This repository is intended to provide a complete and organic learning path to getting started with Machine Learning. You will understand both theory and be able to apply it in practice, with hands-on project.
It does not require any previous knowledge, but being confident with programming and high school math is necessary to understand and implement Machine Learning concepts.
I strongly recommend to buy this phenomenal book: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by OreillY, which inspired me and has driven most of the organization and hierarchy of the content listed above.
A part from this, every content listed here is open source and free, most of that coming from the world-renowned universities and open source associations.
When we learn something new, especially if wide and complex, is necessary to avoid confusion, so I tried to create the next steps of the path preferring contents from the same context and authors, when possible. When not possible, I collected both theory and examples, further some pointers to resources like "best practices for _______".
- Python
- Jupyter Notebook
- The Math you need
- The Machine Learning landscape
- Why Scikit-Learn?
- End-to-End Machine Learning project
- Linear Regression
- Classification
- Training models
- Support Vector Machines
- Decision Trees
- Ensemble Learning and Random Forest
- Wrapping up and looking forward
- Why TensorFlow?
- Up and Running with TensorFlow
- ANN - Artificial Neural Networks
- CNN - Convolutional Neural Networks
- RNN - Recurrent Neural Networks
- Training Networks: Best practices
- AutoEncoders
- Reinforcement Learning
- Next steps
- Machine Learning Projects
- Data Science Tools
- Blogs / Youtube Channels / Websites worth taking a look!
So let's get started!
According to Sun Tzu:
If you don't know Python, learn it yesterday!
Python is one of the most used and loved programming languages, and it's necessary to get things done in the Machine Learning field. Like most of the frameworks of the bigger Data Science field, TensorFlow is married with Python and Scikit-Learn is written in Python.
First, let's install Python 3 on your machine!
We are ready to start our journey!
If you don't know the basics of Python, just start from here.
Else if you know the syntax and you want to have a more solid Python background (recommended) take this Intermediate Python Course from here.
If you are looking for tons of exercises to get your hands dirty and get experience with Python, check here and here.
Once you're familiar with Python, take a look at Numpy, an important module for math operations, that allows you to import in Python the Tensor data type, which is the most used in ML (especially when dealing with Neural Nets). It's not a matrix! This is an awesome Numpy Tutorial.
I also recommend you to install Pycharm Community Edition, a complete IDE for Python development, and set a new Python virtual environment for our experiments.
Directly from here: "The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more." Working with data means -> a lot of expriments. And to document experiments, and organize them in a valuable way to get insights, you definitely need to use Jupyter Notebook during your journey. Why?
Who tells that the math behind Machine Learning is hard... it's not so wrong! But you have to consider that every time you're going to use it, it will be handled by the machine for you! So, the important is to grasp the main math concepts and recognize limits and applications of those. No one is going to ask you to calculate a gradient by hand! So, even if you are not familiar with these concepts, check them, because they are the reason behind everything.
With these three resources, you'll get out the most of what you really need to understand things deeply.
A top course about linear algebra is here.
Integrate with basic probabilities and statistic concepts here.
The most of the remaining math you need here.
Directly from the book cited earlier, this is the most concise and illuminating overview of what is and when you need machine learning. Let's stop use buzzwords! Check it here.
To install Scikit-Learn
python pip install -U scikit-learn
If you encounter some problems, it may be because you don't have the last version of pip. So n the same folder run:
python -m pip install --upgrade pip
Scikit-Learn one of the most complete, mature and well-documented library for Machine Learning tasks. It comes out-of-the-box with powerful and advanced models and offers facility functions for the data science process. We'll learn and use other modules along the road, for a quick usage just look at their official documentation.
For a first taste, i suggest you to go through this Kaggle notebook, which is the most classic example of ML task. The goal is trying to predict if a Titanic passenger would have been most likely to survive or not. Many things will be unclear for now, but don't worry, they will be all explained comprehensively later. Is nice to get the picture of the "applied" project, going through the classical steps of the applied Machine Learning (problem framing, data exploration, question formulation...).
The notebook is on Kaggle, the go-to platform for ML and general Data Science projects, which provides a lot of free datasets and offers interesting challenges and ML model experiments.
This is the notebook: Read it, trying to get the big picture of the process, because some details, functions and code will be clearer later.
This is the simplest form of Machine Learning, and the starting point for everyone interested in predicting outcomes from a dataset. Check here the theoretical lesson from Andrew NG and then go through these examples, from the simplest to the most complete. This is the math behind Linear Regression.
Classification is one of the most important ML tasks, and it consists of predicting an outcome given an input, classifying it among differente possibilities. For example, given handwritten numbers, guess what the number is, with the lowest error rate possible. The simplest case is binary classification (Yes or No, Survived or Not Survived), have a look here. Check here a brief explanation of the theory of logistic regression algorithm for classification, and check here for a deeper comprehension (using the Titanic dataset). You can use a lot of different ML models to classify things, even neural networks! For now, just take a look here, where you see an example of comparison among different models accuracy and recall. Here you have an article about the metrics used to evaluate your classifiers.
Here i grouped some of the techniques used in ML tasks to train the models. In this Google Crash Course you find:
This is another classical algorithm to create ML models. Here you have the explanation of the theory, and here a more pratical approach. Check both. Here is a very good explanation + practice application in Scikit-Learn.
Decision Trees are one of the most simple but effective idea behind predicting outcomes, and they're used in many ways (i.e. Random Forest). Check here and go through the playlist to get a theoretical overview of Decision Trees (ID3). Here you have the pratical application of ID3. Here you have a some end-to-end examples, with Scikit-Learn:
The idea of Ensemble Learning is to leverage all the different features, pro and cons of several ML models to obtain a group of "voters" that, for each prediction, gives you the most likely outcome, voted by different classifiers (SVM, ID3, maybe Logistic Regression). Here you find the basics of the ensemble learning approach, and here you find the most classic of them, the Random Forest. Altough the idea is simple, this ensemble model came up really effective tackling even some "hard" classification problems, or with a lot of data.
Here you get a complete overview of the best practices for ensemble learning, and here you find an example of Random Forest with Scikit-Learn. Both link come with a bunch of useful techniques touse in practice.
Now, if you followed all the steps and explored all the resources i posted, you're likely to be mode confident with Machine Learning and have a general idea of the things. Of course you need to explore and learn more, because this field is changing and enhancing techniques and approaches day-by-day! All the algorithm we've seen are widely used in the Data Science and Analytics field, but there are some complex tasks where they fail or give really poor performances. Now we are ready to fall down in the deep rabbit hole, trying to understand how Neural Network and in general Deep Learning can help tackling big problem with millions of parameters and variables. Why use Deep Learning over classical ML algorithms?
In this section we'll follow a track that will bring us to zero knowledge of neural network to fully understand them, thanks to the Stanford University Deep Learning course and some tutorials i've searched over the internet. Some of them come from Google, other from Stanford or Cambridge university, and you will learn to leverage neural networks (ANN, CNN, RNN) for several kind of ML tasks. These are some use cases of using TensorFlow for ML tasks.
The theory and the applications of the Neural Networks are not too easy to get at a first look. Because of that, you'll need to pass again through tutorials and videos, to ensure a fully comprehension of the coming topics. Because of that, I spent a decent amount of time trying to understand (reading paths like this, articles, offical forums, related subreddits) which was the most effective way to deeply learn the concepts, formulas, tradeoffs... I came up with this approach, but you can tweak it as you prefer, because every brain is different.
After taking the TensorFlow section 3 phases iterative cycle:
- 1 Get an idea of the main concepts through an entire pass of this Stanford course, don't care too much on math explanations, focus on the what and why
- 2 Deeply explore one topic at time, with theory + tutorials + examples (e.g. RNN theory + RNN tutorials + RNN examples) with the links and resources of the topic section of the guide.
- 3 After iterating the 2 phase for each topic, walk again through the entire Stanford course. This time you can fully understand all the formulas, connecting them and catching also the "math flow" of the course.
This iterative process (1-2-2-2-2.....-3) can be repeated may times as you want, and will probably construct in your mind a nice general schema of the things. In each complete iteration you can drop one or more topics, and focus on the ones that are more interesting to you or not so clear.
In each section i've put content for the first time you arrive there (during the first complete iteration), and some content for next time you arrive there (after the 3 phase).
The structure follows the track proposed by the Stanford awesome course. You find the slides here.
This is an alternative course from MIT, more or less the same contents. It's worth watching it to compare and have a differente point of view on the things, besides listening 2X the best professors of the world exploring each topic.
This is the Book I refer to in each section.
Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. It uses Python to provide a convenient front-end API for building applications with the framework, while executing those applications in high-performance C++. TensorFlow is the de-facto standard for the major industry-sized company that need to implement Machine Learning algorithms. Is built for scaling, with really cool features to parallelize training over multiple GPU's or devices.
Assuming you have Python stored in the variable PATH, to install the Tensorflow library you just need to open a terminal inside you Python installation folder and run this command.
python pip install tensorflow
The first read i recommend you is this. The second thing to do is to follow this Introduction to TensorFlow directly from the awesome Google Education page. Again, some theoretical concepts might be unclear, but focus on how the TensorFlow library and process are conceived. This is a good resume of the latter. Another beginner tutorial from google. This is about the TensorFlow 2.0 update.
Now you're most likely familiar with TensorFlow as a tool, and it's time to understand how to use it to build large scale Neural Networks.
First look (in order):
- This video.
- This is your bible, understand it totally.
- This is a gem and read this from the authors.
- This is a really fast-talking guy implementing a Neural Network library from scratch, super useful to understand how is implemented the core of NN in Python. You can imagine that each existing framework is just an enormous expansione of this concept-library.
- This is a step-by-step backpropagation example with calculus.
Second pass:
Tips & Best practices: 1, 2, 3, 4, 5, 6, 7, 8.
First look (in order):
- Here is an awesome deep explanation.
- Here another super good one.
- Here is serious CNN tutorial with TensorFlow.
Second pass:
Tips & Best practices: 1, 2, 3, 4, 5, 6, 7, 8.
First look (in order):
- Here a gentle but detailed explanation.
- Here another interesting explanation.
- Here a video with a more pratical approach.
- Here a guide to implement RNN in TensorFlow.
- Here a 7 pages blog post regarding the TensorFlow implementation.
Second pass:
Tips & Best practices: 1, 2, 3, 4, 5, 6, 7.
First look (in order): I strongly recommend you to refer to this page from Stanford and go through all the Module 1 and 2. I put also here a list of the various topic to explore when talking about how to train NN in real life applications.
- Overfitting vs Underfitting: 1, 2, 3, 4, 5.
- Vanishing/Exploding Gradient: 1, 2, 3, 4, 5.
- Transfer Learning: 1, 2, 3, 4, 5.
- Faster Optimizers: 1, 2, 3, 4.
- Avoiding Overfitting through Regularization: 1, 2, 3, 4.
Second pass:
First look (in order):
- Here you find a first read.
- This is your second recommended read.
- This is a lecture from Andrew NG.
- I give also you some examples: 1, 2, 3, 4.
Second pass: AutoEncoders Chapter.
Tips & Best practices: 1, 2, 3, 4, 5.
First look (in order):
- Here you have an explanation video.
- This article is well explaining RL.
- Here is an interesting read.
- Some examples: 1, 2, 3, 4.
Second pass: The go-to guide. Paper with state of art RL architecture. Complete free book on RL.
Hey You. During the last few years i collected tons of articles, web apps, reddit thread, best practices, projects and repositories, and I want to share with you each single bit of information, trying to organize them by type of resource (blogs or projects ideas, and so on).
- Enormous and awesome collection of Data Science Cheat Sheets
- Infinite collection of actual Data Science / ML projects
- Infinite collection of tutorials and Ml projects in TensorFlow
- Other TensorFlow examples
- Enthought
- Siraj Raval
- 3Blue1Brown
- Microsoft
- TensorFlow Official channel
- Engineering Man
- The Tech Lead
- How to build a data science portfolio
- Distill blog
- Keras
- Paolo Galeone blog
- TensorFlow official blog
- KD Nuggets
- Incredible Graphic explanations
- A monster collection of Data related free course
- Machine Learning Map
- Data modeling for Business Intelligence
- Statistics explained
- Visualizing data - Turorials
- Fast.ai
- Open.ai
- Explained.ai
- Machine Learning Glossary
- Python ML Tutorials
- For Italian Learners!
- Immersive math
- DeepLizard
- Common Statistical Fallacies
Thanks to the great success of this guide, i've decided to expand it a lot and make more similar for different topics. Some of the extensions i'm addig here next weeks are:
- Unsupervised Learning
- Machine Learning mindset framework (how to think like a data scientist)
- Data processing and preparation
- Feature Selection
- Features Engineering
- Extending the parameters optimization section
- Add a section for the Keras Library
- Add a section for the TensorFlow 2.0
- Add a section to "How to deploy my model on AWS"
- Add a section to "How to deploy my model on Azure"
Later on, i'll do an entire guide on AWS, strongly linked with this one :)