Machine Learning
Build predictive models, master supervised & unsupervised learning, and deploy ML applications using Python and scikit-learn.
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Duration
2 Months
Learning Mode
Live Online + Labs
Hands-on Work
4 Real Projects
Credential
Verified Certificate
Technologies & Tools You'll Master
Hands-on experience with production tools and frameworks utilized by modern tech enterprises
About The Course
Practical curriculum engineered to take you from foundational principles to deployment-ready expertise
Learn how to build intelligent systems that learn from data and make predictions. This hands-on Machine Learning course takes you from the fundamentals of machine learning to implementing, evaluating, and deploying predictive models using Python. Through practical coding sessions, real-world datasets, and guided projects, you will develop the skills to solve business problems using data-driven intelligence. Duration: 2 Months | Mode: Online Instructor-Led Training | Tools: Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook.
Core Competencies & Skills
Course Curriculum & Syllabus
Module 1: Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning
- Real-world applications
- Understanding datasets and features
- Introduction to the ML workflow
Module 2: Python and Data Preparation
- Python essentials for ML
- NumPy and Pandas
- Data cleaning and missing values
- Data visualization
- Encoding categorical variables
- Feature scaling and train-test splitting
Module 3: Supervised Learning
- Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
Module 4: Unsupervised Learning
- Introduction to clustering
- K-Means Clustering
- Hierarchical Clustering
- Dimensionality Reduction
- Principal Component Analysis (PCA)
Module 5: Model Evaluation and Optimization
- Accuracy, Precision, Recall and F1-score
- Confusion Matrix
- Regression evaluation metrics
- Overfitting and Underfitting
- Cross-validation
- Hyperparameter tuning
- Feature importance
Module 6: Building and Deploying ML Models
- Creating an end-to-end ML pipeline
- Saving and loading trained models
- Building a simple Streamlit interface
- Deploying a basic ML application
- Introduction to responsible AI
What You Will Be Able To Build
Enterprise Real-World Projects
House Price Prediction
Build a regression model to estimate property prices from relevant features.
Customer Segmentation
Group customers based on their purchasing patterns using clustering techniques.
Heart Disease Risk Prediction
Build and evaluate a classification model using a suitable healthcare dataset for educational purposes.
Customer Churn Prediction
Predict which customers may discontinue a service using historical data.
Career Paths & In-Demand Roles
Frequently Asked Questions
Ready to Master Machine Learning?
Have questions about upcoming batches, syllabus deep-dives, or prerequisites? Chat directly with our technical mentors and admissions coordinators on WhatsApp.
⚡ Quick Response · Flexible Weekday/Weekend Batches · Certificate Included