Machine Learning is the study of computer algorithms to predictions from data without the computer being explicitly programmed to do so. In this course we take a look at the concepts and implementation of various machine learning algorithms in Python.

Requirements

  • Knowledge of python libraies such as Pandas, Matplotlib and Seaborn
  • Fundamental knowledge of mathematics and statistics
  • Laptop
  • Analytic and Logical reasoning.

Who is this course for:

Those who have undergone my Python for Data Analysis track and want to go deeper into machine learning.

Course Outcome:

  • Trainees at the end of the course should be able to use machine learning and deep learning algorithms to make decisions from both structured and unstructured data.

Course Outline:

Linear Regression

Logistic Regression

K-Nearest Neighbours

Decision Trees and Random Forests

Support vector Machines

K-Means Clustering

Principal Component Analysis

Recommender Systems

Natural Language Processing

Big Data and Spark

Deep Learning

Introduction to SciPy

Capstone Project

  • Case Study: Titanic Dataset