The
Supervised
Learning Workshop

Discover how you can supervise machine learning algorithms in Python and personalize predictive models with the help of realtime datasets.

Get Started with Supervised Learning Today

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

$34.99

$34.99The Supervised 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.
Become a verified practitioner and earn an authenticated digital certificate from Packt upon successful completion.
Manage your learning based on your personal schedule, with content that lets you pause and resume your progress at will.

A Smarter Way to Understand Supervised Learning

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

The Supervised Learning Workshop is ideal if you're looking for a structured, hands-on approach to get started with supervised learning.

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Course Curriculum

An A to Z tour of supervised learning.

Workshop Onboarding
1. Fundamentals of Supervised Learning Algorithms
2. Exploratory Data Analysis and Visualization
- Overview
- Exploratory Data Analysis (EDA)
- Exercise 2.01: Summarizing the Statistics of Our Dataset
- Missing Values
- Exercise 2.02: Visualizing Missing Values
- Imputation Strategies for Missing Values
- Exercise 2.03: Performing Imputation Using Pandas
- Exercise 2.04: Performing Imputation Using Scikit-Learn
- Exercise 2.05: Performing Imputation Using Inferred Values
- Activity 2.01: Summary Statistics and Missing Values
- Activity 2.01: Summary Statistics and Missing Values
- Distribution of Values
- Exercise 2.06: Plotting a Bar Chart
- Exercise 2.07: Identifying Data Types for Categorical Variables
- Exercise 2.08: Calculating Category Value Counts
- Exercise 2.09: Plotting a Pie Chart
- Continuous Data
- Exercise 2.10: Plotting a Histogram
- Exercise 2.11: Computing Skew and Kurtosis
- Activity 2.02: Representing the Distribution of Values Visually
- Activity 2.02: Representing the Distribution of Values Visually
- Relationships within the Data
- Exercise 2.12: Plotting a Scatter Plot
- Exercise 2.13: Plotting a Correlation Heatmap
- Using Pairplots
- Exercise 2.14: Implementing a Pairplot
- Exercise 2.15: Plotting a Bar Chart
- Exercise 2.16: Visualizing a Box Plot
- Exercise 2.17: Plotting a Stacked Bar Chart
- Activity 2.03: Relationships within the Data
- Activity 2.03: Relationships within the Data
- Summary
- Survey I
3. Linear Regression
- Overview
- Regression and Classification Problems
- Exercise 3.01: Plotting Data with a Moving Average
- Activity 3.01: Plotting Data with a Moving Average
- Activity 3.01: Plotting Data with a Moving Average
- Linear Regression
- Exercise 3.02: Fitting a Linear Model Using the Least Squares Method
- Activity 3.02: Linear Regression Using the Least Squares Method
- Activity 3.02: Linear Regression Using the Least Squares Method
- Linear Regression with Categorical Variables
- Exercise 3.03: Introducing Dummy Variables
- Activity 3.03: Dummy Variables
- Activity 3.03: Dummy Variables
- Polynomial Models with Linear Regression
- Exercise 3.04: Polynomial Models with Linear Regression
- Activity 3.04: Feature Engineering with Linear Regression
- Activity 3.04: Feature Engineering with Linear Regression
- Generic Model Training
- Exercise 3.05: Linear Regression with Gradient Descent
- Exercise 3.06: Optimizing Gradient Descent
- Activity 3.05: Gradient Descent
- Activity 3.05: Gradient Descent
- Multiple Linear Regression
- Exercise 3.07: Multiple Linear Regression
- Summary
4. Autoregression
- Overview
- Autoregression Models
- Exercise 4.01: Creating an Autoregression Model
- Activity 4.01: Autoregression Model Based on Periodic Data
- Activity 4.01: Autoregression Model Based on Periodic Data
- Summary
- Survey II
5. Classifcation Techniques
- Overview
- Ordinary Least Squares as a Classifier
- Exercise 5.01: Ordinary Least Squares as a Classifier
- Logistic Regression
- Exercise 5.02: Logistic Regression as a Classifier – Binary Classifier
- Exercise 5.03: Logistic Regression – Multiclass Classifier
- Activity 5.01: Ordinary Least Squares Classifier – Binary Classifier
- Activity 5.01: Ordinary Least Squares Classifier – Binary Classifier
- Select K Best Feature Selection
- Exercise 5.04: Breast Cancer Diagnosis Classification Using Logistic Regression
- Classification Using K-Nearest Neighbors
- Exercise 5.05: KNN Classification
- Exercise 5.06: Visualizing KNN Boundaries
- Activity 5.02: KNN Multiclass Classifier
- Activity 5.02: KNN Multiclass Classifier
- Classification Using Decision Trees
- Exercise 5.07: ID3 Classification
- Classification and Regression Tree
- Exercise 5.08: Breast Cancer Diagnosis Classification Using a CART Decision Tree
- Activity 5.03: Binary Classification Using a CART Decision Tree
- Activity 5.03: Binary Classification Using a CART Decision Tree
- Artificial Neural Networks
- Exercise 5.09: Neural Networks – Multiclass Classifier
- Activity 5.04: Breast Cancer Diagnosis Classification Using Artificial Neural Networks
- Activity 5.04: Breast Cancer Diagnosis Classification Using Artificial Neural Networks
- Summary
6. Ensemble Modeling
- Overview
- One-Hot Encoding
- Exercise 6.01: Importing Modules and Preparing the Dataset
- Overfitting and Underfitting
- Exercise 6.02: Using the Bagging Classifier
- Random Forest
- Exercise 6.03: Building the Ensemble Model Using Random Forest
- Boosting
- Exercise 6.04: Implementing Adaptive Boosting
- Exercise 6.05: Implementing GradientBoostingClassifier to Build an Ensemble Model
- Stacking
- Exercise 6.06: Building a Stacked Model
- Activity 6.01: Stacking with Standalone and Ensemble Algorithms
- Activity 6.01: Stacking with Standalone and Ensemble Algorithms
- Summary
- Survey III
7. Model Evaluation
- Overview
- Importing the Modules and Preparing Our Dataset
- Evaluation Metrics
- Exercise 7.01: Calculating Regression Metrics
- Classification Metrics
- Exercise 7.02: Calculating Classification Metrics
- Splitting a Dataset
- Exercise 7.03: Performing K-Fold Cross-Validation with Stratified Sampling
- Performance Improvement Tactics
- Exercise 7.04: Hyperparameter Tuning with Random Search
- Exercise 7.05: Feature Importance Using Random Forest
- Activity 7.01: Final Test Project
- Activity 7.01: Final Test Project
- Summary

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$34.99

$34.99The Supervised Learning Workshop

Unlock one year of full, unlimited access!

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$34.99The Supervised Learning Workshop