Machine Learning Tutorial: Introduction to Machine Learning
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Author Description
Machine Learning is the most popular technique of predicting the future or classifying information to help people in making necessary decisions. Machine Learning algorithms are trained over instances or examples through which they learn from past experiences and also analyze the historical data. Therefore, as it trains over the examples, again and again, it is able to identify patterns in order to make predictions about the future.ContentsChapter 1 IntroductionChapter 2 Basics Chapter 3 SoftwareChapter 4 Applications Chapter 5 Types of Machine Learning Chapter 6 ClassificationChapter 7 ToolsChapter8 Best Way to LearnChapter 9 Future Chapter 10 Why PopularChapter 11 Algorithms.Chapter 12 Use Cases.Chapter 13 Advantages and DisadvantageChapter 14 Java Libraries Chapter 15 Clustering Chapter 16 Gaussian Mixture Model Chapter 17 Convolutional Neural Networks.Chapter 18 Recurrent Neural NetworksChapter 19 Artificial Neural Networks.Chapter 20 ANN Applications Chapter 21 ANN Model.Chapter 22 ANN AlgorithmsChapter 23 Education Chapter 24 Healthcare..Chapter 25 Finance Chapter 26 Entrepreneurs..Chapter 27 Deep Learning Chapter 28 DL Terminologies.Chapter 29 DL For Audio AnalysiChapter 30 Support Vector Machines.Chapter 31 SVM Applications.Chapter 32 SVM Kernel Functions.Chapter 33 Dimensionality Reduction.....Chapter 34 Gradient Boosting Algorithm Chapter 35 XGBoost Chapter 36 XGBoost Algorithm.Chapter 37 AdaBoost Algorithm
Menrva's Summary
THE THESIS
This tutorial introduces the fundamental concepts and applications of Machine Learning. The book aims to guide readers through the process of understanding how algorithms learn from data to make predictions and classifications, drawing upon historical data and past experiences to identify patterns. It covers a broad spectrum of topics, from basic principles and software tools to advanced algorithms and their real-world applications across various industries.
THE SCOPE MAP
- Introduction to Machine Learning principles and its popularity.
- Explanation of basic concepts, software, and tools used in ML.
- Detailed exploration of different types of Machine Learning, including Classification and Clustering.
- In-depth coverage of Artificial Neural Networks (ANNs), Deep Learning (DL), and specific algorithms like Support Vector Machines (SVMs) and Gradient Boosting.
- Practical applications of ML in Education, Healthcare, Finance, and for Entrepreneurs.
- Discussion on the advantages and disadvantages of Machine Learning.
- Guidance on the best ways to learn Machine Learning, including Java Libraries.
THE AUTHORITY PERSPECTIVE
The book appears to be structured as a comprehensive introductory guide to Machine Learning, covering a wide array of topics from foundational concepts to advanced algorithms and their practical uses. The detailed table of contents suggests a thorough exploration of subjects such as ANNs, Deep Learning, SVMs, and various boosting algorithms, indicating a resource aimed at providing a broad overview for learners. The inclusion of chapters on applications in specific sectors like healthcare and finance, along with advice on learning strategies, points to an intent to make the subject accessible and actionable for a diverse audience.
Page Count:
308
Publication Date:
2019-12-23
Publisher:
Independently published
ISBN-10:
1679850059
ISBN-13:
9781679850059
Non-Fiction
Science & Technology
Technology
Machine Learning
Computer Science
Data Science
Community Tags
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