Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.


Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Basic Python, Linux, and linear algebra skills; deep learning helpful

Who should attend & recommended skills

  • Python experienced developers, analysts or others with Python skills who wish to leverage the power of Python and statistical modeling techniques for building accurate predictive models.
  • Skill-level: Foundation-level Training Systems using Python Statistical Modeling is for Intermediate skilled team members. This is not a basic class.
  • Python Developers: Basic (1-2 years’ experience)
  • Linear algebra skills: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Deep learning: Some understanding helpful

About this course

Python’s ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This course takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics. You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This course also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them. By the end of this course, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.

Skills acquired & topics covered

  • Introduction to Python’s rich suite of libraries for statistical modeling
  • Implementing regression, clustering, and training neural networks from scratch
  • Real-world examples on training end-to-end machine learning systems in Python.
  • The importance of statistical modeling
  • Various Python packages for statistical analysis
  • Implementing algorithms such as Naive Bayes, random forests, and more
  • Building predictive models from scratch using Python’s scikit-learn library
  • Implementing regression analysis and clustering
  • How to train a neural network in Python

Course breakdown / modules

  • Technical requirements
  • Computing descriptive statistics
  • Classical inference for proportions
  • Classical inference for means
  • Diving into Bayesian analysis
  • Bayesian analysis for proportions
  • Bayesian analysis for means
  • Finding correlations

  • Principles of machine learning
  • Training models
  • Evaluating models

  • K-nearest neighbors classifier
  • Decision trees
  • Random forests
  • Naive Bayes classifier
  • Support vector machines
  • Logistic regression
  • Extending beyond binary classifiers

  • Linear models
  • Evaluating linear models
  • Bayesian linear models
  • Ridge regression
  • LASSO regression
  • Spline interpolation

  • An introduction to perceptrons
  • Neural networks
  • MLPs for classification
  • MLP for regression

  • Introduction to clustering
  • Exploring the k-means algorithm
  • Evaluating clusters
  • Hierarchical clustering
  • Spectral clustering

  • Introducing dimensionality reduction
  • Principal component analysis
  • Singular value decomposition
  • Low-dimensional representation