Inspired by Machine Learning for Software Engineers by Google Interview University.
Translations: Brazilian Portuguese
- Sides of machine learning?
- Don't feel you aren't smart enough
- Prerequisite Knowledge
- Machine learning overview
- Machine learning mastery
- Machine learning is fun
- Machine learning: an in-depth, non-technical guide
- Stories and experiences
- Machine Learning Algorithms
- Deep Learning Resources
- Deep Learning To read
- Beginner Books
- Practical Books
- Kaggle knowledge competitions
- Video Series
- MOOC
- Resources
- Becoming an Open Source Contributor
- Podcasts
- Communities
There are two sides to machine learning:
- Practical Machine Learning: This is about queries databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
I think the best way for practice-focused methodology is something like 'practice — learning — practice', that means where students first come with some existing projects with problems and solutions (practice) to get familiar with traditional methods in the area and perhaps also with their methodology. After practicing with some elementary experiences, they can go into the books and study the underlying theory, which serves to guide their future advanced practice and will enhance their toolbox of solving practical problems. Studying theory also further improves their understanding on the elementary experiences, and will help them acquire advanced experiences more quickly.
It's a long plan. It's going to take me years. If you are familiar with a lot of this already it will take you a lot less time.
I get discouraged from books and courses that tell me as soon as I can that multivariate calculus, inferential statistics and linear algebra are prerequisites. I still don’t know how to get started…
- What if I’m Not Good at Mathematics
- 5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics
- How do I learn machine learning?
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
- Coding the Matrix torrent
- Coursera introduction to statistics 101
- [Linear Algebra essentials (https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
- Linear Algebra Guide
- Visual Interactive Guide To Basics - Neural Networks
- A Visual Introduction to Machine Learning
- A Gentle Guide to Machine Learning
- 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?
- Machine Learning Algorithms in pure python
- 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
- 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
- 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
- Understanding Bayes: A Look at the Likelihood
- Logistic Regression and Gradient Descent
- The Method of Maximum Likelihood for Simple Linear Regression
- Regression Estimation - Least Squares and Maximum Likelihood
- SVMs and Kernel Method
- 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
- Gradient Descent with Backpropagation
- 10 Machine Learning Algorithms Explained to an ‘Army Soldier’
- Top 10 data mining algorithms in plain English
- 10 Machine Learning Terms Explained in Simple English
- A Tour of Machine Learning Algorithms
- The 10 Algorithms Machine Learning Engineers Need to Know
- Comparing supervised learning algorithms
- i am trask
- Awesome Deep Learning Papers
- Word vectors intuition
- Understanding word2vec NN and how semantics are extracted
- word2vec explorable explanation
- Deep Learning Tensorflow Algorithms
- Visualizing and Understanding Convolutional Networks
- 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
- Neural Networks and Deep Learning
- Machine Learning for Hackers
- Python Machine Learning
- 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)
- Titanic Kaggle Challenge
- 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
- Machine Learning for Hackers
- Fresh Machine Learning
- Machine Learning Recipes with Josh Gordon
- Everything You Need to know about Machine Learning in 30 Minutes or Less
- Nuts and Bolts of Applying Deep Learning - Andrew Ng
- BigML Webinar
- mathematicalmonk's Machine Learning tutorials
- Machine learning in Python with scikit-learn
- My playlist – Top YouTube Videos on Machine Learning, Neural Network & Deep Learning
- 16 New Must Watch Tutorials, Courses on Machine Learning
- Practical Deep Learning For Coders, Part 1
- Udacity’s Intro to Machine Learning
- Udacity’s Supervised, Unsupervised & Reinforcement
- Machine Learning Foundations: A Case Study Approach
- Coursera’s Machine Learning
- Machine Learning Distilled
- BigML training
- Coursera’s Neural Networks for Machine Learning
- Taught by Geoffrey Hinton, a pioneer in the field of neural networks
- Machine Learning - CS - Oxford University
- Creative Applications of Deep Learning with TensorFlow
- Intro to Descriptive Statistics
- Intro to Inferential Statistics
- Deep Learning Weekly
- Learning Tensor Flow
- Machine Learning for Developers
- Machine Learning Advice for Developers
- Machine Learning For Complete Beginners
- Machine Learning Self-study Resources
- Level-Up Your Machine Learning
- Enough Machine Learning to Make Hacker News Readable Again
- Dive into Machine Learning
- Flipboard Topics
- Medium Topics
- Monthly top 10 articles
- Machine Learning
- Algorithms
- Comprehensive list of data science resources
- Machine Learning Summer Schools
- DigitalMind's Artificial Intelligence resources
- Awesome Machine Learning
- CreativeAi's Machine Learning
- AutoGrad - Gradient - Library
- 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.