Machine Learning
Learn Python the Smart Way - Core + Advanced in One Course.
Our Machine Learning (Core & Advanced) online course is designed to take you from complete basics to professional-level machine learning with a clear and structured approach. You will learn essential concepts such as Python for machine learning, data preprocessing, statistics, supervised and unsupervised learning, model training, and evaluation, followed by advanced topics including feature engineering, model optimization, automation, APIs, and real-world project development. The course includes live interactive classes, recorded sessions, hands-on practice tasks, and full mentor support to ensure strong practical skills. It is ideal for beginners, students, and working professionals who want to build a strong IT career in machine learning and artificial intelligence.
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Course Duration
3 Months
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Machine Learning
Module 1: Foundations of Machine Learning (6 Hours)
- Hour 1: Introduction to AI & ML, ML applications, Types of ML
- Hour 2: Features, Labels, Training vs Testing, Overfitting & Underfitting
- Hour 3: Math essentials: Linear Algebra, Probability & Statistics
- Hour 4: Python for ML: NumPy, Pandas, Matplotlib, Data preprocessing
- Hour 5: Exploratory Data Analysis (EDA), Titanic case study
- Hour 6: Mini Project: Data Cleaning & EDA (Iris / Housing dataset)
Module 2: Supervised Learning (8 Hours)
- Hour 7: Supervised Learning, Regression vs Classification
- Hour 8: Linear Regression: theory & Python implementation
- Hour 9: Logistic Regression, Binary classification hands-on
- Hour 10: Decision Trees, Gini, Entropy, Pruning
- Hour 11: Ensemble Methods: Random Forest, Gradient Boosting
- Hour 12: Support Vector Machines (SVM) & kernels
- Hour 13: Model evaluation & hyperparameter tuning
- Hour 14: Project 1: House Price Prediction / Fraud Detection
Module 3: Unsupervised Learning (5 Hours)
- Hour 15: Clustering: K-Means, Hierarchical, Distance metrics
- Hour 16: Hands-on: Customer Segmentation
- Hour 17: Dimensionality Reduction: PCA
- Hour 18: Anomaly Detection, Credit Card Fraud example
- Hour 19: Project 2: Market Basket Analysis (Apriori)
Module 4: Advanced ML & Real-World Aspects (6 Hours)
- Hour 20: Neural Networks & Deep Learning basics
- Hour 21: NLP fundamentals & text preprocessing
- Hour 22: Time Series Forecasting (ARIMA, Prophet)
- Hour 23: Feature Engineering & Regularization
- Hour 24: Model Deployment & MLOps overview
- Hour 25: Project 3: Sentiment Analysis
Module 5: Capstone Project & Review (5 Hours)
- Hour 26: Capstone Project Kickoff & dataset selection
- Hour 27–28: End-to-End ML Pipeline implementation
- Hour 29: Model Deployment Demo (Flask / Streamlit)
- Hour 30: Project Presentation, Review & Q&A