Master Python Machine Learning: A Step‑by‑Step 0‑to‑100 Guide

Python is currently the most popular language for machine learning, and abundant resources are available online. This tutorial aims to help absolute beginners start from zero and become knowledgeable practitioners using only free materials.

Assumed non‑expert areas:

Machine Learning

Python

Any Python machine‑learning, scientific‑computing, or data‑analysis library

Having a basic understanding of the first two topics is helpful but not required; a brief review at the early stage suffices.

Fundamentals

Step 1: Basic Python Skills

Installing Python is the first step. Because we will later use scientific‑computing and machine‑learning packages, the tutorial recommends installing Anaconda, an industrial‑grade Python distribution that includes NumPy, scikit‑learn, Matplotlib, and the IPython Notebook. Python 2.7 is suggested.

Free online books for beginners:

Learn Python the Hard Way – https://learnpythonthehardway.org/book/

If you already have programming experience but are new to Python, consider these courses:

Google Developers Python Course – http://suo.im/toMzq

Python Scientific Computing Intro (UCSB) – http://suo.im/2cXycM

For a 30‑minute quick start:

Learn X in Y minutes (Python) – http://suo.im/zm6qX

Experienced programmers can skip this step, but the official Python documentation remains useful – https://www.python.org/doc/

Step 2: Machine Learning Basics

Zachary Lipton of KDnuggets notes that many standards exist for evaluating a “data scientist,” reflecting the diversity of machine‑learning tasks. Deep theoretical understanding is not required for practical work.

Andrew Ng’s Coursera Machine Learning course is highly recommended – http://suo.im/2o1uD. Unofficial class notes can be found at – http://www.holehouse.org/mlclass/.

Additional courses include Tom Mitchell’s lectures – http://suo.im/497arw.

Step 3: Overview of Scientific Python Packages

NumPy – http://www.numpy.org/

Pandas – http://pandas.pydata.org/

Matplotlib – http://matplotlib.org/

scikit‑learn – http://scikit-learn.org/stable/

Learning material:

SciPy Lecture Notes – http://www.scipy-lectures.org/

10 Minutes to Pandas – http://suo.im/4an6gY

Step 4: Using Python for Machine Learning

Readiness checklist:

Python – ready

Machine‑learning materials – ready

NumPy – ready

Pandas – ready

Matplotlib – ready

Start with the scikit‑learn library.

Most tutorials and exercises use the IPython (Jupyter) Notebook – a convenient interactive environment. Overview of IPython Notebook – http://cs231n.github.io/ipython-tutorial/.

Introductory scikit‑learn article – http://suo.im/3bMdEd.

Random Forest example (Kaggle Titanic) – http://suo.im/1o7ofe.

Step 5: Basic Algorithms in Python

K‑means clustering – http://suo.im/40R8zf

Decision Trees – http://thegrimmscientist.com/tutorial-decision-trees/

Linear Regression – http://suo.im/3EV4Qn

Logistic Regression – http://suo.im/S2beL

Step 6: Advanced Algorithms

Support Vector Machines – http://suo.im/2iZLLa

Random Forest (Kaggle Titanic) – http://suo.im/1o7ofe

PCA (dimensionality reduction) – http://suo.im/2k5y2E

Step 7: Deep Learning with Python

Deep learning builds on neural networks and has seen rapid progress in recent years. Two contemporary Python deep‑learning libraries are highlighted.

Theano – http://deeplearning.net/software/theano/ (tutorial – http://suo.im/1mPGHe).

Caffe – http://caffe.berkeleyvision.org/ (DeepDream example – http://suo.im/2cUSXS).

Additional deep‑learning resources:

Neural Networks and Deep Learning by Michael Nielsen – http://neuralnetworksanddeeplearning.com/

TensorFlow tutorials – RNN: http://suo.im/2gtkze, CNN: http://suo.im/g8Lbg

Further reading on key terms and steps:

Machine‑learning key terms – http://suo.im/2URQGm

7 steps to understand deep learning – http://suo.im/3QmEfV