Mastering Machine Learning: From Basics to Advanced

Govindakumar Madhavan (Autor)

Buch | Hardcover

257 Seiten

2025
Springer Nature (Verlag)
978-981-97-9913-8 (ISBN)

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This book covers all aspects of machine learning (ML) from concepts and math to ML programming. ML concepts and the math associated with ML are written from an application perspective, rather than from a theoretical perspective. The book presents concepts and algorithms precisely as they are used in real-world applications, ensuring a seamless and practical understanding with no gap between theory and practice.

In a distinctive approach, the book's content is complemented by video lectures whose details can be found inside the book. This innovative approach offers readers a multimedia learning experience, accommodating different learning preferences, and reinforcing the material through visual and auditory means. If you are new to Artificial Intelligence and Machine Learning, this could be the first book you read and the first video course you take.

Govindakumar Madhavan is the founder and CEO of SeaportAi. He completed his B.Tech. and MBA from top institutes in India. He also holds certifications in six sigma and project management. He has over 2 decades of experience managing technology, operations, and quality in large MNCs and start-ups. He has led high performance multi-national teams and managed businesses across the Asia Pacific region. He has successfully incubated Centers of Excellence for fraud prevention and service analytics. He has significant experience in design thinking-based product development and management. He has also played a critical role in developing products for emerging markets and won global awards in the areas of Customer Experience, Leadership Excellence, Quality, and Technology. He is a prolific author of about 40 video courses and 10 books.

Chapter 1: Introduction.- Chapter 2: Python Programming Using Google Cloud (COLAB).- Chapter 3: Introduction to Colab: Google Cloud Development Environment.- Chapter 4: Getting started with python.-Chapter 5: Conditions.- Chapter 6: Loops.- Chapter 7: Functions.- Chapter 8: Arrays.- Chapter 9: NumPy.-Chapter 10: PANDAS.- Chapter 11: Data Visualization using Matplotlib.- Chapter 12: Dependent Vs. Independent Variables.- Chapter 13: Types of Data.- Chapter 14: Population Vs. Sample.- Chapter 15: Hypothesis Testing.- Chapter 16: Machine Learning Concepts .- Chapter 17: Measuring Accuracy in Algorithms.- Chapter 18: Understanding Regression Concepts.- Chapter 19: Simple Linear Regression (Programming).- Chapter 20: Advanced Data Visualization for Regression.- Chapter 21: Multiple Linear Regression (Programming).- Chapter 22: Gradient Descent.- Chapter 23: Logistic Regression (Programming).- Chapter 24: Unsupervised Learning – Concepts & Programming.- Chapter 25: Exploratory Data Analysis.

Erscheinungsdatum	13.05.2025
Reihe/Serie	Transactions on Computer Systems and Networks
Zusatzinfo	257 Illustrations, color; 89 Illustrations, black and white; XIII, 257 p. 346 illus., 257 illus. in color. Book + Online Course.
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	anomaly detection algorithms • Data Science • Exploratory Data Analysis (EDA) • linear regression • machine learning • supervised learning algorithms
ISBN-10	981-97-9913-9 / 9819799139
ISBN-13	978-981-97-9913-8 / 9789819799138
Zustand	Neuware