Artificial Intelligence (AI) Using Python

Objective:

In this program, you'll learn all the foundational and advanced skills necessary to start using AI techniques with Python in your current role, prepare for a full-time career in an AI-powered industry, or get started in the amazing world of AI. We will commence to gain an understanding of what AI is and how does it relate to business problems. We will gradually move on to cover more advanced topics of supervised and unsupervised learning using various packages and software environments in Python

Learning Outcomes:

1. Develop a deep knowledge on the various aspects of artificial intelligence and get familiar with the concepts of supervised and unsupervised learning
2. Learn to solve complex analytical and business problems using Python

Lecture-wise Content (1 hour per lecture):

Session	Topic
1	Introduction to Artificial Intelligence What is Artificial Intelligence? Why do we need to study AI? Applications of AI Branches of AI
2	Installing Python on Ubuntu, Mac OS, Windows Installing packages Loading data
3	Classification and Regression Using Supervised Learning - What is classification? Pre-processing data Binarization, Mean removal, Scaling and Normalization
4	Label encoding Logistic Regression classifier
5	Naïve Bayes classifier Confusion matrix
6	Support Vector Machines
7	Project 1 – A mini assignment on classification using the above algorithms
8	What is Regression? Building a single and multi-variable regressor Using support vector
9	Predictive Analytics with Ensemble Learning, Building learning models with Ensemble Learning
10	What are Decision Trees? Building a Decision Tree classifier
11	What are Random Forests and Extremely Random Forests? Building Random Forest and Extremely Random Forest classifiers Estimating the confidence measure of the predictions
12	Dealing with class imbalance Finding optimal training parameters using grid search Computing relative feature importance
13	Project 2: Industry problem on prediction using Random Forest Regressor
14	Introduction to Unsupervised learning
15	Clustering data with K-Means algorithm Estimating the number of clusters with Mean Shift algorithm Estimating the quality of clustering with silhouette scores
16	What are Gaussian Mixture Models? Building a classifier based on Gaussian Mixture Models Finding subgroups in stock market using Affinity Propagation model
17	Building Recommender Systems, Extracting the nearest neighbours Building a K-Nearest Neighbours classifier Computing similarity scores
18	Collaborative Filtering
19	Artificial Neural Networks, Introduction to artificial neural networks, Building a neural network, Training a neural network
20	Building a Perceptron based classifier Constructing a single layer neural network Constructing a multilayer neural network
21	Building a vector quantizer Analysing sequential data using recurrent neural networks
22	Deep Learning with Convolutional Neural Networks What are Convolutional Neural Networks? Architecture of CNNs, Types of layers in a CNN
23	Building a perceptron-based linear regressor
24	Reinforcement Learning Understanding the premise, Reinforcement learning versus supervised learning
25	Heuristic Search Techniques: What is heuristic search? Uninformed versus Informed search, Constraint Satisfaction Problems, Local search techniques
26	Natural Language Processing
27	Genetic Algorithms: Understanding evolutionary and genetic algorithms Fundamental concepts in genetic algorithms
28	Case 1
29	Case2