Mastering Classification Algorithms for Machine Learning

Classification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you.

The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Naïve Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Naïve Bayes algorithm for classification is also covered. Moving forward, the book centers its attention on the Logistic Regression algorithm, exploring the sigmoid function and its significance in binary classification. The book also covers Decision Trees and discusses the Gini Factor, Entropy, and their use in splitting trees and generating decision leaves. The Random Forest algorithm is also thoroughly explained as a cutting-edge method for classification (and regression). The book concludes by exploring practical applications such as Spam Detection, Customer Segmentation, Disease Classification, Malware Detection in JPEG and ELF Files, Emotion Analysis from Speech, and Image Classification.

By the end of the book, you will become proficient in utilizing classification algorithms for solving complex machine learning problems.

KEY FEATURES  

• Get familiar with all the state-of-the-art classification algorithms for machine learning.
• Understand the mathematical foundations behind building machine learning models.
• Learn how to apply machine learning models to solve real-world industry problems.

WHAT YOU WILL LEARN

• Learn how to apply Naïve Bayes algorithm to solve real-world classification problems.
• Explore the concept of K-Nearest Neighbor algorithm for classification tasks.
• Dive into the Logistic Regression algorithm for classification.
• Explore techniques like Bagging and Random Forest to overcome the weaknesses of Decision Trees.
• Learn how to combine multiple models to improve classification accuracy and robustness.

WHO THIS BOOK IS FOR

This book is for Machine Learning Engineers, Data Scientists, Data Science Enthusiasts, Researchers, Computer Programmers, and Students who are interested in exploring a wide range of algorithms utilized for classification tasks in machine learning.

1. Introduction to Machine Learning
2. Naïve Bayes Algorithm
3. K-Nearest Neighbor Algorithm
4. Logistic Regression
5. Decision Tree Algorithm
6. Ensemble Models
7. Random Forest Algorithm
8. Boosting Algorithm
Annexure 1: Jupyter Notebook
Annexure 2: Python
Annexure 3: Singular Value Decomposition
Annexure 4: Preprocessing Textual Data
Annexure 5: Stemming and Lamentation
Annexure 6: Vectorizers
Annexure 7: Encoders
Annexure 8: Entropy

Partha Majumdar is just a programmer. He has been involved in developing more than 10 Enterprise Class products deployed in Customer locations in more than 57 countries. He has worked with key ministries of 8 countries in developing key systems for them. Also, he has been involved in developing key systems for more than 20 enterprises.

Partha has been employed in enterprises including Siemens, Amdocs, NIIT, Mobily, and JP Morgan Chase & Co. Apart from developing company systems, Partha managed highly profitable business units. He has set up three successful companies as of 2021 in India, Dubai, and Saudi Arabia.

Partha has developed OLTP systems for Telcos, Hospitals, Tea Gardens, Factories, Travel Houses, Cricket tournaments, etc. Since 2012, Partha has been developing Data Products and intensively working on Machine Learning and Deep Learning. Partha has a panache for finding patterns in most of what he gets involved in. As a result, Partha has been useful to teams in developing Rapid Development Tools.

Partha has continued to learn new domains and technology throughout his career. After graduating in Mathematics, Partha completed a master's in Telecommunications and a master's in computer security. He has also completed executive MBAs in Information Systems and Business Analytics. He completed a PG Certificate program in AI/ML/DL from Manipal Academy of Higher Education (Dubai), an advanced certificate in Cyber Security from IIT (Kanpur), and a PG-level advanced certificate in Computational Data Sciences from IISc (Bengaluru). He is pursuing a Doctorate in Business Administration from the Swiss School of Business and Management (Geneva).

Partha is an avid traveller. He has had the opportunity to visit twenty-four countries for work and leisure so far. Partha loves experiencing diverse cultures and learns from every interaction.

Partha is married to Deepshree and has two daughters - Riya and Ranoo.