English | 21 Oct. 2017 | ASIN: B076NX6KY7 | 48 Pages | AZW3 | 1.98 MB

If you want to take a grasp of machine learning in an easy-to-follow manner, with many helpful visual illustrations, then this e-book is certainly for you.

In an introductory chapter, you will find:

What machine learning is and what types of problems it is suitable for;

Different types of machine learning;

Features in datasets;

Data normalization;

Dimensionality of a dataset;

The ‘curse’ of dimensionality;

Overfitting and underfitting;

Clustering

Then, in the following chapters, you will learn how to implement all these concepts in practice with clustering algorithms.

If you are already familiar with data science, this e-book can also be helpful for you because it presents detailed explanations with visual representations for several widely-used clustering approaches:

Hierarchical agglomerative clustering;

K-means;

DBSCAN;

Neural networks-based clustering

You will learn different strengths and weaknesses of these algorithms as well as the practical strategies to overcome the weaknesses. In addition, we will briefly touch upon some other clustering methods.

The examples of the algorithms are presented in Python 3. We will work with several datasets, including the ones based on real-world data.

We will be primarily working with the Scikit-learn and SciPy libraries. But our neural network for clustering, we will build basically from scratch, just by using Numpy arrays.