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Python for Data Science and Machine Learning

Master Data Science & Machine Learning in Python

17 hrs 136 coding exercises 6 projects

Learn data analysis, machine learning algorithms, predictive modeling, and feature engineering. Enroll in this Python for Data Science and Machine Learning course and develop intelligent solutions for real-world challenges.

Master Data Science & Machine Learning in Python

17 hrs 136 coding exercises 6 projects
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Skills you will gain

Numpy

Pandas

Seaborn

Scikit Learn

Linear Regression

Logistic Regression

Decision Trees

Ensemble techniques

K-means clustering

Feature engineering

Model tuning

+6 More

About this course

This Python for Data Science and Machine Learning course lays the foundation in Python, with essential libraries such as NumPy and Pandas for data manipulation. You’ll learn to visualize data using Seaborn and Matplotlib and gain hands-on experience with machine learning algorithms, including Linear and Logistic Regression, Decision Trees, Random Forests, and K-Means Clustering. The course covers key techniques like feature engineering, model evaluation, and performance metrics to help you build and assess predictive models effectively.

By the end of the course, you'll be able to apply machine learning models to real-world challenges such as customer segmentation, income prediction, and loan approval. Through hands-on projects, you'll gain practical experience in data preprocessing, model building, and evaluation. These projects will prepare you to generate actionable insights and make data-driven decisions, making you ready to solve complex business problems and excel in data science or analytics roles.



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Course outline

Industry focussed curriculum designed by experts

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
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6 videos

25 mins

  • Introduction to Numpy
  • Indexing an Array
  • Slicing an Array
  • Operations on an Array
  • Arithmetic Functioning in Numpy
  • Concatenation of Array
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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
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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
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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
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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
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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.
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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
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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.
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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
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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
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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
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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)
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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
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4 videos

1 hour

  • Bayes Theorem
  • Introduction to Naive Bayes Classifier
  • Introduction to Naive Bayes Classifier and Examples
  • Naive Bayes - Hands-on
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1 Coding Exercise

  • Naive Bayes Coding Exercise

Supervised Learning - Decision Trees

6 videos

13 Coding Exercises

Decision Tree - CART algorithm, Entropy, Gini Index.
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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
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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.
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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
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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
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5 videos

1 hour

  • Unsupervised Learning
  • Clustering - Types and Distance
  • Clustering - Distance Calculations
  • K-Means Clustering
  • Elbow Method
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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.
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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
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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.
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5 videos

55 mins

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

2 Coding Exercises

  • Coding Exercise on Grid Search - Beginner

Guided Project 1: Income Prediction using Random Forest

Project Certificate

Income Prediction using Random Forest : Functional requirements and step-by-step guide
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Guided Project 2: Customer Clustering

Project Certificate

Customer Clustering : Functional requirements and step-by-step guide
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Guided Project 3 : Revenue Prediction

Project Certificate

Revenue Prediction : Functional requirements and step-by-step guide
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Guided Project 4: Loan Approval using Logistic Regression

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Loan Approval using Logistic Regression : Functional requirements and step-by-step guide
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Guided Project 5: Loan Approval Model using Decision Trees

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

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Guided Projects

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

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  • 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

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Course Instructors

instructor img
instructor img

Prof. Mukesh Rao

Senior Faculty, 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.
instructor img
instructor img

Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great 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.
instructor img
instructor img

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.

Master Data Science & Machine Learning in Python

17 hrs 136 coding exercises 6 projects

Master Data Science & Machine Learning in Python

17 hrs 136 coding exercises 6 projects
Start 7-Day Free Trial Subscribe To Pro+ • $25/Month
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Get course + certificate with Pro+ subscription

Our Learners Experience

    Reviewer Profile
    Naman Chouhan

    5.0

    ★★★★ ★
    An Absolutely Mind-Blowing course to the Future with Gen AI!
    Exceptional course! It brilliantly demystifies Gen AI with clear explanations and practical projects. The hands-on approach built real confidence. Worth every penny and highly recommended for anyone curious about AI.
    Read more
    Reviewer Profile
    Parveen Kumar

    5.0

    ★★★★ ★
    The course improved my technical knowledge and gave me a strong foundation in networking concepts.
    Course was very informative and well-structured. The concepts such as OSI model, TCP/IP, IP addressing, routing, and network protocols were explained clearly with good examples. The practical demonstrations and diagrams helped in understanding the real-time working of networks. Overall, the course improved my technical knowledge and gave me a strong foundation in networking concepts.
    Read more
    Reviewer Profile
    Raaj Rane

    4.0

    ★★★ ★ ☆
    Data visualization with PowerBI
    Really good depth to the topic. Got an opportunity to learn a lot. Maybe add the exact excel data sheet as the one that is being used as the instructor so we can understand even better.
    Read more
    Reviewer Profile
    Advait Shirodkar

    5.0

    ★★★★ ★
    Master Data Analytics in Excel
    Course had a good structure and focused on fundamentals and practical applications.
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    Reviewer Profile
    Sanika Todkari

    5.0

    ★★★★ ★
    Strong Foundation
    This course provided a strong understanding of core Android development concepts. The modules were well-organized, and the instructor explained topics like activities, layouts, intents, and APIs very clearly. The practical assignments and demonstrations helped me build confidence in creating Android applications. Overall, it was a valuable and well-designed learning experience.
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    Reviewer Profile
    Dharani Priya G

    5.0

    ★★★★ ★
    I really enjoyed this course!
    The lessons were clear, beginner-friendly, and well-structured. The practical hands-on exercises helped me understand how to craft effective prompts and avoid common mistakes. The examples, refined prompts, and advanced prompt techniques were especially useful. A great starting point for anyone interested in Generative AI and ChatGPT.
    Read more
    Reviewer Profile
    Anujkumar singh

    4.0

    ★★★ ★ ☆
    The topic was explained in depth
    This course is trending and helps many to enter into AI and analytics
    Read more
    Reviewer Profile
    Aiman Shaikh

    5.0

    ★★★★ ★
    Mastering digital marketing
    It's really amazing i easily understand this course with the help of great learning and great couches amazing team thank you so much for helping me to learn this course.
    Read more
    Reviewer Profile
    Minal

    5.0

    ★★★★ ★
    Excellent Course for Beginners
    This online course exceeded my expectations. The content was detailed yet easy to understand, and the instructor used real-world examples to make concepts relatable. The platform was user-friendly, and each module was organized logically. The quizzes and case studies helped test my understanding. I would recommend this course to anyone looking to upskil
    Read more
    Reviewer Profile
    Nandana Raveendran

    5.0

    ★★★★ ★
    Data visualization using Tableau
    This course was easy to understand and well structured. I especially liked the practical explanations, quizzes, and tools used to explain data visualization concepts. It helped me gain basic confidence in Tableau and understand how to create effective dashboards. A good course for beginners.
    Read more

Frequently Asked Questions

What will I learn in this Master Data Science & Machine Learning course?

This course covers job-ready, in-demand skills such as data preprocessing, exploratory data analysis, machine learning algorithms, deep learning basics, model evaluation, and hands-on projects using Python libraries like Pandas, NumPy, Scikit-Learn, and TensorFlow. These skills are essential for careers in data science, machine learning engineering, and AI-driven roles.

What will I learn in this Machine Learning using Python course?

In this premium Python for data science and machine learning course, you will gain in demand skills, such as data preprocessing, exploratory data analysis, machine learning algorithms, deep learning basics, model evaluation, and hands-on projects using Python libraries such as Pandas, NumPy, Scikit-Learn, and TensorFlow.

What is the course duration, and what support will I get?

The Python for Machine Learning course includes 17 hours of video lessons, 136 hands-on coding exercises, and 6 projects. You will also receive 24/7 technical support and a dedicated mentor to guide you through the course content.



Will beginners benefit from this Python for Machine Learning course?

Yes, this course is designed for beginners with basic Python knowledge. It starts with the fundamentals of data science and machine learning, gradually building the skills needed to solve real-world problems.

How is Great Learning Academy’s premium Python for Data Science and Machine

This premium Python for Data Science course provides in-depth content, structured projects, personalized mentorship, and an industry-recognized certificate, offering a more comprehensive learning experience compared to the free courses.

Do I need prior experience in programming or data science to take this course?

While prior knowledge of Python and basic statistics is helpful, the course includes beginner-friendly modules to help you get started, making it suitable for both beginners and intermediate learners.

What skills will I gain by completing this course?

By completing this Python for Machine Learning course, you will gain the following skills:

  • Numpy

  • Pandas

  • Seaborn

  • Scikit Learn

  • Linear Regression

  • Logistic Regression

  • Decision Trees

  • Ensemble techniques

  • K-means clustering

  • Feature engineering

  • Model tuning


Do I receive a certificate after completing the course?

Yes, upon completion of the course with an active Academy Pro Plus subscription, you will receive a digital certificate that you can add to your LinkedIn profile and include on your resume.

How is this Pro course different from free Data Science courses on Great Learning Academy?

This Pro course provides structured, in-depth personalized learning with hands-on guided projects, AI-powered tools, mock interviews, and an industry-recognized certification. It offers practical applications and advanced concepts that go beyond the basics covered in free courses, helping you build real-world expertise and confidently prepare for job opportunities.

What is the refund policy for Pro courses at Great Learning?

All fees paid for Great Learning Pro courses are non-refundable. Once a payment is made towards enrollment in a Great Learning Pro course, the amount will not be refunded, whether the user completes, partially completes, or does not access the course.

Gain work-ready skills with an Academy Pro+ subscription

Subscribe To Pro+ • $25/Month

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