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Machine Learning with Python and Scikit-Learn
Learn Machine Learning with Python. Master supervised & unsupervised algorithms, model evaluation, and Scikit-Learn with real-world projects.
Available Coaching Centers:
Request your Demo Today
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Stem & Robotics Machine Learning is the backbone of modern AI, predictive analytics, and data-driven decision-making.
This course teaches you machine learning with Python using Scikit-Learn, one of the most widely used ML libraries. You’ll cover supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, and real-world applications.
With live mentor-led coding labs and pre-recorded tutorials, you’ll gain practical experience by applying ML to real datasets. By the end, you’ll build and deploy a capstone ML project.
Target Audience
- Students and professionals starting in AI/ML
- Data analysts moving into machine learning
- Developers integrating ML into applications
- Learners preparing for ML/AI careers
Prerequisites
- Python programming (NumPy, Pandas basics)
- Basic math & statistics (linear algebra, probability helpful)
Learning Outcomes
By completing this course, learners will:
- Understand ML fundamentals & workflows
- Implement supervised and unsupervised ML algorithms
- Use Scikit-Learn for ML pipelines
- Evaluate models using accuracy, precision, recall, F1-score
- Perform hyperparameter tuning with GridSearchCV
- Build ML applications for business & real-world domains
Live Components
- Weekly mentor-led ML problem-solving labs
- Group coding sessions for ML projects
- Capstone project showcase
Pre-recorded Access
- Tutorials on Scikit-Learn, regression, classification, clustering
- Pre-cleaned datasets for practice
- Recorded project walkthroughs
Anushri Mishra
- 4.5 Instructor Rating
- 100+ Reviews
- 200+ Students
- 10 Courses
I am an educator with 3 year of experience teaching Artificial Intelligence, Coding and Robotics. I specialise in simplifying complex technical concepts, making them engaging and accessible for learners of all backgrounds. My classes blend theory with hands-on projects, helping students understand how AI and robotics shape the world around us.
Passionate about fostering curiosity and innovation, I am committed to inspiring the next generation of creators and problem-solvers through practical learning and interactive teaching methods.
Educational Qualification - B.Tech - Computer Science Engineering (CSE)
Experience - 3+ Year in Education-Technology Sector.
Nitil Singh
- 4.9 Instructor Rating
- 900+ Reviews
- 1000+ Students
- 20+ Courses
I am an enthusiastic educator with 3+ year of experience teaching Robotics, STEM and Automation. I specialize in simplifying complex technical concepts, making them engaging and accessible for learners of all backgrounds. My classes blend theory with hands-on projects, helping students understand how robotics shape the world around us.
Passionate about fostering curiosity and innovation, I am committed to inspiring the next generation of creators and problem-solvers through practical learning and interactive teaching methods.
Educational Qualification - B.Tech - Computer Science Engineering (CSE)
Experience - 3+ Year in Education-Technology (EdTech) and Web Development.
Amar Deep Rao
- 4 Instructor Rating
- Good Reviews
- 5000 Students
- 900 Courses
Leave a Review
Available Coaching Centers:
What you need/Requirement
Software/Tools
PDF Reader (for notes and study materials).
Browser/App
Latest version of Google Chrome, Firefox, Safari or Microsoft Edge.
Internet Connection
Stable Internet with at least 2 Mbps speed for smooth video streaming and interactive content.
Device
Smartphone, Tablet, Laptop or Desktop Computer.
Learning Path
Beginner
Start with fundamental concepts and build a strong foundation.
Intermediate
Expand your knowledge and start building real projects.
Advanced
Dive deep into specialized areas and master complex techniques.
Master
Achieve expert-level proficiency and innovate with your skills.
Earn Valuable Credentials
and Lead with a Competitive Edge.
Certificate and Recognition That Validates Your Skills
Our curriculum is meticulously designed in collaboration with industry leaders to ensure every skill you acquire is not just current, but in high demand.
Get Mentorship From Top 1 % Industry Experts
Our mentors are seasoned professionals and thought leaders who provide unparalleled guidance and personalized feedback.
Network For Lifelong Success
Our vibrant community of professionals offers continuous support, mentorship, and a platform for lifelong career acceleration.
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- Learn Python basics for ML (NumPy, pandas, matplotlib).
- Understand how ML works: training vs testing, features vs labels.
- Build ML models using scikit-learn (Regression, Trees, SVM, k-NN, Naive Bayes).
- Clean and prepare data: handle missing values, scaling, encoding.
- Evaluate models using accuracy, precision/recall, confusion matrix & ROC-AUC.
- Improve performance with cross-validation and GridSearch.
- Create hands-on mini-projects (e.g., house price predictor, spam detector).
- Save models and optionally build a simple demo app with Streamlit.
- Ask doubts anytime — get clear, step-by-step help.
- Starter notebooks + datasets provided for every topic.
- One-on-one guidance for debugging code and improving accuracy.
- Code templates for preprocessing, modeling, and evaluation.
- Help using Git/GitHub to organize and share your work.
- Build a portfolio: 2–3 polished ML projects on GitHub.
- Mock interviews covering ML basics and problem-solving.
- Resume tips: how to explain your models and impact.
- Roadmap to next steps: Kaggle, Feature Engineering, Deep Learning.
- Understand where ML is used: finance, healthcare, retail, IoT, and apps.
- Join code-along sessions and small study groups.
- Get and give peer reviews on notebooks and results.
- Demo Day: present your best project and get feedback.
- Community channels to share tips, datasets, and wins.
- Access an alumni circle for collaboration and opportunities.