Python for Data Analytics

Available Upon request
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Course Objectives

This course will introduce the learner to applied data analytics with Python, focusing
more on the techniques and methods than on the statistics behind these methods. The
course will start with a discussion of how machine learning is different than descriptive
statistics, and the introduction to the scikit learn toolkit.

Description

By the end of the class, students learn to:

  • Identify the difference between a supervised (classification) and unsupervised (clustering) technique
  • Identify which technique they need to apply for a particular dataset and need
  • Engineer features to meet the machine learning needs
  • Write python code to carry out an analysis

Target Audience

This short course intended audiences are IT professionals, data analyst and
professionals who want to learn about Machine Learning with Python programming.

Training Outline

Day 1
  • Introduction to Machine Learning
  • Introduction to Scikit – Learn Package
  • Supervised Learning – Regression
  • Laboratory Exercise
  • Supervised Learning – Classification
  • K-Nearest Neighbour (KNN)
Day 2
  • Supervised Learning – Naïve Bayes
  • Supervised Learning – Logistic Regression
  • Supervised Learning – Support Vector Machine (SVM)
Day 3
  • Supervised Learning – Decision Tree and Random Forest
  • Hyperparameter Model Tuning, Regularization – Ridge and Lasso
  • Unsupervised Learning – Clustering
Day 4
  • Cross Validation and Model Evaluation and Selection
  • Select, Manipulate and Analyze Data
  • Introduction to Ensemble Models e.g. Random Forest, Boosting Models
  • Wrap Up

Prerequisite

Basic knowledge of programming is preferable.