Introduction to Data Science and Machine Learning: Session 2

Prerequisites:

Basic Python or any other programming background

Linear algebra

Course Description:

This course is comprised of four lessons, two hours each. It will give you a basic understanding of how to use pandas for data analysis and how to implement linear regression and classification algorithms. We will be scratching the surface only. In order to get the most out of this course, we recommend completing the FREE course on pandas and ML on Kaggle ( link is provided at the end of the page).

Introduction to following core machine learning libraries :

Numpy, Pandas, Matplotlib, Seaborn, ScikitLearn

Session 1: Introduction to Data Analysis with Pandas

• Pandas Series and DataFrames
• indexing and slice Pandas DataFrames columns
• Manipulating columns
• Using .loc[] and .iloc[]
• Dropping rows and columns
• Using Groupby and aggregate functions
• Using .apply functions

Session 2: Data preparation and Visualization with Pandas

• Dealing with duplicate data
• Dealing with missing values
• Dealing with categorical data
• Dealing with text data
• Data Visualization

Session3:Introduction to Machine Learning Part 1 (Implementation of Linear regression algorithm)

United States House price prediction using linear regressing

• Understanding Data
• Building Model
• Evaluating model

Session 4: Introduction to Machine Learning Part 2 (Implementation of Classification algorithm)

• Exploratory Data analysis of Iris dataset
• Correlation and Basic stats
• Data Visualization
• Implementation of KNN classifier

Notes:

Upon successful completion, you will get an attendance certificate.

For Completing you must submit your project (on our chosen dataset) to your GitHub account.

Attend all lessons.

Please go through the following notebook to familiarise yourself with the process

https://www.kaggle.com/dansbecker/your-first-machine-learning-model

We look forward to welcoming you.

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