Clustering and Classification with Machine Learning in Python

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

Key Features

Explore the most important Python data science concepts and packages, including Pandas
Master the Anaconda framework and use it to implement clustering and classification models on your data
Get to grips with data science fundamentals and understand which models should be used when

What You Will Learn

Harness the power of Anaconda/iPython for practical data science
Read data into the Python environment from different sources
Carry out basic data preprocessing and wrangling in Python
Implement unsupervised/clustering techniques such as k-means clustering
Get to grips with dimensionality reduction techniques and feature selection
Implement supervised learning/classification techniques such as random forests
Explore neural network- and deep learning-based classification

About

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning.

The course consists of 7 sections that will help you master Python machine learning. You’ll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you’ll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You’ll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You’ll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you’ll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you’ll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification.

By the end of this course, you'll be able to use packages such as NumPy, Pandas, and Matplotlib to work with real data in Python.

All code and supporting files for this course are available at

https://github.com/sanjanapackt/PacktPublishing-Clustering-and-Classification-with-Machine-Learning-in-Python.