Fundamentals of Machine Learning with scikit-learn [Video]

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Fundamentals of Machine Learning with scikit-learn [Video]

Giuseppe Bonaccorso
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Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide
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Video Details

ISBN 139781789134377
Course Length2 hours and 33 minutes

Video Description

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge.

In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.

The code bundle for this video course is available at - https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning-with-scikit-learn

Style and Approach

An easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of algorithms for Machine Learning.

Table of Contents

Introduction to Machine Learning
The Course Overview
Machine Types and Learning Methods
Data Formats
Learnability
Statistical Learning Approaches
Elements of Information Theory
Feature Selection and Feature Engineering
Splitting Datasets
Managing Data
Data Scaling and Normalization
Principal Component Analysis
Linear Regression
Linear Models and Its Example
Linear Regression with scikit-learn
Ridge, Lasso, and ElasticNet
Regression Types
Logistic Regression
Logistic Regression
Stochastic Gradient Descent Algorithms
Finding the Optimal Hyperparameters
Classification Metrics
ROC Curve
Naive Bayes’
Bayes’ Theorem
Naive Bayes’ in scikit-learn
Support Vector Machines
scikit-learn Implementation
Controlled Support Vector Machines
Decision Trees and Ensemble Learning
Binary Decision Trees
Decision Tree Classification with scikit-learn
Ensemble Learning
Clustering Fundamentals
Clustering Basics
DBSCAN and Spectral Clustering
Evaluation Methods Based on the Ground Truth
Hierarchical Clustering
Agglomerative Clustering
Implementing Agglomerative Clustering
Connectivity Constraints
Introduction to Recommendation Systems
User-Based Systems
Content-Based Systems

What You Will Learn

  • Acquaint yourself with important elements of Machine Learning
  • Understand the feature selection and feature engineering process
  • Assess performance and error trade-offs for Linear Regression
  • Build a data model 
  • Understand how a data model works
  • Understand strategies for hierarchical clustering
  • Ensemble learning with decision trees
  • Learn to tune the parameters of Support Vector machines
  • Implement clusters to a dataset

Authors

Table of Contents

Introduction to Machine Learning
The Course Overview
Machine Types and Learning Methods
Data Formats
Learnability
Statistical Learning Approaches
Elements of Information Theory
Feature Selection and Feature Engineering
Splitting Datasets
Managing Data
Data Scaling and Normalization
Principal Component Analysis
Linear Regression
Linear Models and Its Example
Linear Regression with scikit-learn
Ridge, Lasso, and ElasticNet
Regression Types
Logistic Regression
Logistic Regression
Stochastic Gradient Descent Algorithms
Finding the Optimal Hyperparameters
Classification Metrics
ROC Curve
Naive Bayes’
Bayes’ Theorem
Naive Bayes’ in scikit-learn
Support Vector Machines
scikit-learn Implementation
Controlled Support Vector Machines
Decision Trees and Ensemble Learning
Binary Decision Trees
Decision Tree Classification with scikit-learn
Ensemble Learning
Clustering Fundamentals
Clustering Basics
DBSCAN and Spectral Clustering
Evaluation Methods Based on the Ground Truth
Hierarchical Clustering
Agglomerative Clustering
Implementing Agglomerative Clustering
Connectivity Constraints
Introduction to Recommendation Systems
User-Based Systems
Content-Based Systems

Video Details

ISBN 139781789134377
Course Length2 hours and 33 minutes
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