Introduction to Machine Learning


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The world today has witnessed some of the great successes of machine learning applications. We all have awed at the working of self-driving Google car and relied on online recommendation engines of Netflix and e-commerce sites. These are essentially based on state-of-the-art machine learning algorithms. Because of the advancements in computing technologies and exposure to huge amounts of data, the applicability of machine learning has dramatically increased.

The aim of machine learning is to make automated models such that they learn from data that is fed and produce reliable predictions and decisions without much human intervention. Although this technology is not new, the ability to apply complex mathematical calculations on big data has given a considerable momentum to this field in recent times.

This guide explains what machine learning really is and how it helps to understand a deep meaning in data with patterns and forecasts. It also contains details on different machine learning techniques to help you get started.

This guide will help you:

– Understand machine learning and what it can do
– Assess which technique is right for your data?
– Evaluate the best ways to use it
– Decide the best way to implement machine tools in your organization
– Examples using R
– Success Stories
– Free Datasets

Additional information


Corporate License, Single User License

Table of Content

1. Introduction
2. Executive Summary
3. Types of Machine Learning
3.1 Supervised
3.2 Unsupervised
3.3 Reinforcement
3.4 Semi-supervised
4. Top Machine Learning Techniques
5. Will Machine Learning work for me?
6. How can I implement machine learning?
7. Conclusion and key takeaways

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