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Training Systems using Python Statistical Modeling

  • Course Code: Artificial Intelligence - Training Systems using Python Statistical Modeling
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Leverage the power of Python and statistical modeling techniques for building accurate predictive models.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Training Systems using Python Statistical Modeling skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Leverage the power of Python and statistical modeling techniques for building accurate predictive models.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Deep Learning with PyTorch expert instructor, students will learn about and explore: 

  • Get introduced to Python’s rich suite of libraries for statistical modeling 
  • Implement regression, clustering and train neural networks from scratch 
  • Includes real-world examples on training end-to-end machine learning systems in Python. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand the importance of statistical modeling 
  • Learn about the various Python packages for statistical analysis 
  • Implement algorithms such as Naive Bayes, random forests, and more 
  • Build predictive models from scratch using Python’s scikit-learn library 
  • Implement regression analysis and clustering 
  • Learn how to train a neural network in Python 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Leverage the power of Python and statistical modeling techniques for building accurate predictive models 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python as well as basic linear algebra skills.  
  • Some understanding of deep learning will be helpful 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Classical Statistical Analysis 
  • Classical Statistical Analysis 
  • 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 
  1. Introduction to Supervised Learning 
  • Introduction to Supervised Learning 
  • Principles of machine learning 
  • Training models 
  • Evaluating models 
  1. Binary Prediction Models 
  • Binary Prediction Models 
  • K-nearest neighbors classifier 
  • Decision trees 
  • Random forests 
  • Naive Bayes classifier 
  • Support vector machines 
  • Logistic regression 
  • Extending beyond binary classifiers 
  1. Regression Analysis and How to Use It 
  • Regression Analysis and How to Use It 
  • Linear models 
  • Evaluating linear models 
  • Bayesian linear models 
  • Ridge regression 
  • LASSO regression 
  • Spline interpolation 
  1. Neural Networks 
  • Neural Networks 
  • An introduction to perceptrons 
  • Neural networks 
  • MLPs for classification 
  • MLP for regression 
  1. Clustering Techniques 
  • Clustering Techniques 
  • Introduction to clustering 
  • Exploring the k-means algorithm 
  • Evaluating clusters 
  • Hierarchical clustering 
  • Spectral clustering 
  1. Dimensionality Reduction 
  • Dimensionality Reduction 
  • Introducing dimensionality reduction 
  • Principal component analysis 
  • Singular value decomposition 
  • Low-dimensional representation 
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