Advanced Machine Learning – Self Paced

The Advanced Machine Learning Course 2025 is designed for professionals, data scientists, and AI enthusiasts looking to master machine learning, deep learning, and advanced AI applications. This course provides a comprehensive understanding of supervised, unsupervised, and reinforcement learning, as well as hands-on experience with Python, TensorFlow, Keras, and cutting-edge neural networks.

Updated as per AICTE norms, this course covers everything from core machine learning concepts to deep learning architectures such as CNNs, RNNs, LSTMs, Autoencoders, and GANs. Participants will also learn to implement advanced workflows including model evaluation, hyperparameter tuning, and practical applications like image classification, sentiment analysis, fraud detection, and recommender systems.

Course Curriculum Highlights:

Module 1: Introduction to Machine Learning

• Definition, key concepts, and real-world applications

• Types of Machine Learning: Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning

• Machine Learning Workflow: Data collection, preparation, model selection, training, evaluation, deployment

• Applications: Computer Vision, NLP, Speech Recognition, Recommender Systems, Fraud Detection

Module 2: Programming for Machine Learning

• Python Fundamentals: Variables, data types, operators, control structures, functions, modules

• NumPy and Pandas: Arrays, DataFrames, Series, data manipulation

• Matplotlib: Line, bar, histogram, and scatter plots

• TensorFlow Fundamentals: Tensors, operations, graphs, sessions

Module 3: Supervised Learning

• Linear and Logistic Regression

• Decision Trees and Random Forests

• Support Vector Machines (SVM)

Module 4: Unsupervised Learning

• Clustering: K-Means, Hierarchical, DBSCAN

• Principal Component Analysis (PCA)

• Anomaly Detection: Isolation Forest, Local Outlier Factor

Module 5: Deep Learning

• Neural Networks: Feedforward and Recurrent

• CNNs, RNNs, LSTMs

• Autoencoders and GANs

• Transfer Learning and Word Embeddings

• TensorFlow for deep learning with Keras API

Module 6: Model Evaluation and Optimization

• Data splitting, cross-validation

• Hyperparameter tuning: Grid Search, Random Search

• Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix

Module 7: Practical Applications

• Image Classification using CNNs and transfer learning

• Sentiment Analysis with RNNs and word embeddings

• Fraud Detection with anomaly detection techniques

• Recommender Systems using collaborative and content-based filtering

By completing this course, learners will gain the skills to build, train, and deploy advanced machine learning and deep learning models for real-world AI applications.