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Why us?
- Hands-on Projects
- Real-world problem solving
- Blockchain enabled certificate
Mastering Data Science and ML with Python
Apply core ML algorithms (like Linear Regression and SVM) to real data and perform data wrangling.
100% Online
Difficulty: Intermediate
Taught in English
Mandatory Project
8 hours weekly, over 8 weeks 
UNP Education
Why us?
- Hands-on Projects
- Real-world problem solving
- Blockchain enabled certificate
About the Course
From Python Basics to Real-World AI Solutions — Powered by GenAI
Step into the world of data science and machine learning with this all-in-one Python course designed for beginners and career switchers. Learn everything from Python programming to advanced ML algorithms and real-world project execution — with hands-on guidance, expert mentorship, and the use of cutting-edge Generative AI tools like Gemini, ChatGPT, and GitHub Copilot to supercharge your learning experience.
This course takes you on a comprehensive journey through data science and machine learning using Python — starting from scratch and ending with powerful capstone projects that showcase your skills. You'll learn how to clean and analyze data, build predictive models, apply advanced ML techniques, and present your insights effectively. Plus, you’ll gain the advantage of AI-enhanced coding, documentation, and project execution workflows.
What you'll Learn
- Python programming essentials for data science and ML.
- Data cleaning, transformation & pre-processing techniques.
- Exploratory Data Analysis (EDA) for discovering patterns & insights.
- Predictive modeling for both regression & classification problems.
- Fine-tuning models using cross-validation & hyperparameter tuning.
- Regularization techniques like Lasso and Ridge to prevent overfitting.
- Core ML algorithms: Decision Trees, Random Forests, SVMs, KNN, and more.
- Clustering & dimensionality reduction using unsupervised learning.
- Real-world capstone projects across domains like finance, healthcare, and marketing.
- Best practices to prepare for data science interviews.
- Crafting compelling data stories & stakeholder presentations.
- Leveraging GenAI tools (Gemini, ChatGPT, Copilot) for smarter coding, debugging, and model optimization.
Who should take this Course
- Beginners stepping into data science and AI.
- Professionals transitioning into machine learning and analytics roles.
- IT personnel, engineers, or analysts looking to expand their skill set.
- Students and academics pursuing a career in data-driven domains.
- Anyone preparing for ML & data science job interviews or certifications.
- Learners who want to boost productivity using AI tools in their workflows.
What You’ll Get from This Course
- Step-by-step lessons blending theory and hands-on coding.
- Real-world datasets & projects to build a strong portfolio.
- Personalized feedback & 1-on-1 mentorship from industry experts.
- Mock interview prep, resume review & job readiness support.
- Integration of AI-powered tools (Gemini, Copilot, ChatGPT) to learn and code faster.
- Skills aligned with top certifications and industry expectations.
- Lifetime access to updated videos, resources, and support.
- Practical guidance on presenting your work with confidence.
Capstone Projects by Our Students
NCreated by: Niveditha
PSCreated by: Pyata Saiprasanna
SLCreated by: Sri Laxmi
NSCreated by: Nalam Shivani09
CACreated by: Chandana Anandarao
PrCreated by: Purnima ramana
VDCreated by: Vanamshetty Deepika
BICreated by: Bhavani Itharaveni
PSCreated by: Pasala Supriya
Contact Us
Emaillearn@unp.education
Call Us+1.929.288.1787 (USA) +91.800.829.1301 (India)
Course Content
- 1.1. Exploring Machine Learning and its types
- 1.2. Install Anaconda
- 1.3. Python and Jupyter Demo
- 1.4. Data Science - Quiz
- 2.1. Reading from a CSV
- 2.2. Pandas Use Case 1
- 2.3. Pandas Use Case 2
- 2.4. Pandas Use Case 6
- 2.5. EDA Quiz-1
- 2.6. EDA Quiz-2
- 2.7. Data Preprocessing - Quiz
- 3.1. What is linear regression?
- 3.2. The advertising dataset
- 3.3. Simple Linear Regression
- 3.4. R squared
- 3.5. Multiple linear regression
- 3.6. Model evaluation
- 3.7. Handling categorical features
- 3.8. Linear Regression Quiz 1
- 3.9. Linear regression Quiz 2
- 4.1. Predicting a categorical response
- 4.2. Using logistic regression
- 4.3. What is logistic regression?
- 4.4. Interpreting logistic regression Coefficients
- 4.5. Logistic Regression Quiz 1
- 4.6. Logistic Regression Quiz 2
- 5.1. Train/test split
- 5.2. Cross Validation Quiz 1
- 5.3. Cross Validation Quiz 2
- 6.1. Overfitting
- 6.2. Overfitting with linear models
- 6.3. Regularization Quiz 1
- 6.4. Regularization Quiz 2
- 7.1. Machine Learning with KNN: Theory Explained
- 7.2. KNN Regression from Scratch in Python
- 7.3. KNN Classification from Scratch in Python
- 7.4. K Nearest Neighbor Quiz 1
- 7.5. K Nearest Neighbours Quiz 2
- 8.1. Decision Tree
- 8.2. Boosting
- 8.3. Bagging
- 8.4. Decision Tree Quiz
- 9.1. SVM Theory Explained: A Deep Dive into Support Vector Machines
- 9.2. SVM Regression with Python: A Comprehensive Guide
- 9.3. SVM Classification with Python: A Comprehensive Guide
- 9.4. Support Vector Machine Quiz 1
- 9.5. Support Vector Machine Quiz 2
- 10.1. Detailed Overview of K-Means Clustering
- 10.2. Mastering K-Means Clustering with Python: A Detailed Guide
- 10.3. Clustering Quiz 1
- 10.4. Clustering Quiz 2
Upcoming Batches
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