Course Title: Artificial Intelligence and Machine Learning
Course Duration: 100 Hours
Module 1: Python Programming (6 Projects)
Basics of Programming
Variables, Strings and Numbers, Math Operators, Built-in functions, List and its methods, Tuples, Dictionaries, User Inputs, Conditionals, Custom Functions, Loops.
Numpy Arrays, Indexing, Slicing, Iterating, Stacking and Splitting, Numpy Operations.
Introduction, Series, Data Frames, Merging, Joining and Concatenating, Operations, Data i/p and o/p.
Data Visualisation, Bar Plot, Pair Plot, Histogram and Box-plot, Distribution Plots, Grids and Matrices.
Module 2: Statistics (3 Projects)
Probability, Combinations, Bayesian Inference, Distributions, Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Advanced Statistical Methods.
Module 3: Machine Learning (10 Projects)
Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Ridge and Lasso Regression, Model and Performance Evaluation.
Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), Kernel SVM, Naïve Bayes, Decision Tree, Random Forest , Model and Performance Evaluation.
Clustering, K-Means Clustering, Hierarchical Clustering, Principal Component Analysis
Max Voting, Averaging, Weighted Average, Stacking, Blending, Bagging & Boosting, AdaBoost, Gradient Boosting, XGBoost, CatBoost and Light GBM.
Module 4: Neural Networks and Deep Learning (30 Projects)
Deep Learning Introduction, Shallow Neural Networks, Artificial Neural Networks, Deep Neural Networks, 3 Projects.
Digital Image Processing
Manipulate the images, Image Segmentation, Object Tracking, Object Detection, Feature Detection, Image Filtering, Transformations, Photo Denoising, Facial Landmarks, 10 Mini Projects.
Convolutional Neural Networks , Regularization Methods, 2 Projects, Architectural design, Instance Segmentation, Semantic Segmentation, Object Detection, Video Analysis, SSD, Yolo, Mask-RCNN, 5 Real Time Projects.
Natural Language Processing
NLP Basics, Processing and understanding text, Feature Engineering, Text Classification, Text Summarization, Text Clustering, Semantic Analysis, Sentiment Analysis, Word Embedding, Recurrent Neural , Networks, FastText, Text-CNN, Text-LSTM, Bi-LSTM, 12 Real Time Projects.
Popularity Based RS, Content-Based Filtering, Collaborative Filtering, Hybrid Approaches.
Model Pipelines and Applications, Model Architecture, API Development and CI/CD concepts, Paas and AWS deployment.