Machine Learning Step by Step Guide To Implement Machine Learning Algorithms with Python 1st Edition by Rudolph Russell ISBN 9781719528405

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Product details:

ISBN 10: 
ISBN 13: 9781719528405
Author: Rudolph Russell

This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a practical way, and we’ll implement many machine-learning algorithms using the Scikit-learn library in the Python programming language. In the first chapter, you’ll learn the most important concepts of machine learning, and, in the next chapter, you’ll work mainly with the classification. In the last chapter you’ll learn how to train your model. I assume that you’ve knowledge of the basics of programming

This book contains illustrations and step-by-step explanations with bullet points and exercises for easy and enjoyable learning.

Benefits of reading this book that you’re not going to find anywhere else:

• Introduction to Machine Learning
• Classification
• How to train a Model
• Different Models Combinations

Don’t miss out on this new step by step guide to Machine Learning. All you need to do is scroll up and click on the BUY NOW button to learn all about it!

Machine Learning Step by Step Guide To Implement Machine Learning Algorithms with Python 1st Table of contents:

Chapter 1: Introduction to Machine Learning

• 1.1 What is Machine Learning?
• 1.2 Types of Machine Learning
• 1.3 Applications of Machine Learning
• 1.4 Steps in Machine Learning Workflow
• 1.5 Setting Up Python for Machine Learning
• 1.6 Key Python Libraries for Machine Learning
• 1.7 Summary

Chapter 2: Data Preprocessing

• 2.1 The Importance of Data Preprocessing
• 2.2 Importing and Loading Data
• 2.3 Handling Missing Data
• 2.4 Encoding Categorical Data
• 2.5 Feature Scaling
• 2.6 Splitting the Dataset into Training and Test Sets
• 2.7 Data Preprocessing Example with Python
• 2.8 Summary

Chapter 3: Supervised Learning Algorithms

• 3.1 Introduction to Supervised Learning
• 3.2 Linear Regression
• 3.3 Logistic Regression
• 3.4 Decision Trees
• 3.5 Random Forest
• 3.6 Support Vector Machines (SVM)
• 3.7 k-Nearest Neighbors (k-NN)
• 3.8 Naive Bayes Classifier
• 3.9 Evaluating Model Performance (Accuracy, Precision, Recall)
• 3.10 Summary

Chapter 4: Unsupervised Learning Algorithms

• 4.1 Introduction to Unsupervised Learning
• 4.2 K-Means Clustering
• 4.3 Hierarchical Clustering
• 4.4 Principal Component Analysis (PCA)
• 4.5 Anomaly Detection with Isolation Forest
• 4.6 Evaluating Unsupervised Models
• 4.7 Summary

Chapter 5: Model Evaluation and Hyperparameter Tuning

• 5.1 The Need for Model Evaluation
• 5.2 Cross-Validation
• 5.3 Hyperparameter Tuning with Grid Search
• 5.4 Random Search for Hyperparameter Tuning
• 5.5 Evaluating Model Performance with ROC and AUC
• 5.6 The Bias-Variance Tradeoff
• 5.7 Summary

Chapter 6: Advanced Machine Learning Techniques

• 6.1 Introduction to Advanced Techniques
• 6.2 Ensemble Learning (Bagging and Boosting)
• 6.3 XGBoost and LightGBM
• 6.4 Stacking Models
• 6.5 Support Vector Regression (SVR)
• 6.6 Neural Networks for Machine Learning
• 6.7 Transfer Learning
• 6.8 Summary

Chapter 7: Deep Learning Basics

• 7.1 Introduction to Deep Learning
• 7.2 Neural Networks Overview
• 7.3 Deep Learning with TensorFlow and Keras
• 7.4 Building Your First Neural Network
• 7.5 Convolutional Neural Networks (CNN)
• 7.6 Recurrent Neural Networks (RNN)
• 7.7 Summary

Chapter 8: Natural Language Processing (NLP)

• 8.1 Introduction to NLP
• 8.2 Text Preprocessing (Tokenization, Lemmatization)
• 8.3 Bag-of-Words and TF-IDF
• 8.4 Sentiment Analysis with Python
• 8.5 Word Embeddings (Word2Vec, GloVe)
• 8.6 Text Classification
• 8.7 Summary

Chapter 9: Model Deployment and Productionization

• 9.1 Introduction to Model Deployment
• 9.2 Deploying Models with Flask and FastAPI
• 9.3 Using Docker for Model Deployment
• 9.4 Introduction to Cloud-Based Deployment (AWS, GCP)
• 9.5 Monitoring and Maintaining Machine Learning Models
• 9.6 Summary

Chapter 10: Ethical Considerations in Machine Learning

• 10.1 Bias and Fairness in Machine Learning
• 10.2 Ethical Challenges in AI and ML
• 10.3 Privacy Considerations
• 10.4 Transparency and Explainability in Machine Learning
• 10.5 Responsible AI Practices
• 10.6 Summary

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