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Total Size:
5.4 MB
Info Hash:
40B4C5A886162986A82F947E9D2C037CA6359D4C
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Added:
April 21, 2026, 12:43 p.m.
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(Last updated: April 21, 2026, 12:48 p.m.)
| File | Size |
|---|---|
| Sahay R. Machine Learning Fundamentals. Concepts, Models, and Applications 2025.pdf | 5.4 MB |
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132.9 MB
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| Uploaded by Anonymous | Size 132.9 MB | Health [ 11 /1 ] | Added 2026-03-02 |
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| Uploaded by PornyXXX | Size 111.7 MB | Health [ 17 /33 ] | Added 2025-07-22 |
NOTE
SOURCE: Sahay R. Machine Learning Fundamentals. Concepts, Models, and Applications 2025
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MEDIAINFO
Textbook in PDF format Machine Learning Fundamentals provides a comprehensive overview of Data Science, emphasizing Machine Learning (ML). This book covers ML fundamentals, processes, and applications, that are used as industry standards. Both supervised and unsupervised learning ML models are discussed. Topics include data collection and feature engineering techniques as well as regression, classification, neural networks (Deep Learning), and clustering. Motivated by the success of ML in various fields, this book is designed for a wide audience coming from various disciplines such as engineering, IT, or business and is suitable for those getting started with ML for the first time. This text can also serve as the main or supplementary text in any introductory data science course from any discipline, offering real-world applications and tools in all areas. Data Science is a data-driven decision-making approach that uses several different areas, methods, algorithms, models, and disciplines with the purpose of extracting insights and knowledge from structured and unstructured data. These insights are helpful in applying algorithms and models to make decisions. Data Science draws from several fields. The models in Data Science are used in predictive and prescriptive analytics. Predictive analytics models are used in decision making and predicting future outcomes. Machine Learning (ML) is a key element of Data Science that uses a set of algorithms to improve predictions and decision making by learning from large quantities of data. With the advancement in storage and computing technologies, an abundance of high-quality data is used efficiently in creating ML models. One of the main reasons for the success of ML applications is the difficulty of applying conventional programming techniques to huge quantities of data. Conventional programming techniques are not able to handle massive amounts of data but are successfully applied in using ML techniques to create and solve numerous problems in different areas ranging from engineering, biological sciences, business, and many more. The background needed for Data Science and ML, among other areas, includes the knowledge of linear algebra, statistics and probability, and programming (the most commonly used programming language is Python). This book provides fundamental concepts of ML, managing data and evaluating ML models, the computer packages used in ML, the problem-solving approaches for ML—supervised and unsupervised learning models and their applications, neural networks and Deep Learning models, training ML models, current state, problems solved using ML, and outlook of the field. Introduction Part 1 Data Science and Its Elements Part 2 Machine Learning (ML) Libraries, Data Representation, Problem Formulation, and EDA Part 3 Machine Learning Models Part 4 Training Machine Learning Models Part 5 Current State of Machine Learning
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