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Master Data Science & Machine Learning in Python

17 hrs 136 coding exercises 6 projects

Master the most in-demand Data Science and Machine Learning skills. Learn data analysis, predictive modelling, and feature engineering. Build intelligent ML solutions to solve complex real-world business challenges.

$90 $180 50% off

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Preview Course

Resolve doubts with AI Mentor

Guided Projects for hands-on learning

Coding Exercises with AI hints

Personalised Mock Interviews

Skills you will gain

Numpy

Pandas

Seaborn

Scikit Learn

Linear Regression

Logistic Regression

Decision Trees

Ensemble techniques

K-means clustering

Feature engineering

Model tuning

AI-powered support to help you learn

AI Mentor
Resolve doubts using AI Mentor
AI Mentor answers questions, explains topics, and provides examples to help you understand concepts better.

Coding Exercises
Practice coding exercises effortlessly with AI-powered hints
Get real-time AI guidance while coding. Debug, enhance solutions & learn with personalised feedback on popular languages.

Mock Interviews
Get personalised feedback and prepare for roles in tech
Enhance your professional skills with AI-powered feedback and build confidence for job interviews.

Get exclusive access to premium features and personalised learning

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Guided Projects

Solve real-world projects with a step-by-step guide, starter code templates, and access to model solutions to boost your skills and build a standout resume.

GUIDED PROJECT 1
Identify potential customers for loans
This project is about a Thera Bank which has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).

Gen AI concepts

Prompt Engineering

Chatgpt

Langchain

AI for business solutions

GUIDED PROJECT 2
Exploratory Data Analysis on Movielens dataset
In this project, we will dive into the MovieLens dataset, a rich collection of user ratings, movie information, and genres. Our objective is to perform a thorough analysis of the data, uncover key insights, and present these findings through visually compelling charts.

Exploratory Data Analysis

Python

Imputation

Data Pre processing

GUIDED PROJECT 3
Income Level Prediction with Random Forests
Build a predictive model using Random Forests to classify individuals' income as either <=50K or >50K based on demographic and employment data. Explore binary classification for real-world applications like marketing and policy-making.

Data preprocessing

Random Forests

Binary Classification

Model Evaluation Metrics

Hyperparameter Tuning

GUIDED PROJECT 4
Customer Segmentation for Credit Cards
Utilize customer data to segment individuals into actionable groups based on behavior, such as credit utilization and engagement levels. Leverage clustering techniques to drive targeted marketing and enhance customer retention strategies.

Customer Segmentation

Clustering Algorithms

Feature Engineering

Data Visualization

Actionable Insights Generation

GUIDED PROJECT 5
Interactive Revenue Prediction System
Develop a dynamic system using Linear Regression to predict company revenue based on user inputs. Includes data preprocessing, model evaluation, and an intuitive interface for real-time engagement with the model.

Linear Regression

Data Preprocessing

Model Evaluation

GUIDED PROJECT 6
Loan Approval Prediction System
Build a Logistic Regression-based classifier to predict loan approval using applicant and loan-specific features. This interpretable model aids lending institutions in making smarter, faster, and unbiased decisions.

Logistic Regression

Data Preprocessing

Binary Classification

Model Evaluation

Interpretability in Machine Learning

Industry-focused curriculum

Key Python Libraries - Numpy

6 Videos

20 Coding Exercises

Numpy operations such as array indexing and slicing and advanced functions like arithmetic, concatenation, and splitting

6 videos

25 mins

Introduction to Numpy
Indexing an Array
Slicing an Array
Operations on an Array
Arithmetic Functioning in Numpy
Concatenation of Array

20 Coding Exercises

Coding Exercise on Numpy - Beginner
Coding Exercise on Numpy - Intermediate

Preview module

Key Python Libraries - Pandas

14 videos

17 Coding Exercises

Introduction to Pandas, data structures and data manipulation

14 videos

1 hour and 46 mins

Introduction to Pandas
Introduction to Data Structures
Introduction to Pandas Series and Creating Series
Manipulating Series
Introduction to Dataframes and Creating Dataframe
Manipulating the Dataframes
Reading Data From Different Sources
Concatenate
Merging and Joining a Dataframe
Re-shaping the Dataframe
Pivot Table
Duplicate
Map and Reduce
Group-by in Pandas

17 Coding Exercises

Coding Exercise on Pandas - Intermediate
Coding Exercise on Pandas - Intermediate

Python Visualization using Seaborn & Matplotlib

14 videos

14 Coding Exercises

Introduction to Visualization Libraries

14 videos

40 mins

Introduction to Visualization Libraries
Line Plot
Scatter Plot
Bar Plot
Pie Plot
Histogram Plot
Box Plot
Strip Plot
Swarm Plot
Violin Plot
Pair Plot
Distribution Plot
Heat Map
Count Plot

14 Coding Exercises

Coding Exercise on Visualization - Beginner
Coding Exercise on Visualization - Advance

EDA for Data Science

12 videos

17 Coding Exercises

Data preprocessing techniques, and hands-on case studies to analyze, clean, and transform data effectively.

12 videos

2 hours and 15 mins

Introduction to EDA
Descriptive Data Measures
5-Point Summary and Skewness of Data
Box-Plot, Covariance and Coeff of Correlation
Let's Get Our Hands Dirty with Code
Univariate and Multivariate Analysis
Encoding Categorical Data
Scaling and Normalization
What is Preprocessing?
Imputing Missing Values
Working with Outliers
Case Study Analysis

17 Coding Exercises

Coding Exercise on EDA - Beginner
Coding Exercise on EDA - Intermediate

Introduction to Machine Learning

9 videos

Scikit-learn and it's application, with a practical demo in Python.

9 videos

1 hour

Introduction to Machine Learning
Steps of Machine Learning
Introduction to Scikit Learn
What is Scikit learn?
Installing Scikit learn
Support for Algorithms
Applications of Scikit learn
Advantages and Disadvantages
Practical Demo in Python

Supervised Learning - Linear Regression

11 videos

17 Coding Exercises

Supervised Learning, Linear Regression and Exploratory Data Analysis

11 videos

2 hours and 50 mins

Supervised machine learning - Introduction
Linear regression and its Pearson’s coefficient
Linear regression mathematically and coefficient of Determinant
Brief Scenario of Dataset and Descriptive analysis
Analyse the Distribution - Dependent column
Missing Values Imputation
Bivariate analysis using plots through Seaborn function
Building model using all Information
Exploratory Data Analysis (EDA)
Model Analysis and Squared errors
Summary and Lab exercise of linear regression

17 Coding Exercises

Coding Exercise on Linear Regression - Beginner
Coding Exercise on Linear Regression - Advance

Supervised Learning - Logistic Regression

3 Videos

7 Coding Exercises

Logistics Regression concepts including Sigmoid Functions, Confusion Matric, Precision, Recall etx

3 Videos

1 Hour 30 Minutes

Classification Algorithm: Logistic Regression
Logistic Regression Model and Sigmoid Function
Logistic Regression: Confusion Matrix, Precision, and Recall (Hands-on)

7 Coding Exercises

Beginner-Level Coding Exercise on Logistic Regression
Advanced-Level Coding Exercise on Logistic Regression

Supervised Learning - Naive Bayes Classifier

4 videos

1 Coding Exercise

Bayes theorem, Naive Bayes classifier and hands-on

4 videos

1 hour

Bayes Theorem
Introduction to Naive Bayes Classifier
Introduction to Naive Bayes Classifier and Examples
Naive Bayes - Hands-on

1 Coding Exercise

Naive Bayes Coding Exercise

Supervised Learning - Decision Trees

6 videos

13 Coding Exercises

Decision Tree - CART algorithm, Entropy, Gini Index.

6 videos

1 Hour and 10 mins

Decision Trees introduction
Decision Trees CART Algorithm
Loss Function- Entropy
Loss Function - Gini
Decision Trees - Conclusion
Decision Trees - Hands-on

13 Coding Exercises

Decision Trees Coding exercises - Beginner
Decision Trees Coding exercises - Advanced

Ensemble Techniques

9 videos

19 Coding Exercises

Bagging, Boosting, Random Forest with hands on.

9 videos

1 hour and 10 mins

Ensemble Methods
Bagging
Bagging - Hands on
Boosting
Types of Boosting
Adaboosting - Hands on exercise
Gradient Boosting - Lab exercise
Random Forest
Random Forest - Hands on exercise

19 Coding Exercises

Coding Exercise Bagging
Coding Exercise Adaboosting - Beginner
Coding Exercise Adaboosting - Advance
Coding Exercise Gradient Boosting - Advanced
Coding Exercise Random Forest - Beginner

Unsupervised Learning

5 videos

3 Coding Exercises

Clustering, K-means, Elbow Method, PCA, with hands-on

5 videos

1 hour

Unsupervised Learning
Clustering - Types and Distance
Clustering - Distance Calculations
K-Means Clustering
Elbow Method

3 Coding Exercises

Coding Exercise on K-Means Clustering - Beginner
Coding Exercise on K-Means Clustering - Intermediate

Featurization

9 videos

2 Coding Exercises

Feature Engineering, K-Fold, Cross validation, Up and down sampling with hands on.

9 videos

2 hours

Introduction to Feature Engineering
Hands on exercise - Feature engineering
Cross validation concept and procedure
Implementing K Fold Cross Validation
Some salient features of K-fold
Bootstrap Sampling Concept and Hands-on
Leave one out Cross Validation (LOOCV) Concept
Hands-on Implementation of LOOCV Technique
Up sampling and down sampling

2 Coding Exercises

Coding Exercise on Feature Engineering and Cross Validation - Beginner

Model Performance Measures

5 videos

2 Coding Exercises

Model tuning, Hyper Parameter Tuning, Grid Search, RandomSearch with hands on.

5 videos

55 mins

Model Tuning and Performance
Hyper parameters and Tuning
GridSearch
RandomizedSearch CV
Hands on exercise on RandomizedSearch CV and GridSearch CV

2 Coding Exercises

Coding Exercise on Grid Search - Beginner

Guided Project 1: Income Prediction using Random Forest

Guided Project 1

Income Prediction using Random Forest : Functional requirements and step-by-step guide

Guided Project 2: Customer Clustering

Guided Project 2

Customer Clustering : Functional requirements and step-by-step guide

Guided Project 3 : Revenue Prediction

Guided Project 3

Revenue Prediction : Functional requirements and step-by-step guide

Guided Project 4: Loan Approval using Logistic Regression

Guided Project 4

Loan Approval using Logistic Regression : Functional requirements and step-by-step guide

Guided Project 5: Loan Approval Model using Decision Trees

Guided Project 5

Loan Approval Model using Decision Trees : Functional requirements and step-by-step guide

Guided Project 6: Movielens Exploratory Data Analysis

Guided Project 6

Movielens Exploratory Data Analysis : Functional requirements and step-by-step guide

Machine Learning Engineer - Mock Interview

1 Mock Interview

Personalised Mock Interviews to help you land a Machine Learning Engineer role

Course instructors

Prof. Mukesh Rao

Director, Academics, Great Learning

Prof. Mukesh Rao is a senior faculty of Data Science in Great Learning and he is responsible for designing data science courses offered and mentoring students with capstone projects. Prof. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science and has conducted extensive corporate training in Data Science and Big Data. He also works as a Data Science Trainer & Consultant for 4v Technologies and conducts training in core big data technologies and data science. He has headed Big Data teams at SourceOne and has worked with tech giants like Wipro Technologies.

Prof. Mukesh Rao

Director, Academics, Great Learning

Dr. Abhinanda Sarkar

Academic Director - Data Science & Machine Learning

Dr. Abhinanda Sarkar has B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He was a lecturer at Massachusetts Institute of Technology (MIT) and a research staff member at IBM. Post this he spent a decade at General Electric (GE). He has provided committee service for the University Grants Commission (UGC) of the Government of India, for infoDev – a World Bank program, and for the National Association of Software and Services Companies (NASSCOM). He is a recipient of the ISI Alumni Association Medal, an IBM Invention Achievement Award, and the Radhakrishan Mentor Award from GE India. He is a seasoned academician and has taught at Stanford, ISI Delhi, the Indian Institute of Management (IIM-Bangalore), and the Indian Institute of Science. Currently, he is a Full-Time Faculty at Great Lakes. He is Associate Dean at the MYRA School of Business where he teaches courses such as business analytics, data mining, marketing research, and risk management. He is also co-founder of OmiX Labs – a startup company dedicated to low-cost medical diagnostics and nucleic acid testing.

Dr. Abhinanda Sarkar

Academic Director - Data Science & Machine Learning

Mr. Bharani Akella

Data Scientist

Bharani has been working in the field of data science for the last 2 years. He has expertise in languages such as Python, R and Java. He also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow. He has been in the technical content side from last 2 years and has taught numerous classes with respect to data science.

Mr. Bharani Akella

Data Scientist