• Introduction to Artificial Intelligence
• Introduction to Machine Learning
• Need for Machine Learning in Forecasting
• Demand of forecasting analytics in current industrial trends
• Scope of ML
• Introduction to Machine Learning Algorithm Categories
• Supervised and Unsupervised Learning

• Introduction to Regression
• Exercise on Linear Regression using Scikit Learn Library
• Project on Linear regression using USA_HOUSING data
• Evaluation of Linear regression using python visualizations
• Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.

K- Nearest neighbors using Python

• Exercise on K-Nearest neighbors using Sci-kit Learn Library
• Project on Logistic regression using Dogs and horses’ dataset
• Getting the correct number of clusters
• Evaluation of model using confusion matrix and classification report
• Standard scaling problem
• Practice project on KNN algorithm.

Decision tree and Random forest with python

• Intuition behind Decision trees
• Implementation of decision tree using a real time dataset
• Ensemble learning
• Decision tree and random forest for regression
• Decision tree and random forest for classification
• Evaluation of the decision tree and random forest using different methods
• Practice project on decision tree and random forest using social network
• Data to predict if someone will purchase an item or not

Support Vector Machines

• Linearly separable data
• Non-linearly separable data
• SVM project with telecom dataset to predict the users portability

Principal Component Analysis

• Introduction to PCA
• Need for PCA
• Implementation to select a model on breast-cancer dataset
• Model evaluation
• Bias variance trade-off
• Accuracy paradox
• CAP curve and analysis

Clustering in unsupervised learning

• K-means clustering intuition
• Implementation of K-means with Python using mall customers data to implement clusters on the basis of spending and income
• Hierarchical clustering intuition
• Implementation of Hierarchical clustering with python

Association Algorithms

• A priori theory and explanation
• Market basket analysis
• Implementation of Apriori
• Evaluation of association learning

POC – To make a model to predict the relationship between frequently bought products together on the given dataset from a supermarket.

• Introduction to Natural Language processing
• NLTK Python library
• Data stemming technique
• Data Vectorization
• Exercise on NLTK
• POC– Apply NLP techniques to understand reviews given by customers in a dataset and predict if a review is good/bad without human intervention.

• Neural Network and Deep Learning
• What is TensorFlow?
• TensorFlow Installation
• TensorFlow basics
• TensorFlow with Contrib Learn
• TensorFlow Exercise
• What is Keras?
• Keras Basics
• Pipeline implementation using Keras
• MNIST implementation with Keras