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Complete Introduction to Data Science and Machine Learning from Basic to Advanced.

What you will learn

Students will have develop understanding of libraries used for Data Analysis like Pandas and Numpy.

Learn to create impactful visualizations using Matplotlib and Seaborn. By creating these visualizations you will be able to derive better conclusions from data.

After this course you will learn to build complete Data Science Pipeline from Data preparation to building the best Machine Learning Model.

The course contains practical section after every new concept discussed and the course also has two projects at the end.


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  • Learn how to use Numpy and Pandas for Data Analysis. This will cover all basic concepts of Numpy and Pandas that are useful in data analysis.
  • Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.
  • This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.
  • Learn about different Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression etc… All the video sessions will first discuss the basic theory concept behind these algorithms followed by the practical implementation.
  • Learn to how to choose the best hyper parameters for your Machine Learning Model using GridSearch CV. Choosing the best hyper parameters is an important step in increasing the accuracy of your Machine Learning Model.
  • You will learn to build a complete Machine Learning Pipeline from Data collection to Data Preprocessing to Model Building. ML Pipeline is an important concept that is extensively used while building large scale ML projects.
  • This course has two projects at the end that will be built using all concepts taught in this course. The first project is about Diabetes Prediction using a classification machine learning algorithm and second is about prediciting the insurance premium using a regression machine learning algorithm.


Welcome and Course Overview

Course Overview


Numpy Introduction and Installation
Creating Arrays in Numpy
Array Shape and Reshape
Array Indexing
Array Iterating
Array Slicing
Searching and Sorting


Pandas Introduction and Installation
Pandas Series
Pandas DataFrame
Pandas ReadCSV
Pandas Analyzing DataFrames

Data Visualization

Matplotlib Introduction
Different types of plots in Matplotlib

Data Preparation

Handling Missing Values
Feature Encoding
Feature Scaling

Machine Learning

Machine Learning Introduction
Supervised Machine Learning
Unsupervised Machine Learning
Train Test Split
Regression Analysis
Linear Regression
Logistic Regression
Decision Tree
Random Forest
K Means Clustering
GridSearch CV

Machine Learning Pipeline

Machine Learning Pipeline


Diabetes Prediction
Insurance Cost Prediction