment in a notebook
[📰] Securely storing configuration credentials in a Jupyter Notebook​
[📰] Automatically Reload Modules with %autoreload​
[ ] Calmcode: ipywidgets​
[ ] Documentation: Jupyter Lab​
[ ] Pluralsight: Getting Started with Jupyter Notebook and Python​
[📺 ] William Horton — A Brief History of Jupyter Notebooks​
[📺 ] I Like Notebooks​
[📺 ] I don’t like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)​
[📺 ] Ryan Herr — After model.fit, before you deploy| JupyterCon 2020​
[📺 ] nbdev live coding with Hamel Husain​
[📺 ] How to Use JupyterLab​
Be able to visualize data
[📰] Creating a Catchier Word Cloud Presentation​
[📰] Effectively Using Matplotlib​
[💻] Introduction to Data Visualization with Python​
[💻] Introduction to Seaborn​
[💻] Introduction to Matplotlib​
[💻] Intermediate Data Visualization with Seaborn​
[💻] Visualizing Time Series Data in Python​
[💻] Improving Your Data Visualizations in Python​
[💻] Visualizing Geospatial Data in Python​
[💻] Interactive Data Visualization with Bokeh​
[📺 ] AppliedMachine Learning 2020–02 Visualization and matplotlib 1:07:30
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
[ ] 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
[ ] 3Blue1Brown: Neural networks​
[ ] But what is a Neural Network? | Deep learning, chapter 1 0:19:13
[ ] Gradient descent, how neural networks learn | Deep learning, chapter 2 0:21:01
[ ] What is backpropagation really doing? | Deep learning, chapter 3 0:13:54
[ ] Backpropagation calculus | Deep learning, chapter 4 0:10:17
[📰] A Visual Tour of Backpropagation​
[📰] Entropy, Cross Entropy, and KL Divergence​
[📰] Interview Guide to Probability Distributions​
[📰] Introduction to Linear Algebra for Applied Machine Learning with Python​
[📰] Entropy of a probability distribution — in layman’s terms​
[📰] KL Divergence — in layman’s terms​
[📰] Probability Distributions​
[📰] Relearning Matrices as Linear Functions​
[📰] You Could Have Come Up With Eigenvectors — Here’s How​
[📰] PageRank — How Eigenvectors Power the Algorithm Behind Google Search​
[📰] Interactive Visualization of Why Eigenvectors Matter​
[📰] Cross-Entropy and KL Divergence​
[📰] Why Randomness Is Information?​
[📰] Basic Probability Theory​
[📰] Math You Need to Succeed InMachine Learning Interviews​
[📖] Basics of Linear Algebra for Machine Learning​
[💻] Introduction to Statistics in Python​
[💻] Foundations of Probability in Python​
[💻] Statistical Thinking in Python (Part 1)​
[💻] Statistical Thinking in Python (Part 2)​
[💻] Statistical Simulation in Python​
[ ] 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)​
[ ] 1. The Geometry of Linear Equations 0:39:49
[ ] 2. Elimination with Matrices. 0:47:41
[ ] 3. Multiplication and Inverse Matrices 0:46:48
[ ] 4. Factorization into A = LU 0:48:05
[ ] 5. Transposes, Permutations, Spaces R^n 0:47:41
[ ] 6. Column Space and Nullspace 0:46:01
[ ] 9. Independence, Basis, and Dimension 0:50:14
[ ] 10. The Four Fundamental Subspaces 0:49:20
[ ] 11. Matrix Spaces; Rank 1; Small World Graphs 0:45:55
[ ] 14. Orthogonal Vectors and Subspaces 0:49:47
[ ] 15. Projections onto Subspaces 0:48:51
[ ] 16. Projection Matrices and Least Squares 0:48:05
[ ] 17. Orthogonal Matrices and Gram-Schmidt 0:49:09
[ ] 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
[ ] 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
[ ] 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
[🅤]ꭏ Eigenvectors and Eigenvalues​
[🅤]ꭏ Linear Algebra Refresher​
[🅤]ꭏ Statistics​
[🅤]ꭏ Intro to Descriptive Statistics​
[🅤]ꭏ Intro to Inferential Statistics​
Be able to setup project structure
[📰] pydantic​
[📰] Organizing machine learning projects: project management guidelines​
[📰] Logging and Debugging in Machine Learning — How to use Python debugger and the logging module to find errors in your AI application​
[📰] Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation​
[📰] Configuring Google Colab Like A Pro​
[📰] Stop using print, start using loguru in Python​
[📰] Hypermodern Python​
[📰] Hypermodern Python Chapter 2: Testing​
[📰] Hypermodern Python Chapter 3: Linting​
[📰] Hypermodern Python Chapter 4: Typing​
[📰] Hypermodern Python Chapter 5: Documentation​
[📰] Hypermodern Python Chapter 6: CI/CD​
[📰] Push and pull: when and why to update your dependencies​
[📰] Reproducible and upgradable Conda environments: dependency management with conda-lock​
[📰] Options for packaging your Python code: Wheels, Conda, Docker, and more​
[📰] Making model training scripts robust to spot interruptions​
[ ] Calmcode: logging​
[ ] Calmcode: tqdm​
[ ] Calmcode: virtualenv​
[ ] Coursera: Structuring Machine Learning Projects​
[ ] Doc: Python Lifecycle Training​
[💻] Introduction to Data Engineering​
[💻] Conda Essentials​
[💻] Conda for Building & Distributing Packages​
[💻] Software Engineering for Data Scientists in Python​
[💻] Designing Machine Learning Workflows in Python​
[💻] Object-Oriented Programming in Python​
[💻] Command Line Automation in Python​
[💻] Creating Robust Python Workflows​
[ ] Developing Python Packages​
[ ] Treehouse: Object Oriented Python​
[ ] Treehouse: Setup Local Python Environment​
[🅤]ꭏ Writing READMEs​
[📺 ] Lecture 1: Introduction to Deep Learning​
[📺 ] Lecture 2: Setting Up Machine Learning Projects​
[📺 ] Lecture 3: Introduction to the Text Recognizer Project​
[📺 ] Lecture 4: Infrastructure and Tooling​
[📺 ] Hydra configuration​
[📺 ] Continuous integration​
[📺 ] Data Engineering +Machine Learning + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16​
[📺 ] OO Design and Testing Patterns for Machine Learning with Chris Gerpheide​
[📺 ] Tutorial: Sebastian Witowski — Modern Python Developer’s Toolkit​
[📺 ] Lecture 13:Machine Learning Teams (Full Stack Deep Learning — Spring 2021) 0:58:13
[📺 ] Lecture 5:Machine Learning Projects (Full Stack Deep Learning — Spring 2021) 1:13:14
[📺 ] Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning — Spring 2021) 1:07:21
Be able to version control code
​Understanding Git (A Beginners Guide Containing Cheat Sheets & Resources)
Basic Git Work Flow.levelup.gitconnected.com​
​Github Repositories That Will Teach You How To Code For Free!
Update: here’s a repo full of helpful repos:levelup.gitconnected.com​
[📰] Mastering Git Stash Workflow​
[📰] How to Become a Master of Git Tags​
[📰] How to track large files in Github / Bitbucket? Git LFS to the rescue​
[📰] Keep your git directory clean with git clean and git trash​
[ ] Codecademy: Learn Git​
[ ] Code School: Git Real​
[💻] Introduction to Git for Data Science​
[ ] Thoughtbot: Mastering Git​
[🅤]ꭏ GitHub & Collaboration​
[🅤]ꭏ How to Use Git and GitHub​
[🅤]ꭏ Version Control with Git​
[📺 ] 045 Introduction to Git LFS​
[📺 ] Git & Scripting​
Be able to setup model validation
[📰] Evaluating a machine learning model​
[📰] Validating your Machine Learning Model​
[📰] Measuring Performance: AUPRC and Average Precision​
[📰] Measuring Performance: AUC (AUROC)​
[📰] Measuring Performance: The Confusion Matrix​
[📰] Measuring Performance: Accuracy​
[📰] ROC Curves: Intuition Through Visualization​
[📰] Precision, Recall, Accuracy, and F1 Score for Multi-Label Classification​
[📰] The Complete Guide to AUC and Average Precision: Simulations and Visualizations​
[📰] Best Use of Train/Val/Test Splits, with Tips for Medical Data​
[📰] The correct way to evaluate online machine learning models​
[📰] Proxy Metrics​
[📺 ] Accuracy as a Failure​
[📺 ] AppliedMachine Learning 2020–09 — Model Evaluation and Metrics 1:18:23
[📺 ] Machine Learning Fundamentals: Cross Validation 0:06:04
[📺 ] Machine Learning Fundamentals: The Confusion Matrix 0:07:12
[📺 ] Machine Learning Fundamentals: Sensitivity and Specificity 0:11:46
[📺 ] Machine Learning Fundamentals: Bias and Variance 0:06:36
[📺 ] ROC and AUC, Clearly Explained! 0:16:26
​
Be familiar with inner working of models
​
***Bays theorem is super interesting and applicable ==> — \[📰\]*** Naive Bayes classification​
[📰] Linear regression​
[📰] Polynomial regression​
[📰] Logistic regression​
[📰] Decision trees​
[📰] K-nearest neighbors​
[📰] Support Vector Machines​
[📰] Random forests​
[📰] Boosted trees​
[📰] Hacker’s Guide to Fundamental Machine Learning Algorithms with Python​
[📰] Neural networks: activation functions​
[📰] Neural networks: training with backpropagation​
[📰] Neural Network from scratch-part 1​
[📰] Neural Network from scratch-part 2​
[📰] Perceptron to Deep-Neural-Network​
[📰] One-vs-Rest strategy for Multi-Class Classification​
[📰] Multi-class Classification — One-vs-All & One-vs-One​
[📰] One-vs-Rest and One-vs-One for Multi-Class Classification​
[📰] Deep Learning Algorithms — The Complete Guide​
[📰] Machine Learning Techniques Primer​
[ ] AWS: Understanding Neural Networks​
[📖] Grokking Deep Learning​
[📖] Make Your Own Neural Network​
[ ] Coursera: Neural Networks and Deep Learning​
[💻] Extreme Gradient Boosting with XGBoost​
[💻] Ensemble Methods in Python​
[ ] StatQuest: Machine Learning​
[ ] 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: StatQuest: Linear Models Pt.2 — t-tests and ANOVA 0:11:37
[ ] 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
[⨊ ] Part 1: Regression Main Ideas 0:15:52
[⨊ ] Part 2: Regression Details 0:26:45
[⨊ ] Part 3: Classification 0:17:02
[⨊ ] Part 4: Classification Details 0:36:59
[⨊ ] 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
[🅤]ꭏ Classification Models​
[📺 ] 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
[📺 ] AppliedMachine Learning 2020–03 Supervised learning and model validation 1:12:00
[📺 ] AppliedMachine Learning 2020–05 — Linear Models for Regression 1:06:54
[📺 ] AppliedMachine Learning 2020–06 — Linear Models for Classification 1:07:50
[📺 ] AppliedMachine Learning 2020–07 — Decision Trees and Random Forests 1:07:58
[📺 ] AppliedMachine Learning 2020–08 — Gradient Boosting 1:02:12
[📺 ] AppliedMachine Learning 2020–18 — Neural Networks 1:19:36
[📺 ] AppliedMachine Learning 2020–12 — AutoML (plus some feature selection) 1:25:38
​
Be able to improve models
[📰] Deep neural networks: preventing overfitting​
[📰] Normalizing your data (specifically, input and batch normalization)​
[📰] Batch Normalization​
[📰] Are Deep Neural Networks Dramatically Overfitted?​
[📰] In-layer normalization techniques for training very deep neural networks​
[📰] Label Smoothing Explained using Microsoft Excel​
[📰] Uncertainty Quantification Part 4: Leveraging Dropout in Neural Networks (CNNs)​
[📰] Simple Ways to Tackle Class Imbalance​
[📺 ] AppliedMachine Learning 2020–10 — Calibration, Imbalanced data 1:16:14
[📺 ] Lecture 10: Troubleshooting Deep Neural Networks​
​
Be familiar with fundamental Machine Learning concepts
​CNN​
[📰] Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks​
[📰] MLE and MAP — in layman’s terms​
[ ] An overview of gradient descent optimization algorithms​
[📰] Optimization for Deep Learning Highlights in 2017​
[📰] Gradient descent​
[📰] Setting the learning rate of your neural network​
[📰] Cross-entropy for classification​
[📰] Dismantling Neural Networks to Understand the Inner Workings with Math and Pytorch​
[💻] AI Fundamentals​
[💻] Foundations of Predictive Analytics in Python (Part 1)​
[💻] Foundations of Predictive Analytics in Python (Part 2)​
[ ] Elements of AI​
[ ] edX: Principles of Machine Learning​
[ ] edX: Data Science Essentials​
[ 📺] Fast.ai: Deep Learning for Coder (2020)​
[ 📺] Lesson 0​
[📺 ] Lesson 1​
[📺 ] Lesson 2​
[📺 ] Lesson 3​
[📺 ] Lesson 4​
[ 📺] Lesson 5​
[ 📺] Lesson 6​
[ 📺] Lesson 7​
[📺 ] Lesson 8​
[📺 ] Deep Double Descent​
Implement models in scikit-learn
[📰] Stacking made easy with Sklearn​
[📰] Curve Fitting With Python​
[📰] A Guide to Calibration Plots in Python​
[ ] Calmcode: human-learn​
[💻] Supervised Learning with scikit-learn​
[💻] Machine Learning with Tree-Based Models in Python​
[💻] Introduction to Linear Modeling in Python​
[💻] Linear Classifiers in Python​
[💻] Generalized Linear Models in Python​
[ ] Notebook: scikit-learn tips​
[ ] Pluralsight: Building Machine Learning Models in Python with scikit-learn​
[ ] Video: human learn​
[📺 ] dabl: Automatic Machine Learning with a Human in the Loop 00:25:43
[📺 ] Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python​
[📺 ] DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1​
​
Be able to implement models in Tensorflow and Keras
[ ] Coursera: Introduction to Tensorflow​
[ ] Coursera: Convolutional Neural Networks in TensorFlow​
[ ] Deeplizard: Keras — Python Deep Learning Neural Network API​
[📖] Deep Learning with Python (Page: 276)​
[💻] Deep Learning in Python​
[💻] Convolutional Neural Networks for Image Processing​
[💻] Introduction to TensorFlow in Python​
[💻] Introduction to Deep Learning with Keras​
[💻] Advanced Deep Learning with Keras​
[G] Machine Learning Crash Course​
[ ] Pluralsight: Deep Learning with Keras​
[🅤]ꭏ Intro to TensorFlow for Deep Learning​
Be able to implement models in PyTorch
[📰] Keeping Up with PyTorch Lightning and Hydra​
[📰] The One PyTorch Trick Which You Should Know​
[📰] How does automatic differentiation really work?​
[📰] 7 Tips To Maximize PyTorch Performance​
[📰] An introduction to PyTorch Lightning with comparisons to PyTorch​
[📰] Converting From Keras To PyTorch Lightning​
[📰] From PyTorch to PyTorch Lightning — A gentle introduction​
[📰] Introducing PyTorch Lightning Sharded: Train SOTA Models, With Half The Memory​
[📰] Sharded: A New Technique To Double The Size Of PyTorch Models​
[📰] Understanding Bidirectional RNN in PyTorch​
[📰] A developer-friendly guide to mixed precision training with PyTorch​
[📰] A developer-friendly guide to model pruning in PyTorch​
[📰] A developer-friendly guide to model quantization with PyTorch​
[📰] Tricks for training PyTorch models to convergence more quickly​
[📰] PyTorch Lightning Bolts — From Linear, Logistic Regression on TPUs to pre-trained GANs​
[📰] Scaling Logistic Regression Via Multi-GPU/TPU Training​
[📰] Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups​
[📰] PyTorch Lightning 0.9 — synced BatchNorm, DataModules and final API!​
[📰] PyTorch Lightning: Metrics​
[📰] PyTorch Multi-GPU Metrics Library and More in PyTorch Lightning 0.8.1​
[📰] Distributed model training in PyTorch using DistributedDataParallel​
[📰] Distributed model training in PyTorch using DistributedDataParallel​
[📰] EINSUM IS ALL YOU NEED — EINSTEIN SUMMATION IN DEEP LEARNING​
[📰] Faster Deep Learning Training with PyTorch — a 2021 Guide​
[📰] Fit More and Train Faster With ZeRO via DeepSpeed and FairScale​
[📰] PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA​
[📰] But what are PyTorch DataLoaders really?​
[📰] Using PyTorch + NumPy? You’re making a mistake.​
[📰] How Wadhwani AI Uses PyTorch To Empower Cotton Farmers​
[📰] Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health​
[📰] How to Build a Streaming DataLoader with PyTorch​
[📰] Transform your ML-model to Pytorch with Hummingbird​
[📰] PyTorch Loss Functions: The Ultimate Guide​
[📰] Pad pack sequences for Pytorch batch processing with DataLoader​
[📰] Model Parallelism​
[ ] Notebook: Tensor Arithmetic​
[ ] Notebook: Autograd​
[ ] Notebook: Optimization​
[ ] Notebook: Network modules​
[ ] Notebook: Datasets and Dataloaders​
[ ] Documentation: Pytorch Lightning​
[💻] Introduction to Deep Learning with PyTorch​
[ ] Deeplizard: Neural Network Programming — Deep Learning with PyTorch​
[🅤]ꭏ Intro to Deep Learning with PyTorch​
[📺 ] PyTorch Lightning 101​
[ ] Training a classification model on MNIST with PyTorch​
[ ] From PyTorch to PyTorch Lightning​
[ ] Lightning Data Modules​
[ ] PyTorch Dropout, Batch size and interactive debugging​
[ ] Episode 4: Implementing a PyTorch Trainer: PyTorch Lightning Trainer and callbacks under-the-hood​
[📺 ] SimCLR with PyTorch Lightning​
[📺 ] PyTorch Performance Tuning Guide 26:41:00
[📺 ] 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
[📺 ] Learn with Lightning​
[📺 ] PyTorch Tutorial — RNN & LSTM & GRU — Recurren Neural Nets 00:15:51
[📺 ] Pytorch Zero to All​
[ ] PyTorch Lecture 01: Overview 0:10:18
[ ] PyTorch Lecture 02: Linear Model 0:12:52
[ ] PyTorch Lecture 03: Gradient Descent 0:08:24
[ ] PyTorch Lecture 04: Back-propagation and Autograd 0:15:25
[ ] PyTorch Lecture 05: Linear Regression in the PyTorch way 0:11:50
[ ] PyTorch Lecture 06: Logistic Regression 0:10:41
[ ] PyTorch Lecture 07: Wide and Deep 0:10:37
[ ] PyTorch Lecture 08: PyTorch DataLoader 0:06:41
[ ] PyTorch Lecture 09: Softmax Classifier 0:18:47
[ ] PyTorch Lecture 10: Basic CNN 0:15:52
[ ] PyTorch Lecture 11: Advanced CNN 0:12:58
[ ] PyTorch Lecture 12: RNN1 — Basics 0:28:47
[ ] PyTorch Lecture 13: RNN 2 — Classification 0:17:22
[ ] PyTorch Developer Day 2020 | Full Livestream​
[📺 ] Lightning Chat: How a Grandmaster Won a Kaggle Competition Using Pytorch Lightning​
[📺 ] Production Inference Deployment with PyTorch​
[📺 ] What is Automatic Differentiation?​
[📺 ] JAX: accelerated machine learning research via composable function transformations in Python​
​
Be able to implement models using cloud services
[ ] AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription​
[ ] AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker​
[ ] AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications​
[ ] AWS: Hands-on Rekognition: Automated Video Editing​
[ ] AWS: Introduction to Amazon Comprehend​
[ ] AWS: Introduction to Amazon Comprehend Medical​
[ ] AWS: Introduction to Amazon Elastic Inference​
[ ] AWS: Introduction to Amazon Forecast​
[ ] AWS: Introduction to Amazon Lex​
[ ] AWS: Introduction to Amazon Personalize​
[ ] AWS: Introduction to Amazon Polly​
[ ] AWS: Introduction to Amazon SageMaker Ground Truth​
[ ] AWS: Introduction to Amazon SageMaker Neo​
[ ] AWS: Introduction to Amazon Transcribe​
[ ] AWS: Introduction to Amazon Translate​
[ ] AWS: Introduction to AWS Marketplace — Machine Learning Category​
[ ] AWS: Machine Learning Exam Basics​
[ ] AWS: Neural Machine Translation with Sockeye​
[ ] AWS: Process Model: CRISP-DM on the AWS Stack​
[ ] AWS: Satellite Image Classification in SageMaker​
[💻] Introduction to AWS Boto in Python​
[ ] edX: Amazon SageMaker: Simplifying Machine Learning Application Development​
By Bryan Guner on August 30, 2021.
​Canonical link​
Exported from Medium on August 31, 2021.