
Machine Learning Algorithms
Description
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- [*] Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
- [*] Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.
Book DescriptionIn this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering. In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously. On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problemWhat you will learn - [*] Acquaint yourself with the important elements of machine learning
- [*] Understand the feature selection and feature engineering processes
- [*] Assess performance and error trade-offs for linear regression
- [*] Build a data model and understand how it
- [*] Learn to tune the parameters of SVMs
- [*] Implement clusters in a dataset
- [*] Explore the concept of Natural Processing Language and Recommendation Systems
- [*] Create a machine learning architecture from scratch
Who this book is forThis book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.
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Content
- Important Elements In A Machine Learning
- Feature Selection & Feature Engineering
- Linear Regression
- Logistic Regression
- Naïve Baiyes
- Support Vector Machines
- Decision Trees And Random Forests
- K-Means
- Heirarchical Clustering
- Introduction To Recommedation Systems
- Introduction To Natural Language Processing
- Topic Modelling and Sentiment Analysis in NLP
- Brief Introduction To Deep Learning And Tensorflow
- Creating a Machine Learning Architecture
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