Machine Learning

Supervised and unsupervised algorithms, model evaluation and feature engineering with Scikit-Learn.

• Category: AI & Data Science
• Duration: 3 Months (60 hours)
• Level: Beginner to Intermediate
• Delivery: Live + Recorded
• Projects: 3

Fees and formats

• Self-paced: ₹7,499 (regular ₹9,000), 365 days access — includes Recorded video lessons, Quizzes & assignments, Learn at your own pace, Verifiable certificate
• Mentor-led: ₹14,000 (regular ₹16,000), 365 days access — includes Everything in Self-paced, Weekly live mentor sessions, 1:1 doubt support from mentors, Project reviews & feedback, Career guidance

Enrol in Machine Learning

About the program

Machine Learning enables systems to learn from data and make predictions without being explicitly programmed. In this program you'll prepare real datasets, train supervised and unsupervised models, and evaluate them the way industry teams do.

You'll work in Python with NumPy, Pandas, Matplotlib, Seaborn and Scikit-Learn, and finish with portfolio projects reviewed by mentors.

Highlights

• Recorded lessons + weekly live mentor sessions
• 3 hands-on portfolio projects
• Doubt support from industry mentors
• Verifiable digital certificate
• Resume & interview preparation

What you will learn

• Clean, pre-process, and visualize complex datasets in Python.
• Train, evaluate, and tune supervised classification and regression models.
• Apply unsupervised clustering algorithms to segments and group data points.
• Deploy robust machine learning prediction pipelines.

Tools and technologies

Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn

Curriculum

1. Weeks 1-4: Data Preprocessing & Python Libraries
   • Python programming basics: Loops, lists, and directories
   • Data manipulation with NumPy array calculations
   • Pandas library for filtering, cleaning, and grouping datasets
   • Data visualization with Matplotlib and Seaborn charts
2. Weeks 5-8: Supervised Learning Algorithms
   • Linear Regression and Logistic Regression mathematics
   • Model evaluation metrics: MSE, R2, Accuracy, Precision, Recall
   • Tree-based algorithms: Decision Trees, Bagging, and Random Forests
   • Boosting models (XGBoost, AdaBoost) for structured data prediction
3. Weeks 9-12: Unsupervised Learning & Model Selection
   • Clustering models: K-Means and Hierarchical clustering
   • Dimensionality reduction using Principal Component Analysis (PCA)
   • Hyperparameter tuning and cross-validation techniques
   • Building and deploying end-to-end machine learning pipelines

Capstone projects

• Predictive Customer Churn Model — Implement a machine learning pipeline to preprocess customer data, scale features, and train Random Forest models to predict subscriber churn. (built with Python, Pandas, Scikit-Learn, Seaborn)
• House Price Forecasting Pipeline — Train a regression pipeline using boosting models to forecast real estate prices based on geographical and structural features. (built with Python, Scikit-Learn, XGBoost)

Eligibility

Open to all. No prior coding experience required.

Frequently asked questions

Who can join this program?

Students, fresh graduates and working professionals. No prior experience is needed unless listed under eligibility — we start from the basics.

How are classes delivered?

The program runs for 3 Months. You get recorded lessons you can watch any time, plus weekly live mentor sessions for doubt clearing and project reviews.

Will I get a certificate?

Yes. When you complete all lessons and the capstone project you receive a Wayspire certificate with a unique ID that anyone can verify on our website.

Do you provide placement?

We provide career support — resume reviews, mock interviews and guidance. We do not guarantee jobs or placements.

What if I miss a live session?

Every live session is recorded and added to your learner panel within 24 hours.

Wayspire provides training, mentorship, projects and certification. We do not guarantee jobs, internships, placements or salary packages — career support is guidance to help you in your own job search.