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Python Machine Learning By Example

  By mastering Machine Learning through a practical, example-driven approach, you bridge the gap between abstract data science and impactful software engineering.

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About This Course

Are you ready to move beyond "copy-pasting" code and truly leverage Machine Learning to solve complex, real-world problems? The role of the modern developer has shifted from writing logic to training it. This highly specialized course, Python Machine Learning By Example, moves you past dry mathematical theory and dives deep into the hands-on architecture of intelligent systems.

You will master the foundational models of Supervised Learning and Unsupervised Learning by working through actual datasets and industry use cases. You'll learn the professional art of Feature Engineering to prepare your data for battle and explore the high-level design of Neural Networks. Whether you are aiming to deploy high-stakes Predictive Modeling for financial forecasting, design recommendation engines, or lead a team in developing computer vision solutions, this program provides the practical, code-first knowledge to launch advanced AI from prototype to production.

Skills You’ll Get

  • Hands-on Algorithmic Foundations: Master the art and science of Machine Learning by implementing regression, classification, and clustering models directly in Python, ensuring you can match the right algorithm to any business challenge.
  • Advanced Feature Engineering: Dive into the most critical part of the pipeline—data preparation. You will apply sophisticated Feature Engineering techniques to clean, transform, and optimize raw data for maximum model performance.
  • Supervised & Unsupervised Mastery: Learn by doing as you build Supervised Learning models for structured predictions and utilize Unsupervised Learning to discover hidden patterns in unlabelled data.
  • Neural Networks & Predictive Logic: Explore the future of deep learning by architecting Neural Networks. You will learn to fine-tune hyperparameters and deploy Predictive Modeling systems that can handle complex, non-linear data at scale.
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 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

Any questions?
Check out the FAQs

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This program is ideal for software developers, data scientists, and engineers who want to move away from purely theoretical AI and gain a practical, project-based understanding of how to implement Machine Learning using Python.

  Not at all. While we cover the necessary logic, our "By Example" philosophy prioritizes code and implementation. We show you how the math works through the lens of Feature Engineering and model tuning rather than just abstract formulas.

  Yes. The course includes dedicated modules on Neural Networks, taking you from basic perceptrons to more complex architectures, ensuring you can build and evaluate deep learning models effectively.

  The course is heavily focused on practice. You will work through uCertify’s hands-on labs, building everything from a basic Predictive Modeling tool to sophisticated Unsupervised Learning clusters, giving you a production-ready skill set.

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