Forked from ZuzooVn/machine-learning-for-software-engineers.
Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.
This short section were prerequisites/interesting info I wanted to learn before getting started on the daily plan.
- What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?
- Learning How to Learn
- Don’t Break The Chain
- How to learn on your own
- A Visual Introduction to Machine Learning
- 👍 A Visual and Interactive Guide to the Basics of Neural Networks
- A Gentle Guide to Machine Learning
- Introduction to Machine Learning for Developers
- Machine Learning basics for a newbie
- How do you explain Machine Learning and Data Mining to non Computer Science people?
- Machine Learning: Under the hood. Blog post explains the principles of machine learning in layman terms. Simple and clear
- What is machine learning, and how does it work?
- Deep Learning - A Non-Technical Introduction
- The Machine Learning Mastery Method
- Machine Learning for Programmers
- Applied Machine Learning with Machine Learning Mastery
- Python Machine Learning Mini-Course
- Machine Learning Algorithms Mini-Course (Same as e-mail series sent after registration)
- 👍 Machine Learning is Fun!
- 👍 Part 2: Using Machine Learning to generate Super Mario Maker levels
- 👍 Part 3: Deep Learning and Convolutional Neural Networks
- 👍 Part 4: Modern Face Recognition with Deep Learning
- 👍 Part 5: Language Translation with Deep Learning and the Magic of Sequences
- 👍 Part 6: How to do Speech Recognition with Deep Learning
- Overview, goals, learning types, and algorithms
- Data selection, preparation, and modeling
- Model evaluation, validation, complexity, and improvement
- Model performance and error analysis
- Unsupervised learning, related fields, and machine learning in practice
- Part 1: Machine Learning Crash Course
- Part 2: Machine Learning Crash Course
- Machine Learning in a Week
- Machine Learning in a Year
- How I wrote my first Machine Learning program in 3 days
- Learning Path : Your mentor to become a machine learning expert
- You Too Can Become a Machine Learning Rock Star! No PhD
- How to become a Data Scientist in 6 months: A hacker’s approach to career planning
- 5 Skills You Need to Become a Machine Learning Engineer
- Are you a self-taught machine learning engineer? If yes, how did you do it & how long did it take you?
- How can one become a good machine learning engineer?
- A Learning Sabbatical focused on Machine Learning
- 👎 10 Machine Learning Algorithms Explained to an ‘Army Soldier’
- 10 Machine Learning Terms Explained in Simple English
- The 10 Algorithms Machine Learning Engineers Need to Know
- A Tour of Machine Learning Algorithms
- Top 10 data mining algorithms in plain English
- Comparing supervised learning algorithms (Good comparison matrix)
- Machine Learning Algorithms: A collection of minimal and clean implementations of machine learning algorithms (Code examples in Python)
- ML From Scratch
- Data Smart: Using Data Science to Transform Information into Insight 1st Edition
- Data Science for Business: What you need to know about data mining and data analytic-thinking
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
- 🏃 Python Machine Learning
- Machine Learning for Hackers
- Programming Collective Intelligence: Building Smart Web 2.0 Applications
- Machine Learning: An Algorithmic Perspective, Second Edition
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Data Mining: Practical Machine Learning Tools and Techniques, Third Edition
- Teaching material
- Machine Learning in Action
- Reactive Machine Learning Systems(MEAP)
- An Introduction to Statistical Learning
- Building Machine Learning Systems with Python
- Learning scikit-learn: Machine Learning in Python
- Probabilistic Programming & Bayesian Methods for Hackers
- Probabilistic Graphical Models: Principles and Techniques
- Machine Learning: Hands-On for Developers and Technical Professionals
- Learning from Data
- Reinforcement Learning: An Introduction (2nd Edition)
- Machine Learning with TensorFlow(MEAP)
- Learn Python stack: scipy, numpy, pandas, scikit-learn, jupyter, matplotlib/seaborn.
- Learn machine learning tools: XGBoost, Scikit-learn, Keras, Vowpal Wabbit.
- Do data science competitions: Kaggle, DrivenData, TopCoder, Numerai.
- Take these courses: https://www.coursera.org/learn/machine-learning, http://work.caltech.edu/telecourse.html
- Work on soft skills: Business, management, visualization, reporting.
- Do at least one real-life data science project: Open Data, Journalism, Pet project.
- Contribute to community: Create wrappers, open issues/pull reqs, write tutorials, write about projects.
- Read: FastML, /r/MachineLearning, Kaggle Forums, Arxiv Sanity Preserver.
- Implement: Recent papers, older algorithms, winning solutions.
Note: As a software engineer you have a major advantage for applied ML: You know how to code. AI is just Advanced Informatics. If you want to become a machine learning researcher... skip all this and start from scratch: a PhD. Else: Learn by doing. Only those who got burned by overfit, will know how to avoid it next time.
- 🔖 Kaggle
- 🔖 ImageNet
- Kaggle Competitions: How and where to begin?
- How a Beginner Used Small Projects To Get Started in Machine Learning and Compete on Kaggle
- Master Kaggle By Competing Consistently
- 👍 Tensorflow and deep learning - without at PhD (Material/Blog, first 103 minutes builds up a convolutional neural network for handwritten digit classification while the rest focuses on RNN, but much less deep; watch at 130-150% speed; the guy is a bit strange, but if you follow along, you learn interessting tricks how to improve your accuracy)
- Tensorflow and deep learning - without at PhD (Short edition of above talk without batch normalization and RNNs)
- Machine Learning for Hackers (Actually, funny knowledgeable young guy, though deliberately superficial, which seems to be his (chosen) "format" for his series nonetheless)
- Fresh Machine Learning (The very same guy from above)
- Machine Learning Recipes with Josh Gordon (Google guy)
- Everything You Need to know about Machine Learning in 30 Minutes or Less (Fairly outdated by 4 years)
- A Friendly Introduction to Machine Learning
- Nuts and Bolts of Applying Deep Learning - Andrew Ng
- BigML Webinar
- mathematicalmonk's Machine Learning tutorials (Very exhaustive collection of brief sessions (160) on ML algorithms/concepts, but fairly outdated by 6 years)
- My playlist – Top YouTube Videos on Machine Learning, Neural Network & Deep Learning
- 16 New Must Watch Tutorials, Courses on Machine Learning
- 21 Deep Learning Videos, Tutorials & Courses on Youtube from 2016
- 30 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016
- Learning To See (Well done brief series on computer vision that actually only shows that it's hard, but doesn't show how to do it)
- 🏃 Practical Deep Learning For Coders
- Practical Machine Learning Tutorial with Python (Multi-part tutorial on ML using Python)
- DeepLearning.TV (Multi-part comic-like tutorial on ML in general)
- Machine learning in Python with scikit-learn
- Udacity
- Coursera
- Andrew Ng Machine Learning (Review, Roadmap, Videos Only; Last updated 2 years ago)
- Machine Learning Foundations: A Case Study Approach
- Neural Networks for Machine Learning
- Stanford
- Oxford
- Cambridge
- Sherbrooke (Last updated 4 years ago)
- A Guide to Deep Learning by YN2 (Very comprehensive list of links on ML)
- Learning Machine Learning: A beginner's journey
- 👍 A Step by Step Backpropagation Example
- 👎 Learn Machine Learning in a Single Month
- The Non-Technical Guide to Machine Learning & Artificial Intelligence
- Machine Learning for Software Engineers on Hacker News
- Machine Learning for Developers
- Machine Learning Advice for Developers
- Machine Learning For Complete Beginners (Nice live, though somewhat clunky, initial analysis of the Titanic data set using a Jupyter notebook)
- Getting Started with Machine Learning: For absolute beginners and fifth graders
- How to Learn Machine Learning: The Self-Starter Way
- Machine Learning Self-study Resources
- 5 Tasty Python Web Scraping Libraries
- Data Science Ipython Notebooks
- Level-Up Your Machine Learning
- An Honest Guide to Machine Learning
- Enough Machine Learning to Make Hacker News Readable Again
- 👍 Dive into Machine Learning (Excellent collection)
- {Machine, Deep} Learning for software engineers
- Python, Machine Learning, and Language Wars
- Deep Learning For Beginners
- Top Machine Learning Books
- Comprehensive list of data science resources
- DigitalMind's Artificial Intelligence resources
- Awesome Machine Learning
- CreativeAi's Machine Learning
- Most Cited Deep Learning Papers
- Libraries
- Keras (Beautiful interface to Google Tensorflow and grand-daddy Theano)
- TensorFlow (Similar to Theano, huge ML library, but supporting more than just DL, still in its infancy)
- Theano (Evaluate mathematical functions blazingly fast - also on GPUs)
- SciKit-Learn (ML algorithms for preprocessing, model selection, and of course classification, regression, clustering,...)
- Pandas (Vastly duplicative of NumPy, but handy import/export functions)
- NumPy (Everything vectors and matrices)
- SciPy (Integration, optimization, interpolation, transformation, and statistics functions on top of NumPy)
- SymPy (Do all sorts of symbolic computations like Wolfram Alpha does)
- MatPlotLib (Visualize "stuff" like NumPy vectors and matrices)
- Gephi (Visualize large networks graphs)
- Flipboard Topics
- Medium Topics
- Monthly top 10 articles
- Machine Learning
- Algorithms
- Halite: A.I. Coding Game
- Vindinium: A.I. Programming Challenge
- General Video Game AI Competition
- Angry Birds AI Competition
- The AI Games
- Fighting Game AI Competition
- CodeCup
- Student StarCraft AI Tournament
- AIIDE StarCraft AI Competition
- CIG StarCraft AI Competition
- CodinGame - AI Bot Games
- tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence
- tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning
- cmusatyalab/openface: Face recognition with deep neural networks.
- tensorflow/models/syntaxnet: Neural Models of Syntax.
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Quora
-
Reddit
- Neural Information Processing Systems (NIPS)
- IEEE Conference on Computational Intelligence and Games (CIG)
- IEEE International Conference on Machine Learning and Applications (ICMLA)
- International Conference on Machine Learning (ICML)
- How To Prepare For A Machine Learning Interview
- 40 Interview Questions asked at Startups in Machine Learning / Data Science
- 21 Must-Know Data Science Interview Questions and Answers
- Top 50 Machine learning Interview questions & Answers
- Machine Learning Engineer interview questions
- Popular Machine Learning Interview Questions
- What are some common Machine Learning interview questions?
- What are the best interview questions to evaluate a machine learning researcher?
- Collection of Machine Learning Interview Questions
- 121 Essential Machine Learning Questions & Answers