| Unit 1 Artificial Intelligence for Business Planning {Book} | |
| Introduction and Data sources for AI | VIEW |
| Knowledge Acquisition | VIEW |
| Knowledge Representation | VIEW |
| History of Machine Learning | VIEW |
| Framework for building ML Systems-KDD process mode | VIEW |
| Introduction of Machine Learning Approaches: | |
| Artificial Neural Network | VIEW |
| Clustering Reinforcement Learning | VIEW |
| Decision Tree Learning | VIEW |
| Bayesian networks | VIEW |
| Support Vector Machine | VIEW |
| Genetic Algorithm | VIEW |
| Issues in Machine Learning | VIEW |
| Data Science Vs Machine Learning | VIEW |
| Unit 2 Supervised Learning and Applications {Book} | ||
| Supervised Learning: Introduction to classification | VIEW | |
| Linear Regression | VIEW | |
| Metrics for evaluating linear model, Multivariate regression | VIEW | |
| Non-Linear Regression | VIEW | |
| K-Nearest Neighbor | VIEW | |
| Decision Trees | VIEW | VIEW |
| Logistic Regression | VIEW | |
| Support Vector Machines | VIEW | |
| Model Evaluation | VIEW | |
| Applications of Supervised learning in multiple domains | VIEW | |
| Application of supervised learning in Solving business problems such as Pricing, Customer relationship management, Sales and Marketing | VIEW | |
| Unit 3 {Book} | |
| Unsupervised Learning | VIEW |
| Clustering, Hierarchical clustering | VIEW |
| Partitioning Clustering- K-mean clustering | VIEW |
| Density Based Methods DBSCAN, OPTICS | VIEW |
| Applications of unsupervised learning in multiple domains | VIEW |
| Association rules: Introduction, Large Item sets, Apriori Algorithms and applications | VIEW |
| Unit 4 Artificial Neural Networks & Deep Learning {Book} | |
| Perceptron model, Multilayer perceptron | VIEW |
| Gradient descent and the Delta rule | VIEW |
| Multilayer networks | VIEW |
| Backpropagation Algorithm | VIEW |
| DEEP LEARNING Introduction | VIEW |
| Concept of Convolutional Neural network | VIEW |
| Types of layers (Convolutional Layers, Activation function, Pooling, Fully connected) | VIEW |
| Concept of Convolution (1D and 2D) Layers | VIEW |
| Training of Network, Recent Applications | VIEW |
| Unit 5 Reinforcement Learning {Book} | |
| Introduction to Reinforcement Learning, Learning Task, Example of Reinforcement Learning in Practice, Learning model for Reinforcement Markov Decision process | VIEW |
| Q Learning: Q Learning function, Q Learning Algorithm, Application of Reinforcement Learning, Introduction to Deep Q Learning | VIEW |
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