Python Machine Learning A Beginner's Guide to Scikit-Learn: A Hands-On Approach

✍ Scribed by Rajender Kumar

• Publisher: Rajender Kumar
• Year: 2024
• Tongue: English
• Leaves: 623
• Category: Library

✦ Synopsis

Are you ready to dive into the world of Python machine learning?

Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of machine learning and the powerful Scikit-learn library.

Key Features

Detailed introduction to the fundamentals of machine learning and the Scikit-Learn library.

Comprehensive coverage of essential concepts such as data preprocessing, model selection, evaluation, and optimization.

Hands-on experience with real-world datasets and practical projects that will help you develop the skills you need to succeed in machine learning.

Easy-to-follow explanations and step-by-step examples that make it easy for beginners to get started and advanced users to take their skills to the next level.

See how machine learning is being used to solve problems in industries such as healthcare, finance and more.

This book is perfect for beginners who are new to machine learning and want to learn Scikit-Learn from scratch. It is also ideal for intermediate and advanced users who want to expand their knowledge and build more complex models.

Outcome

Unlock the earning potential of up to $300k in job after reading the book.

Boosting your resume.

Opening doors to new opportunities.

What other people says

Don't just take our word for it - see what other readers have said

"I was able to understand machine learning concepts and implement them easily with the help of this book."

"Rajender Kumar's writing style made the complex concepts easy to understand."

"I highly recommend this book to anyone looking to learn machine learning with Python."

Don't miss out on this opportunity to master the art of Python machine learning with "Python Machine Learning: A Beginner's Guide to Scikit-Learn". Get your copy today and start building your own intelligent systems!

WHO THIS BOOK IS FOR?

"Python Machine Learning: A Beginner's Guide to Scikit-Learn" is intended for a wide range of readers, including

Individuals who are new to the field of machine learning and want to gain a solid understanding of the basics and how to apply them using the popular scikit-learn library in Python.

Data scientists, statisticians, and analysts who are familiar with machine learning concepts but want to learn how to implement them using Python and scikit-learn.

Developers and engineers who want to add machine learning to their skill set and build intelligent applications using Python.

Students and researchers who are studying machine learning and want to learn how to apply it using a widely used and accessible library like scikit-learn.

✦ Table of Contents

Found Typos & Broken Link
Support
Disclaimer
Acknowledgments
How to use this book?
Conventions Used in This Book
Get Code Examples Online
About the Author
Who this book is for?
What are the requirements? (Pre-requisites)
Preface
Why Should You Read This Book?
Python Machine Learning: A Beginner's Guide to Scikit-learn
1 Introduction to Machine Learning
1.1 Background on machine learning
1.2 Why Python for Machine Learning
1.3 Overview of scikit-learn
1.4 Setting up the development environment
1.5 Understanding the dataset
1.6 Type of Data
1.7 Types of machine learning models
1.8 Summary
1.9 Test Your Knowledge
1.10 Answers
2 Python: A Beginner's Overview
2.1 Python Basics
2.2 Data Types in Python
2.3 Control Flow in Python
2.4 Functio in Python
2.5 Anonymous (Lambda) Function
2.6 Function for List
2.7 Function for Dictionary
2.8 String Manipulation Function
2.9 Exception Handling
2.10 File Handling in Python
2.11 Modlues in Python
2.12 Style Guide for Python Code
2.13 Docstring Conventions in python
2.14 Python library for Data Science
2.15 Summary
2.16 Test Your Knowledge
2.17 Answers
3 Data Preparation
3.1 Importing data
3.2 Cleaning data
3.3 Exploratory data analysis
3.4 Feature engineering
3.5 Splitting the data into training and testing sets
3.6 Summary
3.7 Test Your Knowledge
3.8 Answers
4 Supervised Learning
4.1 Linear regression
4.2 Logistic Regression
4.3 Decision Trees
4.4 Random Forests
4.5 Confusion Matrix
4.6 Support Vector Machines
4.7 Summary
4.8 Test Your Knowledge
4.9 Answers
5 Unsupervised Learning
5.1 Clustering
5.2 K-Means Clustering
5.3 Hierarchical Clustering
5.4 DBSCAN
5.5 GMM (Gaussian Mixture Model)
5.6 Dimensionality Reduction
5.7 Principal Component Analysis (PCA)
5.8 Independent Component Analysis (ICA)
5.9 t-SNE
5.10 Autoencoders
5.11 Anomaly Detection
5.12 Summary
5.13 Test Your Knowledge
5.14 Answers
6 Deep Learning
6.1 What is Deep Learning
6.2 Neural Networks
6.3 Backpropagation
6.4 Convolutional Neural Networks
6.5 Recurrent Neural Networks
6.6 Generative Models
6.7 Transfer Learning
6.8 Tools and Frameworks for Deep Learning
6.9 Best Practices and Tips for Deep Learning
6.10 Summary
6.11 Test Your Knowledge
6.12 Answers
7 Model Selection and Evaluation
7.1 Model selection and evaluation techniques
7.2 Understanding the Bias-Variance trade-off
7.3 Overfitting and Underfitting
7.4 Splitting the data into training and testing sets
7.5 Hyperparameter Tuning
7.6 Model Interpretability
7.7 Feature Importance Analysis
7.8 Model Visualization
7.9 Simplifying the Model
7.10 Model-Agnostic Interpretability
7.11 Model Comparison
7.12 Learning Curves
7.13 Receiver Operating Characteristic (ROC) Curves
7.14 Precision-Recall Curves
7.15 Model persistence
7.16 Summary
7.17 Test Your Knowledge
7.18 Answers
8 The Power of Combining: Ensemble Learning Methods
8.1 Types of Ensemble Learning Methods
8.2 Bagging (Bootstrap Aggregating)
8.3 Boosting: Adapting the Weak to the Strong
8.4 Stacking: Building a Powerful Meta Model
8.5 Blending
8.6 Rotation Forest
8.7 Cascading Classifiers
8.8 Adversarial Training
8.9 Voting Classifier
8.10 Summary
8.11 Test Your Knowledge
8.12 Practical Exercise
8.13 Answers
8.14 Exercise Solutions
9 Real-World Applications of Machine Learning
9.1 Natural Language Processing
9.2 Computer Vision
9.3 Recommender Systems
9.4 Time series forecasting
9.5 Predictive Maintenance
9.6 Speech Recognition
9.7 Robotics and Automation
9.8 Autonomous Driving
9.9 Fraud Detection
9.10 Other Real-Life applications
9.11 Summary
9.12 Test Your Knowledge
9.13 Answers
A. Future Directions in Python Machine Learning
B. Additional Resources
Websites & Blogs
Online Courses and Tutorials
Conferences and Meetups
Communities and Support Groups
Podcasts
Research Papers
C. Tools and Frameworks
D. Datasets
Open-Source Datasets
E. Career Resources
Companies and Startups working in the field of Machine Learning
Research Labs and Universities with a focus on Machine Learning
Government Organizations and Funding Agencies supporting ML Research and Development
F. Glossary