Machine Learning Essentials with Python

This course aims to introduce the principles and practice of Machine Learning using Python Libraries. You’ll learn how to use popular Python data science libraries, implement Big Data & Machine Learning solutions, and more. The essential features of Machine Learning are explored with practical examples. Machine Learning will be demystified.


3 days


  • Approx. 6 months Python experience

What you’ll learn

  • Recap Essential Python Features
  • Python data science techniques
  • Python Big Data
  • The principles of machine learning
  • Making forecasts from training data
  • Machine learning case studies

Course details

Recap Essential Python Features 

  • Language Fundamentals
  • Functions
  • Data Structures
  • Defining and Using Packages
  • Additional Techniques

Getting Started with Python Data Science and NumPy

  • Introduction to Python Data Science
  • NumPy Arrays
  • Manipulating Array Elements
  • Manipulating Array Shape

NumPy Techniques

  • NumPy Universal Functions
  • Aggregations
  • Broadcasting
  • Manipulating Arrays using Boolean Logic
  • Additional Techniques

Getting Started with Pandas

  • Introduction to Pandas
  • Creating a Series
  • Using a Series
  • Creating a DataFrame
  • Using a DataFrame

Pandas Techniques

  • Universal Functions
  • Merging and Joining Datasets
  • A Closer Look at Joins

Working with Time Series Data

  • Introduction to Time Series Data
  • Indexing and Plotting Time Series Data
  • Testing Data for Stationarity
  • Making Data Stationary
  • Forecasting Time Series Data
  • Scaling Back the ARIMA Results

Introduction to Machine Learning

  • Machine Learning Concepts
  • Classification
  • Clustering

Getting Started with Scikit-Learn

  • Scikit-Learn Essentials
  • A Closer Look at Datasets

Understanding the Scikit-Learn API

  • Introduction
  • Scikit-Learn API Essentials
  • Performing Linear Regression

Going Further with Scikit-Learn

  • Introduction
  • Understanding Naïve Bayes Classification
  • Naïve Bayes Example using Scikit-Learn

Case Study

  • Worked example of a real-world data science problem


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