Advanced Professional Certification Programme in Data Science & Machine Learning - E&ICT, IIT Guwahati

Statistical Foundations for Data Science

• Linear Algebra, Multivariate Calculus
• Probability & Statistics and Optimization
• Population, sample, methods of sampling – basics of statistics
• Measures of distribution – Mean, median, mode, S.D & variance
• Empirical rule – Normal distribution
• Probability & its basics – Mutually exclusive events, independent events
• Laws of probability
• Conditional probability
• Types of distribution – Binomial, Poisson & Normal distribution
• Hypothesis & T – testing
• Lab – Exercises

Data Handling and Data Visualisation with Tableau

• Data extraction, cleaning
• Data Preprocessing
• Outlier Treatment for Various types of data
• Data Visualization
• Tableau Training, GIT, SQL, JSON
• Lab – Exercises

Machine Learning using Clustering Unsupervised Learning

• K means Clustering
• Hierarchical clustering

Machine Learning Optimization Techniques

• Importance of Dimensionality Reduction
• PCA
• Project

Performance Metrics

• Boosting methods
• Bagging techniques

Recommender Systems using Artificial Intelligence

• Introduction to Recommendation systems
• Market Basket Analysis
• Content-based recommendation system
• Popularity based model
• Collaborative filtering (User similarity & Item similarity)
• Hybrid models

Introduction to Neural Networks and Deep Learning

• Gradient Descent
• Introduction to Perceptron & Neural Networks
• Batch Normalisation
• Activation and Loss functions
• Confidential – Not to be shared without permission
• Hyperparameter tuning
• Deep Neural Networks
• Tensor Flow & Keras for Neural Networks & Deep Learning

Advanced Applications of Deep Learning

• Introduction to Convolutional
• Neural Networks
• Forward propagation &
• Backpropagation for CNNs
• Convolution, Pooling, Padding & its mechanisms
• CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
• Transfer Learning
• Auto encoders
• Semantic segmentation
• YOLO
• Siamese Networks
• Object & face recognition using techniques above

Natural Language Processing  using Sentimental Analysis

• Bag of Words Model
• TF-IDF
• POS Tagging
• Named Entity Recognition
• Tokenization
• Stop Words
• Word Vectorizer
• Introduction to Sequential data
• RNNs and its mechanisms
• Vanishing & Exploding gradients in RNNs
• Time series analysis
• LSTMs
• LSTMs with attention mechanism and GRU

Model Applications & Deployment

• Deployment on Cloud
• Recent Trends in AI/ML
• Capstone Project