Python Machine Learning Projects: Learn how to build Machine Learning projects from scratch
- Length: 260 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2023-03-13
- ISBN-10: 9389898269
- ISBN-13: 9789389898262
- Sales Rank: #0 (See Top 100 Books)
A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques
Key Features
- Understand the core concepts and algorithms of Machine Learning.
- Get started with your Machine Learning career with this easy-to-understand guide.
- Discover different Machine Learning use cases across different domains.
Description
Since the last two decades, there have been many advancements in the field of Machine Learning. If you are new or want a comprehensive understanding of Machine Learning, then this book is for you.
The book starts by explaining how important Machine Learning is today and the technology required to make it work. The book then helps you get familiar with basic concepts that underlie Machine Learning, including basic Python Programming. It explains different types of Machine Learning algorithms and how they can be applied in various domains like Recommendation Systems, Text Analysis and Mining, Image Processing, and Social Media Analytics. Towards the end, the book briefly introduces you to the most popular metaheuristic algorithms for optimization.
By the end of the book, you will develop the skills to use Machine Learning effectively in various application domains.
What you will learn
- Discover various applications of Machine Learning in social media.
- Explore image processing techniques that can be used in Machine Learning.
- Learn how to use text mining to extract valuable insights from text data.
- Learn how to measure the performance of Machine Learning algorithms.
- Get familiar with the optimization algorithms in Machine Learning.
Who this book is for
This book delivers an excellent introduction to Machine Learning for beginners with no prior knowledge of coding, maths, or statistics. It is also helpful for existing and aspiring data professionals, students, and anyone who wishes to expand their Machine Learning knowledge.
Cover Page Title Page Copyright Page Dedication Page About the Authors About the Reviewer Acknowledgements Preface Errata Table of Contents 1. Introduction to ML Introduction Structure Objectives Introduction to Machine Learning (ML) Models in Machine Learning Supervised machine learning model through training Unsupervised machine learning model: Self-sufficient learning Semi-supervised machine learning model Reinforcement machine learning model: Hit and Trial Types of Machine Learning Algorithms Working of Machine Learning algorithm Challenges for Machine Learning Projects Limitations of machine learning Application areas of ML Difference between the terms data science, data mining, machine learning and deep learning Conclusion Questions and Answers 2. Python Basics for ML Introduction Structure Objectives Spyder IDE Jupyter Notebook Python: Input and Output Commands Logical Statements Loop and Control Statements Functions and Modules Class Handling Exception Handling File Handling String functions Conclusion Questions and Answers 3. An Overview of ML Algorithms Introduction Structure Objectives Machine learning modeling flow Terms used in preprocessing Raw data (or just data) Prepared data Need for Data Preprocessing Preprocessing in ML Researching the best model for the data Training and testing the model on data Evaluation Hyperparameter tuning Prediction Metrics Used Regression algorithms Types of regression techniques Linear Regression Logistic Regression Polynomial Regression Stepwise Regression Ridge Regression Lasso Regression ElasticNet Regression Classification Terminology used in Classification Algorithms Types of classification algorithms Performance measures for classification Clustering Clustering algorithms K-Means Clustering Mean-Shift Clustering Agglomerative Hierarchical Clustering Clustering Validation Neural Network and SVM Building Blocks: Neurons Combining Neurons into a Neural Network Training the neural network Neural Network Architectures Support vector machine (SVM) Machine Learning Libraries in Python Numpy Pandas Populating the Dataframe Displaying the Data Using Dataframe Accessing the Data Selectively in Dataframe Basic Descriptive Statistics using Pandas Data transformation Data Preprocessing – Handling missing values MatplotLib Line Graph Scatter Plot Bar plot Histogram Pie Chart Evaluation of ML Systems Test and Train Datasets Performance Measure Model Evaluation Techniques Model evaluation metrics Classification Metrics Regression Metrics Conclusion Questions 4. Case Studies and Projects in Machine Learning Introduction Structure Objectives Recommendation Generation Importance of recommendation systems Key terms used Items/Documents Query/Context Approaches for building recommendation systems Basic recommendation systems Candidate Generation Recommendation generation Information collection phase Learning phase Prediction/recommendation phase Evaluation metrics for recommendation algorithms Statistical accuracy metric Case study on Recommendation system: E-learning system domain Recommender systems Problem definition Objective of the case study Considerations for the case study System development Constraints / limitations while developing the recommendation system Text Analysis and Mining Importance of text mining Basic blocks of text mining system using ML Steps involved in preparing an unstructured text document for deeper analysis Text mining techniques Information Retrieval (IR) Natural language processing (NLP) Sentiment analysis Naive Bayes Linear regression Support Vector Machines (SVM) Deep learning Case study on product recommendation system based on sentiment analysis Product recommendation Problem definition System development Considerations for the case study Opinion mining Image processing Importance of image processing Purpose of image processing Basic Image Processing System Image Processing using popular ML algorithms Real Time case studies of Image Processing using ML Problem definition Objective of the case study Considerations for the case study System development Algorithms that can be employed Tool Utilization Constraints/Limitations while developing the system Evaluation Measures Predictive analytics Importance of predictive analytics Need for predictive analysis Machine Learning vs Predictive Modeling Building a predictive model Types of predictive algorithms Types of Predictive Analytical Models Comparison of Predictive Modeling and Predictive Analytics Predictive modeling vs data analytics Comparison between and Predictive Analytics and Data Analytics Uses or applications of Predictive Analytics Benefits of predictive analytics Challenges of predictive modeling Limitations of predictive modeling Case studies Social media analytics Case study of Instagram Case study on Customer churning analytics Building the hypothesis Case study on learning analytics in education systems Challenges faced Approach Other case studies Conclusion 5. Optimization in ML Algorithm Introduction Structure Objectives Optimization – Need of ML projects Types of optimization techniques Conventional Approach Metaheuristic Approach Basic Optimization Techniques Backpropagation optimization Gradient descent optimization Metaheuristic approaches to optimization Types of metaheuristic algorithms Single solution-based algorithms Population-based algorithms Improvisation of ML algorithms by optimizing the learning parameters Case study 1: Metaheuristic Optimization Techniques in Healthcare Case Study 2: Genetic Algorithm (GA) in Batch Production Optimization using Python Conclusion Questions Index
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