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
* Build complex data-driven models using the lookup and reference functions.
* Learn how to speed up tedious and time-consuming tasks with the user-defined functions in Excel.
* Use a wide range of financial functions to perform complex financial calculations.
* Analyze data and perform various statistical calculations using the statistical functions.
* Explore and work with different mathematical functions in Excel.

WHO THIS BOOK IS FOR
This book is for everyone who uses Excel daily. It is also for business professionals, researchers, scientists, statisticians, and students who want to use Excel for managing and analyzing data.

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 not just a programmer. He has been involved in developing more than 10 Enterprise and 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 has managed highly profitable business units. He has set up three successful companies as of 2021 in India, Dubai, and Saudi Arabia.

Partha has also 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. He 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 masters in Telecommunications and a masters in computer security. He has also completed executive MBAs in Information Systems and Business Analytics. Alongside this, he has 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 currently pursuing a Doctorate in Business Administration from the Swiss School of Business and Management (Geneva).

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