Machine Learning
Master the fundamentals and advanced concepts of Machine Learning by learning how to build intelligent systems that can analyze data, identify patterns, and make accurate predictions. This course is designed to take you from foundational concepts to real-world implementation, covering data preprocessing, exploratory analysis, supervised and unsupervised learning techniques, model optimization, and deployment. Through a hands-on, project-based approach, you will work with real datasets, develop predictive models, and gain practical experience in solving industry-relevant problems. You will also learn how to evaluate model performance, fine-tune algorithms, and deploy machine learning solutions into applications. By the end of this course, you will have a strong understanding of the complete machine learning lifecycle along with a portfolio of projects that demonstrate your ability to apply ML techniques in real-world scenarios, making you job-ready for roles such as Machine Learning Engineer, Data Scientist, and AI Specialist.
Starting from
- Key Highlights
- Project-First Learning — Build and deploy real ML models from Day 1, no theory-only sessions.
- End-to-End Pipeline — Master data cleaning, model training, evaluation, and production deployment.
- Industry Tools — Hands-on with Scikit-learn, TensorFlow, Pandas, NumPy & Jupyter Notebooks.
- 1:1 Mentorship — Weekly sessions with practicing ML engineers for code reviews & guidance.
- Certificate + Placement Support — Leave with projects, a certificate, and referrals to 50+ hiring partners.
Syllabus
- What is Machine Learning?
- Types of Machine Learning
- Applications & Industry Use Cases
- Machine Learning Workflow
- Python Basics (Variables, Data Types, Loops)
- Functions & Libraries
- NumPy for Numerical Computing
- Pandas for Data Handling
- Data Cleaning Techniques
- Data Transformation
- Feature Engineering
- Train-Test Split & Data Preparation
- Understanding Data Distributions
- Visualization Techniques
- Outlier Detection
- Correlation Analysis
- Linear Regression
- Polynomial Regression
- Model Evaluation Metrics (Regression)
- Regularization Techniques
- Logistic Regression
- Decision Trees
- k-Nearest Neighbors (KNN)
- Naive Bayes
- Random Forest
- Bagging Techniques
- Boosting Algorithms
- Model Comparison & Selection
- Clustering Techniques
- Dimensionality Reduction
- Association Rule Learning
- Evaluation of Clustering Models
- Overfitting & Underfitting
- Cross-Validation Techniques
- Hyperparameter Tuning
- Pipeline Building
- Saving & Loading Models
- Building ML Applications
- API Development for ML Models
- End-to-End ML Project