Python Machine Learning By Example

(MACHINE-LEARN.AJ1)
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1

Introduction

  • Who this course is for
  • What this course covers
2

Getting Started with Machine Learning and Python

  • An introduction to machine learning
  • Knowing the prerequisites
  • Getting started with three types of machine learning
  • Digging into the core of machine learning
  • Data preprocessing and feature engineering
  • Combining models
  • Installing software and setting up
  • Summary
  • Exercises
3

Building a Movie Recommendation Engine with Naïve Bayes

  • Getting started with classification
  • Exploring Naïve Bayes
  • Implementing Naïve Bayes
  • Building a movie recommender with Naïve Bayes
  • Evaluating classification performance
  • Tuning models with cross-validation
  • Summary
  • Exercises
4

Predicting Online Ad Click-Through with Tree-Based Algorithms

  • A brief overview of ad click-through prediction
  • Getting started with two types of data – numerical and categorical
  • Exploring a decision tree from the root to the leaves
  • Implementing a decision tree from scratch
  • Implementing a decision tree with scikit-learn
  • Predicting ad click-through with a decision tree
  • Ensembling decision trees – random forests
  • Ensembling decision trees – gradient-boosted trees
  • Summary
  • Exercises
5

Predicting Online Ad Click-Through with Logistic Regression

  • Converting categorical features to numerical – one-hot encoding and ordinal encoding
  • Classifying data with logistic regression
  • Training a logistic regression model
  • Training on large datasets with online learning
  • Handling multiclass classification
  • Implementing logistic regression using TensorFlow
  • Summary
  • Exercises
6

Predicting Stock Prices with Regression Algorithms

  • What is regression?
  • Mining stock price data
  • Getting started with feature engineering
  • Estimating with linear regression
  • Estimating with decision tree regression
  • Implementing a regression forest
  • Evaluating regression performance
  • Predicting stock prices with the three regression algorithms
  • Summary
  • Exercises
7

Predicting Stock Prices with Artificial Neural Networks

  • Demystifying neural networks
  • Building neural networks
  • Picking the right activation functions
  • Preventing overfitting in neural networks
  • Predicting stock prices with neural networks
  • Summary
  • Exercises
8

Mining the 20 Newsgroups Dataset with Text Analysis Techniques

  • How computers understand language – NLP
  • Touring popular NLP libraries and picking up NLP basics
  • Getting the newsgroups data
  • Exploring the newsgroups data
  • Thinking about features for text data
  • Visualizing the newsgroups data with t-SNE
  • Summary
  • Exercises
9

Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

  • Learning without guidance – unsupervised learning
  • Getting started with k-means clustering
  • Clustering newsgroups dataset
  • Discovering underlying topics in newsgroups
  • Summary
  • Exercises
10

Recognizing Faces with Support Vector Machine

  • Finding the separating boundary with SVM
  • Classifying face images with SVM
  • Estimating with support vector regression
  • Summary
  • Exercises
11

Machine Learning Best Practices

  • Machine learning solution workflow
  • Best practices in the data preparation stage
  • Best practices in the training set generation stage
  • Best practices in the model training, evaluation, and selection stage
  • Best practices in the deployment and monitoring stage
  • Summary
  • Exercises
12

Categorizing Images of Clothing with Convolutional Neural Networks

  • Getting started with CNN building blocks
  • Architecting a CNN for classification
  • Exploring the clothing image dataset
  • Classifying clothing images with CNNs
  • Boosting the CNN classifier with data augmentation
  • Improving the clothing image classifier with data augmentation
  • Advancing the CNN classifier with transfer learning
  • Summary
  • Exercises
13

Making Predictions with Sequences Using Recurrent Neural Networks

  • Introducing sequential learning
  • Learning the RNN architecture by example
  • Training an RNN model
  • Overcoming long-term dependencies with LSTM
  • Analyzing movie review sentiment with RNNs
  • Revisiting stock price forecasting with LSTM
  • Writing your own War and Peace with RNNs
  • Summary
  • Exercises
14

Advancing Language Understanding and Generation with the Transformer Models

  • Understanding self-attention
  • Exploring the Transformer’s architecture
  • Improving sentiment analysis with BERT and Transformers
  • Generating text using GPT
  • Summary
  • Exercises
15

Building an Image Search Engine Using CLIP: a Multimodal Approach

  • Introducing the CLIP model
  • Getting started with the dataset
  • Finding images with words
  • Summary
  • Exercises
  • References
16

Making Decisions in Complex Environments with Reinforcement Learning

  • Setting up the working environment
  • Introducing OpenAI Gym and Gymnasium
  • Introducing reinforcement learning with examples
  • Solving the FrozenLake environment with dynamic programming
  • Performing Monte Carlo learning
  • Solving the Blackjack problem with the Q-learning algorithm
  • Summary
  • Exercises