The
Machine
Learning
Workshop

Take a comprehensive and step-by-step approach to understanding machine learning.

Get Started with Machine Learning Today

You'll be up and running with machine learning in no time at all.

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$34.99The Machine Learning Workshop

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Learn by doing real-world development, supported every step of the way with step-by-step examples and expert screencasts.
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A Smarter Way to Learn ML

A step-by-step approach to getting up and running with real-world machine learning in no time at all.

The Machine Learning Workshop is ideal if you're looking for a structured, self-paced way to get started with machine learning.

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Course Curriculum

An A to Z tour of machine learning.

Workshop Onboarding
1. Introduction to Scikit-Learn
2. Unsupervised Learning – Real-Life Applications
- Overview
- Clustering
- Exploring a Dataset – Wholesale Customers Dataset
- Data Visualization
- Exercise 2.01: Plotting a Histogram of One Feature from the Circles Dataset
- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process
- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process
- k-means Algorithm
- Exercise 2.02: Importing and Training the k-means Algorithm over a Dataset
- Activity 2.02: Applying the k-means Algorithm to a Dataset
- Activity 2.02: Applying the k-means Algorithm to a Dataset
- Mean-Shift Algorithm
- Exercise 2.03: Importing and Training the Mean-Shift Algorithm over a Dataset
- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset
- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset
- DBSCAN Algorithm
- Exercise 2.04: Importing and Training the DBSCAN Algorithm over a Dataset
- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset
- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset
- Evaluating the Performance of Clusters
- Exercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski–Harabasz Index
- Activity 2.05: Measuring and Comparing the Performance of the Algorithms
- Activity 2.05: Measuring and Comparing the Performance of the Algorithms
- Summary
- Survey I
3. Supervised Learning – Key Steps
- Overview
- Supervised Learning Tasks
- Model Validation and Testing
- Exercise 3.01: Performing a Data Partition on a Sample Dataset
- Cross-Validation
- Exercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set
- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset
- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset
- Evaluation Metrics
- Exercise 3.03: Calculating Different Evaluation Metrics on a Classification Task
- Choosing an Evaluation Metric
- Exercise 3.04: Calculating Evaluation Metrics on a Regression Task
- Activity 3.02: Evaluating the Performance of the Model Trained on a Handwritten Dataset
- Activity 3.02: Evaluating the Performance of the Model Trained on a Handwritten Dataset
- Error Analysis
- Exercise 3.05: Calculating the Error Rate on Different Sets of Data
- Activity 3.03: Performing Error Analysis on a Model Trained to Recognize Handwritten Digits
- Activity 3.03: Performing Error Analysis on a Model Trained to Recognize Handwritten Digits
- Summary
- Survey II
4. Supervised Learning Algorithms: Predicting Annual Income
- Overview
- Exploring the Dataset
- The Naïve Bayes Algorithm
- Exercise 4.01: Applying the Naïve Bayes Algorithm
- Activity 4.01: Training a Naïve Bayes Model for Our Census Income Dataset
- Activity 4.01: Training a Naïve Bayes Model for Our Census Income Dataset
- The Decision Tree Algorithm
- Exercise 4.02: Applying the Decision Tree Algorithm
- Activity 4.02: Training a Decision Tree Model for Our Census Income Dataset
- Activity 4.02: Training a Decision Tree Model for Our Census Income Dataset
- The Support Vector Machine Algorithm
- Exercise 4.03: Applying the SVM Algorithm
- Activity 4.03: Training an SVM Model for Our Census Income Dataset
- Activity 4.03: Training an SVM Model for Our Census Income Dataset
- Error Analysis
- Summary
5. Artificial Neural Networks: Predicting Annual Income
- Overview
- Artificial Neural Networks
- How Do ANNs Work?
- Understanding the Hyperparameters
- Applications of Neural Networks
- Applying an Artificial Neural Network
- Exercise 5.01: Applying the MLP Classifier Class
- Activity 5.01: Training an MLP for Our Census Income Dataset
- Activity 5.01: Training an MLP for Our Census Income Dataset
- Performance Analysis
- Activity 5.02: Comparing Different Models to Choose the Best Fit for the Census Income Data Problem
- Activity 5.02: Comparing Different Models to Choose the Best Fit for the Census Income Data Problem
- Summary
- Survey III
6. Building Your Own Program
- Overview
- Program Definition
- Understanding the Dataset
- Activity 6.01: Performing the Preparation and Creation Stages for the Bank Marketing Dataset
- Activity 6.01: Performing the Preparation and Creation Stages for the Bank Marketing Dataset
- Saving and Loading a Trained Model
- Exercise 6.01: Saving a Trained Model
- Loading a Model
- Exercise 6.02: Loading a Saved Model
- Activity 6.02: Saving and Loading the Final Model for the Bank Marketing Dataset
- Activity 6.02: Saving and Loading the Final Model for the Bank Marketing Dataset
- Interacting with a Trained Model
- Exercise 6.03: Creating a Class and a Channel to Interact with a Trained Model
- Activity 6.03: Allowing Interaction with the Bank Marketing Dataset Model
- Activity 6.03: Allowing Interaction with the Bank Marketing Dataset Model
- Summary

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$34.99The Machine Learning Workshop