Learning Philosophy: Master Adjacent Disciplines, The Power of Tiny Gains, T-shaped skills
- Book: Delivering Happiness: A Path to Profits, Passion, and Purpose
- Book: Good to Great: Why Some Companies Make the Leap...And Others Don't
- Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
- Book: How Google Works
- Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business
- Book: Rework
- Book: The Airbnb Story
- Facebook: Digital marketing: get started
- Facebook: Digital marketing: go further
- Google Analytics for Beginners
- Google: Fundamentals of Digital Marketing
- Moz: The Beginner's Guide to SEO
- Smartly: Marketing Fundamentals
- Treehouse: SEO Basics
- The Personal MBA: Master the Art of Business
- Thoughtbot: Analytics for Developers
- Udacity: App Monetization
- Udacity: App Marketing
- Udacity: Get Your Startup Started
- Udacity: How to Build a Startup
- [Pattern Recognition (following Bishop book)]
- AWS: Types of Machine Learning Solutions
- Book: AI Superpowers: China, Silicon Valley, and the New World Order
- Book: A Human's Guide to Machine Intelligence
- Book: The Future Computed
- Book: Machine Learning Yearning by Andrew Ng
- Book: Prediction Machines: The Simple Economics of Artificial Intelligence
- Coursera: AI For Everyone
- Datacamp: Case Studies in Statistical Thinking
- Datacamp: Data Science for Everyone
- [Datacamp: Machine Learning with the E perts: School Budgets](https://www.datacamp.com/courses/machine-learning-with-the-e perts-school-budgets)
- Datacamp: Machine Learning for Everyone
- Datacamp: Analyzing Police Activity with pandas
- Datacamp: Data Science for Managers
- Facebook: Field Guide to Machine Learning
- Google: Art and Science of Machine Learning
- Google: How Google does Machine Learning
- 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
- Pluralsight: How to Think About Machine Learning Algorithms
- Udacity: Problem Solving with Advanced Analytics
- Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
- Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
- [Youtube: Training a NER Model with Prodigy and Transfer Learning](https://youtu.be/59BKHO_ BPA)
- Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning
- Datacamp: Intro to Python for Data Science
- Pluralsight: Working with Multidimensional Data Using NumPy
- Datacamp: pandas Foundations
- Datacamp: Pandas Joins for Spreadsheet Users
- 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
- 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
- [ed : Analyzing and Visualizing Data with E cel](https://www.ed .org/course/analyzing-visualizing-data-e cel-microsoft-dat206 -7)
- [Youtube: Jake VanderPlas - E ploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018](https://www.youtube.com/watch?v=ms29ZPUK bU)
- UWData: Data Visualization Curriculum
- [Book: Learn SQL the hard way](http://sql.learncodethehardway.org/book/e 1.html)
- Codecademy: SQL Track
- Codecademy: SQL: Table Transformation
- Codecademy: SQL: Analyzing Business Metrics
- Datacamp: Intro to SQL for Data Science
- Datacamp: Introduction to MongoDB in Python
- Datacamp: Intermediate SQL
- [Datacamp: E ploratory Data Analysis in SQL](https://www.datacamp.com/courses/sql-for-e ploratory-data-analysis)
- Datacamp: Joining Data in PostgreSQL
- 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
- Khan Academy: SQL
- Launch School: Introduction to SQL
- Treehouse: Using Databases in Python
- Udacity: SQL for Data Analysis
- Udacity: Intro to relational database
- Udacity: Database Systems Concepts & Design
- Plural Sight: Introduction to MongoDB
- Bash Programming
- Codecademy: Learn the Command Line
- CONQUERING THE COMMAND LINE
- 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: tmu
- [Udacity: Linu Command Line Basics](https://www.udacity.com/course/linu -command-line-basics--ud595)
- Udacity: Linu Web Servers
- Udacity: Shell Workshop
- Udacity: Web Tooling & Automation
- [Web Bos: Command Line Power User](https://www.youtube.com/watch?v=DP218aBHm1Q&list=PLu8EoS D HP7t PJp5ZmUpuT7sFvrswzf&inde =2)
- Datacamp: Analyzing Social Media Data in Python
- Datacamp: Dimensionality Reduction in Python
- Datacamp: Preprocessing for Machine Learning in Python
- Datacamp: Data Types for Data Science
- Datacamp: Cleaning Data in Python
- Datacamp: Feature Engineering for Machine Learning in Python
- Datacamp: Importing Data in Python (Part 2)
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- Datacamp: Web Scraping in Python
- Datacamp: Analyzing IoT Data in Python
- Datacamp: Dealing with Missing Data in Python
- [Datacamp: E ploratory Data Analysis in Python](https://www.datacamp.com/courses/e ploratory-data-analysis-in-python)
- [ed : Data Science Essentials](https://www.ed .org/course/data-science-essentials-microsoft-dat203-1 -5)
- Google: Feature Engineering
- Udacity: Creating an Analytical Dataset
- Datacamp: Introduction to Data Visualization with Python
- Datacamp: Introduction to Seaborn
- 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
- Paper: A Neural Probabilistic Language Model
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- Paper: Sequence to Sequence Learning with Neural Networks
- [Paper: Neural Machine Translation by Jointly Learning to Align and Translate](https://ar iv.org/abs/1409.0473)
- [Paper: Attention Is All You Need](https://ar iv.org/abs/1706.03762)
- [Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://ar iv.org/abs/1810.04805)
- [Paper: LNet: Generalized Autoregressive Pretraining for Language Understanding](https://ar iv.org/abs/1906.08237)
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- [Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://ar iv.org/abs/1907.11692)
- Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
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- [Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback](https://ar iv.org/pdf/1205.2618.pdf)
- Paper: Factorization Machines
- [Paper: Wide & Deep Learning for Recommender Systems](https://ar iv.org/pdf/1606.07792.pdf)
- [Paper: Neural Factorization Machines for Sparse Predictive Analytics](https://l.facebook.com/l.php?u=https%3A%2F%2Far iv.org%2Fpdf%2F1708.05027.pdf&h=AT3VuDk1rSqAkgo1 79wl9F tb7SFMT01B1M LMvp0O8sy 2BuHYB70EJkMwVngQtShj0yTTn6laoRQ3I7StkJQJ9j1b8DiHM7g Nv7dWvL9S_khSF4wWZA9No70BhewiggJ8a8Pa0jTnq4_ppOIsk-qDYVkyJM5QuoSSg)
- Paper: Multiword E pressions: A Pain in the Neck for NLP
- [Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://ar iv.org/pdf/1912.01703.pdf)
- [Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS](https://ar iv.org/pdf/1909.11942.pdf)
- [Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://ar iv.org/abs/1902.06162)
- [Paper: A Simple Framework for Contrastive Learning of Visual Representations](https://ar iv.org/pdf/2002.05709.pdf)
- [Paper: Self-Supervised Learning of Prete t-Invariant Representations](https://ar iv.org/abs/1912.01991)
- [Paper: Fi Match: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://ar iv.org/abs/2001.07685)
- [Paper: Self-Labelling via Simultaneous Clustering and Representation Learning](https://www.robots.o .ac.uk/~vgg/research/self-label/)
- [Paper: A Survey on Conte tual Embeddings](https://ar iv.org/abs/2003.07278v1)
- [Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification](https://ar iv.org/abs/2002.08721)
- [Paper: Shortcut Learning in Deep Neural Networks](https://ar iv.org/abs/2004.07780)
- [Paper: Multi-document Summarization by using Te tRank and Ma imal Marginal Relevance for Te t in Bahasa Indonesia](https://www.researchgate.net/publication/338940065_Multi-document_Summarization_by_using_Te tRank_and_Ma imal_Marginal_Relevance_for_Te t_in_Bahasa_Indonesia)
- [Paper: Train Once, Test Anywhere: Zero-Shot Learning for Te t Classification](https://ar iv.org/abs/1712.05972)
- [Paper: Zero-shot Te t Classification With Generative Language Models](https://ar iv.org/abs/1912.10165)
- [Paper: How to Fine-Tune BERT for Te t Classification?](https://ar iv.org/abs/1905.05583)
- [Paper: Universal Sentence Encoder](https://ar iv.org/abs/1803.11175)
- [Paper: Enriching Word Vectors with Subword Information](https://ar iv.org/abs/1607.04606)
- [Paper: Deep Learning Based Te t Classification: A Comprehensive Review](https://ar iv.org/abs/2004.03705)
- [Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://ar iv.org/abs/2005.04118)
- Whitepaper: Architecting for the Cloud AWS Best Practices
- Whitepaper: AWS Well-Architected Framework
- Whitepaper: AWS Security Best Practices
- Whitepaper: Blue/Green Deployments on AWS
- Whitepaper: Microservices on AWS
- Whitepaper: Optimizing Enterprise Economics with Serverless Architectures
- Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS
- Whitepaper: Running Containerized Microservices on AWS
- Whitepaper: Serverless Architectures with AWS Lambda
- Book: Basics of Linear Algebra for Machine Learning
- Book: Doing Math with Python
- 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
- [ed : Essential Statistics for Data Analysis using E cel](https://www.ed .org/course/essential-statistics-data-analysis-using-microsoft-dat222 -1)
- [Essence of Linear Algebra](https://www.youtube.com/watch?v=fNk_zzaMoSs&inde =1&list=PLZHQObOWTQDPD3MizzM2 VFitgF8hE_ab)
- 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)
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
- Lecture 9
- Lecture 10
- Lecture 11
- Lecture 14
- Lecture 15
- Lecture 16
- Lecture 17
- Lecture 21
- Lecture 22
- Lecture 30
- Lecture 31
- Lecture 33
- Lecture 34
- MIT:Introduction to probability
- [Part 1: The fundamentals]
- [Part 2: Inference & Limit Theorems]
- [Part 3: Random Processes]
- Udacity: Algebra Review
- Udacity: Differential Equations in Action
- Udacity: Eigenvectors and Eigenvalues
- Udacity: Linear Algebra Refresher
- Udacity: Statistics
- Udacity: Intro to Descriptive Statistics
- Udacity: Intro to Inferential Statistics
- MIT statistics(open course ware)
- Standford: Machine Learning with Graphs-Fall 2019
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
- Lecture 9
- Lecture 10
- Lecture 11
- Lecture 12
- Lecture 13
- Lecture 14
- Lecture 15
- Lecture 16
- Full Stack Deep Learning-Fall 2019
- Lecture 1(Fundamentals of Deep learning)
- Lecture 2(CNN)
- Lecture 3(Computer Vision Applications)
- Lecture 4(RNN)
- Lecture 5(RNNs)
- Lecture 6(Transfer learning and Transformer)
- Lecture 7(Transformer)
- Lecture 8(ML Projects)
- Lecture 9
- Lecture 10
- Lecture 11
- Lecture 12
- Lecture 13
- Lecture 14
- Lecture 15
- Lecture 16
- Coursera: Structuring Machine Learning Projects
- Datacamp: Conda Essentials
- Datacamp: Conda for Building & Distributing Packages
- Datacamp: Creating Robust Python Workflows
- Datacamp: Software Engineering for Data Scientists in Python
- Datacamp: Designing Machine Learning Workflows in Python
- Datacamp: Object-Oriented Programming in Python
- Datacamp: Command Line Automation in Python
- Datacamp: Introduction to Data Engineering
- [Datacamp: E perimental Design in Python](https://www.datacamp.com/courses/e perimental-design-in-python)
- Full Stack Deep Learning Bootcamp: March 2019
- Lecture 1: Introduction to Deep Learning
- Lecture 2: Setting Up Machine Learning Projects
- [Lecture 3: Introduction to the Te t Recognizer Project](https://youtu.be/mmlvGLS KLc)
- Lecture 4: Infrastructure and Tooling
- Lecture 5: Tracking E periments
- [Lecture 6: Data Management](https://youtu.be/T5jv8- ZhZI)
- Lecture 7: Machine Learning Teams
- Lecture 9: Lukas Biewald
- Lecture 10: Troubleshooting Deep Neural Networks
- Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment
- Lecture 12: Testing and Deployment
- Lecture 13: Research Directions
- [Lecture 14: Jeremy Howard](https://youtu.be/hZd3 _nGdew)
- Lecture 15: Richard Socher
- Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning
- MIT: The Missing Semester of CS Education
- Treehouse: Object Oriented Python
- Treehouse: Setup Local Python Environment
- Udacity: Writing READMEs
- AWS: Semantic Segmentation E plained
- AWS: The Elements of Data Science
- AWS: Understanding Neural Networks
- Book: Pattern Recognition and Machine Learning
- Coursera: Neural Networks and Deep Learning
- Datacamp: AI Fundamentals
- Datacamp: Kaggle Competition
- [Datacamp: E treme Gradient Boosting with GBoost](https://www.datacamp.com/courses/e treme-gradient-boosting-with- gboost)
- 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)
- Datacamp: Ensemble Methods in Python
- Datacamp: HR Analytics in Python: Predicting Employee Churn
- Datacamp: Predicting Customer Churn in Python
- Elements of AI
- [ed : Principles of Machine Learning](https://www.ed .org/course/principles-machine-learning-microsoft-dat203-2 -5)
- [ed : Data Science Essentials](https://www.ed .org/course/data-science-essentials-microsoft-dat203-1 -5)
- [ed : Implementing Predictive Analytics with Spark in Azure HDInsight](https://www.ed .org/course/implementing-predictive-analytics-spark-microsoft-dat202-3 -2)
- DeepMind: Inefficient Data Efficiency
- [DeepMind: DeepMind UCL | Deep Learning Lecture Series 2020](https://www.youtube.com/playlist?list=PLqYmG7hTraZCD Z44o4p3N5Anz3lLRVZF)
- Google: Launching into Machine Learning
- Book: Grokking Deep Learning
- Book: Make Your Own Neural Network
- MIT: 6.S191: Introduction to Deep Learning
- Pluralsight: Understanding Algorithms for Recommendation Systems
- Pluralsight: Deep Learning: The Big Picture
- [Udacity: A Friendly Introduction to Machine Learning](https://www.youtube.com/playlist?list=PLAw Tw4SYaPknYBrOQ 6UCyq67kprq e3)
- 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
- [Youtube: DETR: End-to-End Object Detection with Transformers (Paper E plained)](https://www.youtube.com/watch?v=T35ba_V kMY)
- [Youtube: Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift](https://www.youtube.com/watch?v=tpAr5-Y4J U)
- Youtube: How do we check if a neural network has learned a specific phenomenon?
- Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial e ample compilation
- CS294-158-SP20 Deep Unsupervised Learning Spring 2020
- Datacamp: Customer Segmentation in Python
- Google: Clustering
- Google: Recommendation Systems
- Udacity: Segmentation and Clustering
- Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper E plained)
- Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper E plained)
- Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)
- Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning
- Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81
- 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
- Google: ML Practicum: Image Classification
- Udacity: Introduction to Computer Vision
- Standford: NLP with Deep Learning 2019
Website-1
syllabus-2
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
- Lecture 9
- Lecture 10
- Lecture 11
- Lecture 12
- Lecture 13
- Lecture 14
- Lecture 15
- Lecture 16
- Lecture 17
- Lecture 18
- Lecture 19
- Lecture 20
- A friendly introduction to Recurrent Neural Networks
- Coursera: Sequence Models
- Coursera: Natural Language Processing in TensorFlow
- Datacamp: Advanced NLP with spaCy
- Datacamp: Building Chatbots in Python
- Datacamp: Clustering Methods with SciPy
- Datacamp: Feature Engineering for NLP in Python
- Datacamp: Machine Translation in Python
- Datacamp: Natural Language Processing Fundamentals in Python
- Datacamp: Natural Language Generation in Python
- Datacamp: RNN for Language Modeling
- [Datacamp: Regular E pressions in Python](https://www.datacamp.com/courses/regular-e pressions-in-python)
- Datacamp: Sentiment Analysis in Python
- Datacamp: Spoken Language Processing in Python
- [fast.ai Code-First Intro to Natural Language Processing](https://www.youtube.com/playlist?list=PLtmWHN -gukKoc QOkQjuV glSDYWsSh9)
- [RNN and LSTM](https://www.youtube.com/watch?v=WCUNPb-5EYI&inde =2&list=PLVZqlMpoM6kbaeyS hdtgQPFEC5nV7Faa&t=0s)
- [Spacy Tutorial](https://www.youtube.com/watch?v=cgwDB1THUBY&list=PLJ39kWiJ Siz1LK8d_fy b7FTn4mBYOsD)
- Stanford CS224U: Natural Language Understanding | Spring 2019
- [Te tBlob Tutorial Series](https://www.youtube.com/watch?v=4k2cqUIjb8g&list=PLJ39kWiJ SizrWpC7hcu1_mLN EPzN0gF)
- [Treehouse: Regular e pression](https://teamtreehouse.com/library/regular-e pressions-in-python)
- [Youtube: BERT Research Series](https://www.youtube.com/playlist?list=PLam9sigHPGwOBuH4_4fr- vDbe5uneaf6)
- YouTube: Intro to NLP with Spacy
- Talk: Practical NLP for the Real World
- YouTube: Level 3 AI Assistant Conference 2020
- Youtube: Conversation Analysis Theory in Chatbots | Michael Szul
- Youtube: Designing Practical NLP Solutions | Ines Montani
- Youtube: Effective Copywriting for Chatbots | Hans Van Dam
- [Youtube: Distilling BERT | Sam Sucik](https://youtu.be/ ji8NmL3FvQ?list=LLqn7Nv8Zg6tWbBonrUOJGwQ)
- Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam
- Youtube: From Research to Production – Our Process at Rasa | Tanja Bunk
- Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol
- [Youtube: A brief history of the Transformer architecture in NLP](https://www.youtube.com/watch?v=iH-wmt Hunk)
- Youtube: The Transformer neural network architecture e plained. “Attention is all you need” (NLP)
- [Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision](https://www.youtube.com/watch?v=dd7nE4nb N0)
- Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)
- Youtube: Ilya Sutskever - GPT-2
- Youtube: NLP Masterclass | Modeling Fallacies in NLP
- [Youtube: What is GPT-3? Showcase, possibilities, and implications](https://youtu.be/5fq POaaqi0)
- Youtube: Te tAttack: A Framework for Data Augmentation and Adversarial Training in NLP
- Datacamp: Machine Learning for Finance in Python
- 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
- Datacamp: Predicting CTR with Machine Learning in Python
- Datacamp: Intro to Financial Concepts using Python
- 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
- [DeepLizard: Reinforcement Learning - Goal Oriented Intelligence](https://www.youtube.com/playlist?list=PLZbbT5o_s2 oWNVdDudn51 M8lOuZ_Njv)
- Datacamp: Supervised Learning with scikit-learn
- Datacamp: Unsupervised Learning in Python
- Datacamp: Machine Learning with Tree-Based Models in Python
- Datacamp: Introduction to Linear Modeling in Python
- Datacamp: Linear Classifiers in Python
- Datacamp: Generalized Linear Models in Python
- Pluralsight: Building Machine Learning Models in Python with scikit-learn
- [Youtube: Applied Machine Learning 2020](https://www.youtube.com/playlist?list=PL_pVmAaAn IRnSw6wiCpSvshFyCREZmlM)
- Coursera: Introduction to Tensorflow
- Coursera: Convolutional Neural Networks in TensorFlow
- Coursera: Getting Started With Tensorflow 2
- Coursera: Customising your models with TensorFlow 2
- [Deeplizard: Keras - Python Deep Learning Neural Network API](https://www.youtube.com/playlist?list=PLZbbT5o_s2 rwRn k_yCPtnqqo4_u2YGL)
- Book: Deep Learning with Python
- Datacamp: Deep Learning in Python
- Datacamp: Convolutional Neural Networks for Image Processing
- Datacamp: Introduction to TensorFlow in Python
- Datacamp: Introduction to Deep Learning with Keras
- Datacamp: Advanced Deep Learning with Keras
- Google: Intro to Tensorflow
- Google: Machine Learning Crash Course
- Pluralsight: Deep Learning with Keras
- Udacity: Intro to TensorFlow for Deep Learning
- Datacamp: Introduction to Deep Learning with PyTorch
- [Deeplizard: Neural Network Programming - Deep Learning with PyTorch](https://www.youtube.com/playlist?list=PLZbbT5o_s2 rfNyHZsM6ufI0iZENK9 gG)
- Udacity: Intro to Deep Learning with PyTorch
- [Deeplizard: TensorFlow.js series](https://www.youtube.com/watch?v=HEQDRWMK6yY&list=PLZbbT5o_s2 r83l8w44N_g3pygvajLrJ-)
- AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
- AWS: Build a Te t 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 Le
- 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 E am Basics
- AWS: Neural Machine Translation with Sockeye
- AWS: Process Model: CRISP-DM on the AWS Stack
- AWS: Satellite Image Classification in SageMaker
- [ed : Amazon SageMaker: Simplifying Machine Learning Application Development](https://www.ed .org/course/simplifying-machine-learning-app-development-with-amazon-sagemaker)
- Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Datacamp: Model Validation in Python
- Datacamp: Hyperparameter Tuning in Python
- Google: Testing and Debugging
- Troubleshooting Deep Neural Networks
- Acloudguru: AWS Certified Machine Learning - Specialty
- Acloudguru: AWS Certified Developer - Associate
- Acloudguru: AWS Certification Preparation Guide
- AWS: E am Readiness: AWS Certified Developer – Associate
- [AWS: Thirty Serverless Architectures in 30 Minutes](https://www.youtube.com/watch?v= Jcm9V2jagc)
- Codecademy: Deploy a Website
- Datacamp: Parallel Computing with Dask
- Pluralsight: Docker and Containers: The Big Picture
- Pluralsight: Docker and Kubernetes: The Big Picture
- Pluralsight: AWS Developer: The Big Picture
- Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)
- Pluralsight: AWS VPC Operations
- 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 Linu Web Servers](https://www.udacity.com/course/configuring-linu -web-servers--ud299)
- Udacity: Scalable Microservices with Kubernetes
- Udemy: AWS Concepts
- Udemy: Serverless Concepts
- Udemy: AWS Certified Developer - Associate 2018
- Datacamp: Customer Analytics & A/B Testing in Python
- Udacity: A/B Testing
- Udacity: A/B Testing for Business Analysts
- Datacamp: Unit Testing for Data Science in Python
- Pluralsight: Test-driven Development: The Big Picture
- [Test Driven Development with Python](http://chimera.labs.oreilly.com/books/1234000000754/inde .html)
- Thoughtbot: Fundamentals of TDD
- Treehouse: Python Testing
- Udacity: Software Analysis & Testing
- Udacity: Software Testing
- Udacity: Software Debugging
- Book: A Byte of Python
- Book: Learn Python The Hard way
- Book: Python 201
- [Book: Python Anti-Patterns](https://docs.quantifiedcode.com/python-anti-patterns/inde .html)
- Book: Real Python
- [Book: The Python 3 Standard Library By E ample](https://doughellmann.com/blog/the-python-3-standard-library-by-e ample)
- [Book: Writing Idiomatic Python 3](https://www.amazon.com/Writing-Idiomatic-Python-Jeff-Knupp-ebook/dp/B00B5V MRG)
- Codecademy: Learn Python
- Cognitiveclass.ai: Python for Data Science
- Datacamp: Python for R Users
- Datacamp: Python for Spreadsheet Users
- Datacamp: Python for MATLAB Users
- Datacamp: Importing Data in Python (Part 1)
- Datacamp: Intermediate Python for Data Science
- [Datacamp: Python Data Science Toolbo (Part 1)](https://www.datacamp.com/courses/python-data-science-toolbo -part-1)
- [Datacamp: Python Data Science Toolbo (Part 2)](https://www.datacamp.com/courses/python-data-science-toolbo -part-2)
- Datacamp: Intro to Python for Finance
- Datacamp: Writing Efficient Python Code
- Datacamp: Writing Functions in Python
- Datacamp: Working with Dates and Times in Python
- [ed : Introduction to Python for Data Science](https://www.ed .org/course/introduction-python-data-science-microsoft-dat208 -7)
- [ed : Programming with Python for Data Science](https://www.ed .org/course/programming-python-data-science-microsoft-dat210 -5)
- Google's Python Class
- 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 E pressions](https://thoughtbot.com/upcase/regular-e pressions)
- TheNewBoston: Python Programming Tutorials
- Udacity: Introduction to Python Programming
- Udacity: Programming Foundations with Python
- Udacity: What is Programming?
- Codecademy: Learn Git
- Code School: Git Real
- Datacamp: Introduction to Git for Data Science
- Learn enough git to be dangerous
- Thoughtbot: Mastering Git
- Udacity: GitHub & Collaboration
- Udacity: How to Use Git and GitHub
- Udacity: Version Control with Git
- Book: Refactoring UI
- Codecademy: HTML Projects
- Codecademy: Learn HTML
- Codecademy: Learn Color Design
- Codecademy: Learn SASS
- Codecademy: Make a website
- Codecademy: Learn ReactJS: Part I
- Codecademy: Learn ReactJS: Part II
- Codecademy: Learn JavaScript
- Codecademy: Jquery Track
- Codecademy: Learn Ruby
- Code School: Fundamentals of Design
- Code School: Blasting Off with Bootstrap
- Django Best Practices
- (ES6) - Beau teaches JavaScript
- [Pluralsight: U Fundamentals](https://www.pluralsight.com/courses/u -fundamentals-2426)
- Pluralsight: HTML, CSS, and JavaScript: The Big Picture
- Pluralsight: CSS Positioning
- Pluralsight: Introduction to CSS
- Pluralsight: CSS: Specificity, the Bo Model, and Best Practices
- Pluralsight: CSS: Using Fle bo for Layout
- Pluralsight: Using The Chrome Developer Tools
- Thoughtbot: Design for Developers
- Treehouse: HTML
- Treehouse: Javascript Booleans
- Udacity: Authentication & Authorization: OAuth
- Udacity: Designing RESTful APIs
- Udacity: Client-Server Communication
- Udacity: ES6 - JavaScript Improved
- Udacity: Intro to Javascript
- Udacity: Object Oriented JS 1
- Udacity: Object Oriented JS 2
- Udemy: Understanding Typescript
- Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPI
- Youtube: FastAPI from the ground up
- Codecademy: Big O
- [Crashcourse: Computer Science](https://www.youtube.com/playlist?list=PL8dPuuaLj tNlUrzyH5r6jN9ulIgZBpdo)
- 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
- Launch School: Agile Planning
- Pluralsight: Product Owner Fundamentals
- Pluralsight: Scrum Master Fundamentals - Foundations
- Pluralsight: Security Awareness: Basic Concepts and Terminology
- Pluralsight: Secure Software Development
- 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
- [Learn Enough Te t Editor to Be Dangerous](https://www.learnenough.com/te t-editor-tutorial)
- Mastering Pycharm
- Google: Technical Writing
- Book: Emotional Intelligence
- Book: How to Win Friends & Influence People
- Book: Influence: The Psychology of Persuasion
- Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't
- Book: Multipliers: How the Best Leaders Make Everyone Smarter
- Book: Soft Skills: The software developer's life manual
- Book: The New One Minute Manager
- [Youtube: Building a psychologically safe workplace | Amy Edmondson | TED HGSE](https://youtu.be/LhoLuui9g 8)
- Datacamp: Preparing for Statistics Interview Questions in Python
- Datacamp: Preparing for Coding Interview Questions in Python
- Udacity: Optimize your GitHub
- Udacity: Strengthen Your LinkedIn Network & Brand
- Udacity: Data Science Interview Prep
- 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
- Book: Atomic Habits
- Book: Deep Work
- Book: Outliers: The Story of Success
- Book: Rich Dad Poor Dad
- Book: The Power of Broke
- [Book: The 10 Rule: The Only Difference Between Success and Failure](https://www.amazon.com/10 -Rule-Difference-Between-Success/dp/0470627603)
- Book: The Millionaire Fastlane
- Book: The Subtle Art of Not Giving a F**k