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Training Your Systems with Python Statistical Modeling [Video]

More Information
  • Find correlations in your data using SciPy
  • Train different machine learning models and evaluate their results
  • Make predictions using Naïve Bayes Algorithm with the help of Python code
  • Employ support vector machines for classification and detection
  • Employ ridge and lasso regression models
  • Train a neural network

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. This course takes you through the various different concepts that get you acquainted and working with the different aspects of Machine Learning.

You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics with Pandas. From there, you will be introduced to supervised learning, where you will explore the principles of machine learning and train different machine learning models. Next, you’ll work with binary prediction models, such as data classification using K-nearest neighbors, decision trees, and random forests.

After that, you’ll work with algorithms for regression analysis, and employ different types of regression, such as ridge and lasso regression, and spline interpolation using SciPy. Then, you’ll work on neural networks, train them, and employ regression on neural networks. You’ll be introduced to clustering, and learn to evaluate cluster model results, as well as employ different clustering types such as hierarchical and spectral clustering. Finally, you’ll learn about the dimensionality reduction concepts such as principal component analysis and low dimension representation.

Style and Approach

This course balances in-depth content with tutorials that put the theory into practice. This course will give you both a theoretical understanding and practical examples that show you the art of statistical modeling and training with the help of Python’s various tools and packages.

  • Exploring important aspects of statistical modeling using Python 
  • Filled with real-world, practical examples that show you how to jump in and start building effective prediction models 
  • Covers important concepts such regression analysis and dimensionality reduction with the help of Python
Course Length 4 hours 5 minutes
ISBN 9781788293402
Date Of Publication 31 Jan 2018


Curtis Miller

Curtis Miller is a doctoral candidate at the University of Utah studying mathematical statistics. He writes software for both research and personal interest, including the R package (CPAT) available on the Comprehensive R Archive Network (CRAN). Among Curtis Miller's publications are academic papers along with books and video courses all published by Packt Publishing. Curtis Miller's video courses include Unpacking NumPy and Pandas, Data Acquisition and Manipulation with Python, Training Your Systems with Python Statistical Modelling, and Applications of Statistical Learning with Python. His books include Hands-On Data Analysis with NumPy and Pandas.