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Python Machine Learning By Example

  Master Python Machine Learning by building real-world examples. Learn practical ML algorithms, deployment considerations, and best practices for robust solutions.

(MACHINE-LEARN.AJ1) / ISBN : 979-8-90059-033-2
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About This Course

This Python machine learning course cuts through the theory to deliver hands-on expertise. You'll tackle real-world problems, from building movie recommenders with Naïve Bayes to predicting stock prices using neural networks.

We'll dive into critical topics like data preprocessing, feature engineering, and evaluating model performance, exposing common pitfalls and limitations. Learn to implement decision trees, logistic regression, SVMs, and advanced deep learning architectures like CNNs and RNNs. Understand the trade-offs between model complexity and interpretability. This isn't about perfection; it's about building functional, robust machine learning solutions and understanding their practical constraints.

Skills You’ll Get

  • Implement and evaluate core machine learning algorithms like Naïve Bayes, Decision Trees, Logistic Regression, and SVMs for classification and regression tasks, understanding their underlying mechanics and practical limitations.
  • Develop and deploy deep learning models, including Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models for complex tasks like image classification, sentiment analysis, and text generation.
  • Apply essential data preprocessing, feature engineering, and model selection techniques to prepare datasets for machine learning, recognizing the impact of data quality on model performance and generalization.
  • Design and build end-to-end machine learning solutions, from data acquisition and model training to evaluation and deployment, adhering to best practices for maintainability and scalability, while acknowledging real-world deployment challenges.
Download Course Outline

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 the 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

1

Building a Movie Recommendation Engine with Naïve Bayes

  • Implementing Naïve Bayes
  • Building a Movie Recommender with Naïve Bayes
2

Predicting Online Ad Click-Through with Tree-Based Algorithms

  • Implementing a Decision Tree with scikit-learn
  • Predicting Ad Click-Through with a Decision Tree
3

Predicting Online Ad Click-Through with Logistic Regression

  • Training a Logistic Regression Model Using Gradient Descent
  • Predicting Ad Click-Through with Logistic Regression Using Gradient Descent
  • Training a Logistic Regression Model Using SGD
  • Performing Feature Selection Using L1 Regularization and Random Forest
  • Implementing Logistic Regression Using TensorFlow
4

Predicting Stock Prices with Regression Algorithms

  • Acquiring Data and Generating Features
  • Implementing Linear Regression with scikit-learn
  • Implementing Linear Regression with TensorFlow
  • Implementing Decision Tree Regression
  • Implementing a Regression Forest
5

Predicting Stock Prices with Artificial Neural Networks

  • Building a Neural Network
  • Predicting stock prices with the three regression algorithms
  • Predicting Stock Prices with Neural Networks
6

Mining the 20 Newsgroups Dataset with Text Analysis Techniques

  • Getting and Exploring the Newsgroup Data
  • Visualizing the Newsgroups Data with t-SNE
7

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

  • Implementing k-means from Scratch
  • Implementing k-means with scikit-learn
  • Clustering Newsgroups Data Using k-means
  • Discovering Underlying Topics in Newsgroups
8

Recognizing Faces with Support Vector Machine

  • Implementing SVR
  • Implementing SVM
  • Classifying Face Images with SVM
9

Machine Learning Best Practices

  • Handling Missing Data in Datasets
  • Extracting and Representing Features from Text Data
  • Selecting and Evaluating Features for Model Training
  • Saving, Loading, and Reusing Trained Models
10

Categorizing Images of Clothing with Convolutional Neural Networks

  • Exploring the clothing image dataset
  • Reducing Dimensionality for Improving Model Performance
  • Fitting the CNN model
  • Boosting the CNN classifier with data augmentation
  • Improving the clothing image classifier with data augmentation
11

Making Predictions with Sequences Using Recurrent Neural Networks

  • Analyzing and preprocessing the data

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  You should have a solid grasp of Python programming fundamentals, including data structures, functions, and basic object-oriented concepts. Familiarity with linear algebra and calculus is beneficial but not strictly required, as the course focuses on practical application.

  This course is heavily example-driven. While we introduce the necessary theoretical concepts for each algorithm, the primary focus is on hands-on implementation using Python and libraries like scikit-learn and TensorFlow/Keras. You'll build real-world projects from scratch.

  The course covers machine learning best practices, including stages like data preparation, model training, evaluation, and selection, which are crucial for deployment. While it doesn't delve into specific production infrastructure (e.g., AWS, Azure), it equips you with the knowledge to build robust, deployable models and understand the workflow.

  You'll be equipped to tackle a wide range of problems, including building recommendation engines, predicting ad click-through rates, forecasting stock prices, categorizing images, analyzing text sentiment, and even developing basic image search engines using advanced models like CLIP.

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