Loading...Loading...Comprehensive AI & ML program: Python for ML, statistics, supervised & unsupervised learning, deep learning (TensorFlow/Keras), NLP, and MLOps basics. Real-time projects including house price prediction, churn prediction, sentiment analysis, and chatbot. Portfolio building and interview prep.
Introduction to AI & Machine Learning, AI vs ML vs Deep Learning, Types of ML, ML Life Cycle, Role of AI/ML Engineer, Real-World AI Use Cases.
Python Refresher for ML, NumPy, Pandas, Data Cleaning, Data Preprocessing, Feature Engineering, Handling Missing Values, Train-Test Split.
Descriptive Statistics, Probability, Distributions, Correlation & Covariance, Linear Algebra Basics, Gradient Descent, Bias vs Variance.
Introduction to Supervised Learning, Linear Regression, Multiple Linear Regression, Logistic Regression, KNN, SVM, Decision Trees, Random Forest, Model Evaluation Metrics, Confusion Matrix, ROC-AUC Basics.
Introduction to Unsupervised Learning, K-Means Clustering, Hierarchical Clustering, DBSCAN, PCA, Association Rules Basics.
Cross Validation, Hyperparameter Tuning, Grid Search, Random Search, Overfitting vs Underfitting, Regularization.
Introduction to Neural Networks, Perceptron, Activation Functions, Forward & Backpropagation, TensorFlow & Keras, Building ANN Models, CNN Basics, Image Classification Project.
Introduction to NLP, Text Preprocessing, Tokenization, Stop Word Removal, TF-IDF, Sentiment Analysis, Text Classification, Named Entity Recognition Overview.
Introduction to MLOps, Model Versioning, Model Deployment Basics, Building ML API with Flask/FastAPI, Docker for ML Models, CI/CD for ML Overview.
House Price Prediction, Customer Churn Prediction, Sentiment Analysis Project, AI Chatbot Project, End-to-End ML Project Deployment, Portfolio Building, Resume Building, Mock Interviews, AIML Interview Questions.
Supervised/Unsupervised, Deep Learning, NLP. Real projects.
Python, NumPy, Pandas, Scikit-learn, TensorFlow, Keras, NLP libraries
Freshers with math/programming interest, data analysts, developers.
ML Engineer, Data Scientist, AI Engineer, NLP Engineer
Enquire NowStudent experiences from model-building to real-world AI projects.
"Machine learning concepts were explained simply and backed with project work."
"NLP and deep learning modules helped me build confidence in advanced topics."
"Excellent guidance on projects, model deployment, and interview prep."