Python is one of the most powerful languages for machine learning and data science. The tutorials below walk you through building real projects — from training linear regression models and fruit classifiers with scikit-learn, to constructing convolutional neural networks with Keras and TensorFlow, to building NLP pipelines and generative models with PyTorch. Along the way you’ll work with core libraries like numpy, pandas, scikit-learn, TensorFlow, Keras, and PyTorch across a wide range of domains including sentiment analysis, recommendation systems, fake-news detection, image recognition, and deep learning.Documentation Index
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These tutorials use real Python ML libraries. You’ll need Python 3.x and typically numpy, pandas, scikit-learn, and either Keras/TensorFlow or PyTorch installed.
Data Science
The video series below takes a hands-on approach, walking through complete data science projects from scratch.- Learn Python For Data Science by Doing Several Projects (video series):
Machine Learning
These tutorials cover the full range of supervised and unsupervised ML techniques, from simple linear models to neural networks and NLP.- Write Linear Regression From Scratch in Python (video)
- Step-By-Step Machine Learning In Python
- Predict Quality Of Wine
- Solving A Fruits Classification Problem
- Learn Unsupervised Learning with Python
- Build Your Own Neural Net from Scratch in Python
- Linear Regression in Python without sklearn
- Multivariate Linear Regression without sklearn
- Music Recommender using KNN
- Find Similar Quora Questions:
- Detecting Fake News with Python and Machine Learning
Deep Learning with Keras / TensorFlow
Build image classifiers, caption generators, face recognition pipelines, and NLP models using Keras and TensorFlow.- Using Convolutional Neural Nets to Detect Facial Keypoints
- Generate an Average Face using Python and OpenCV
- Break A Captcha System using CNNs
- Use pre-trained Inception model to provide image predictions
- Create your first CNN
- Build A Facial Recognition Pipeline
- Build An Image Caption Generator
- Make your Own Face Recognition System
- Train a Language Detection AI in 20 minutes
- Object Detection With Neural Networks
- Learn Twitter Sentiment Analysis:
- Part I - Data Cleaning
- Part II - EDA, Data Visualisation
- Part III - Zipf’s Law, Data Visualisation
- Part IV - Feature Extraction (count vectoriser)
- Part V - Feature Extraction (Tfidf vectoriser)
- Part VI - Doc2Vec
- Part VII - Phrase Modeling + Doc2Vec
- Part VIII - Dimensionality Reduction
- Part IX - Neural Nets with Tfidf vectors
- Part X - Neural Nets with word2vec/doc2vec
- Part XI - CNN with Word2Vec
- Use Transfer Learning for custom image classification
- Learn to Code a simple Neural Network in 11 lines of Python
- Build a Neural Network using Gradient Descent Approach
- Train a Keras Model To Generate Colors
- Get Started with Keras on a Custom Dataset
- Use EigenFaces and FisherFaces on Faces94 dataset
- Kaggle MNIST Digit Recognizer Tutorial
- Fashion MNIST tutorial with tf.keras
- CNN using Keras to automatically classify root health
- Keras vs Tensorflow
- Deep Learning and Medical Image Analysis for Malaria Detection
- Transfer Learning for Image Classification using Keras
- Code a Smile Classifier using CNNs in Python
- Natural Language Processing using scikit-learn
- Code a Taylor Swift Lyrics Generator