In this course, We’ll focus on the algorithms used to create machine learning models. Using clear explanations, simple Python code (no libraries), and step-by-step labs, you’ll discover how to load and prepare data, evaluate your models, and implement a suite of linear and nonlinear algorithms along with assembling algorithms from scratch. You’ll also learn about algorithm applicability along with their limitations and practical use cases..

What You Will Learn :

  • Getting Started with Python and Jupyter
  • Statistics and Probability Refresher and Python Practice
  • Matplotlib and Advanced Probability Concepts
  • Algorithm Overview
  • Predictive Models
  • Applied Machine Learning
  • Recommender Systems
  • Dealing with Data in the Real World
  • Machine Learning on Big Data (with Apache Spark)
  • Testing and Experimental Design
  • GUIs and REST: Build a UI and REST API for your Models

Who should attend ?

  • Business Analysts
  • Data Analysts
  • Developers

Prerequisites :

  • Basic Python skills
  • Good foundational mathematics in linear algebra and probability