Course Includes:
- Price: ₹ 29,999
- Instrutor: Srikanth K
- Days: Monday - Friday
- Duration: 60 Hours
- Enrolled: 65 students
- Articles: 1 Article
- Language: English
- Certificate: Yes
Machine Learning & AI Mastery Program
- Learn the core concepts of Machine Learning, Deep Learning, and AI, including supervised, unsupervised, and reinforcement learning.
- Build a strong foundation in math and statistics — linear algebra, probability, and optimization for model development.
- Gain hands-on experience in data preprocessing, feature engineering, and model evaluation using Python and popular ML libraries.
- Master advanced algorithms for regression, classification, clustering, and dimensionality reduction with real-world datasets.
- Explore Deep Learning architectures (CNN, RNN, LSTM, Transformers, GANs) using TensorFlow, Keras, and PyTorch.
- Implement end-to-end ML projects with MLOps, Docker, Flask APIs, and cloud deployment (AWS, GCP, Azure)
Course Curriculum
Module 1. Introduction to Machine Learning
- What is ML? Difference between AI, ML, and DL
- Types of ML: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
- Real-world applications
- ML workflow
Module 2. Mathematical & Statistical Foundations
- Linear algebra fundamentals
- Probability & statistics
- Calculus & optimization techniques
Module 3. Data Preprocessing & Feature Engineering
- Data collection & cleaning
- Handling missing values & outliers
- Encoding categorical data
- Feature scaling
- Feature selection techniques
- Dimensionality reduction
Module 4. Exploratory Data Analysis (EDA)
- Data visualization techniques
- Correlation & covariance analysis
- Distribution analysis
- Handling imbalanced datasets
Module 5. Supervised Learning
- Regression algorithms
- Classification algorithms
- Ensemble learning techniques
Module 6. Model Evaluation & Validation
- Train-test split & cross-validation
- Bias-variance tradeoff
- Regression & classification metrics
- Confusion matrix & ROC
Module 7. Unsupervised Learning
- Clustering algorithms
- Dimensionality reduction techniques
- Anomaly detection
- Association rule learning
Module 8. Feature Engineering for Real-world Data
- Text data processing
- Image data preprocessing
- Time series feature engineering
Module 9. Advanced ML Topics
- Explainable AI (SHAP, LIME)
- Handling imbalanced data
- Transfer & semi-supervised learning
- Online & incremental learning
Module 10. Deep Learning (Basics)
- Neural networks fundamentals
- Activation functions
- Backpropagation
- Deep learning frameworks
Module 11. Deep Learning (Advanced)
- CNNs for computer vision
- RNN, LSTM, GRU
- Transformers & attention models
- Autoencoders & GANs
Module 12. Time Series Analysis
- Time series fundamentals
- ARIMA & SARIMA
- Prophet
- LSTM for forecasting
Module 13. Reinforcement Learning
- Markov Decision Processes
- Q-Learning
- Deep Q Networks
- Industry use cases
Module 14. Deployment & MLOps
- Model serialization
- Building ML APIs
- Docker & containerization
- Experiment tracking
- CI/CD for ML
- Cloud deployment
- Model monitoring & retraining
Module 15. Domain Applications
- BFSI use cases
- Healthcare applications
- Retail analytics
- Manufacturing intelligence
- Cybersecurity applications
Module 16. Capstone Projects
- Customer churn prediction
- House price prediction
- Recommendation systems
- Handwritten digit recognition
- Stock price forecasting
- Fraud detection project
Course Rating
5.00 average rating based on 7 rating
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