Module 1
Installation of jupyter Notebook Setup with Python (AI Development
Environment)

• What Is Artificial Intelligence
• Level Of product
• Introduction Of Machine Learning
• Types of Data Analytics
• Introduction To Statistics
• Application Of Machine Learning
• Machine Learning Cloud Platforms
• Objective Of Machine Learning Algorithm
• Introduction of Deep Learning
• Machine Learning VS Deep Learning
• Application of Deep Learning.
• Introduction to Data Science
• Introduction to Data Analytics
• Difference Between Data Science and Business Intelligence
• Turing Test
• History of Data Science
• Important Scientists to follow in the field of Machine Learning
• Why do we care about Data Science?

Module 2

• Hands on practice on Python Programming Language
• History Python 2 vs python 3
• Data Types and Data Structure with Python
• Methods and Functions in Python
• Object Oriented Programming In python.
• Python fundamentals and required libraries for the course
• Functions from numpy, matplotlib, scikit-learn and various other libraries
• Graph and Images in python
• Data Analysis
• Hands on practice with Salaries Dataset
• Statistical Analysis
• Data Visualization with Seaborn and Pyplot
• Hands on Practice with Ariflight Dataset.

Module 3

Linear Regression

• What Is Linear Regression?
• Univariate Linear Regression
• Hands on practice with Salary Dataset
• Gradient Descent Algorithm
• Linear Regression Use Cases
• Objectives of Linear Regression
• Multivariate Linear Regression
• One Hot Encoding and Dummy Variable
• Gradient Descent with Multiple Variable
• Hands On practice with Startup Funding Dataset
• Polynomial Regression
• Hands On practice with 50 Startup Dataset
• Linear Regression Assumptions

Module 4
Logistic Regression

• What Is Logistic Regression?
• Logistic Regression Features
• Objective of Logistic Regression
• Activation Functions:

1. Sigmoid Function
2. Softmax Function

• Model Evaluation
• Confusion Matrix

1. True Positive and True Negative
2. False Positive , False Negative
3. Accuracy, Precision
4. Recall, F1-Score

• Over Fitting & Generalisation
• Hands On practice with Bank Marketing Dataset
• Hands On practice with Social Network Advertisement Dataset

Module 5
Decision Tree

• Decision Tree Introduction
• Classification Tree
• ID3 Algorithm

1. Entropy
2. Information Gain

• CART algorithm

1. Gini Index
2. Gini Impurity

• C4.5 Algorithm
• Chi-Square Algorithm
• Regression Tree
• CART Algorithm

1. Variance

• Advantages and Disadvantages of Decision Tree
• Hands on Practice of Decision Tree with Breast Cancer Dataset

Hands on Practice of Decision Tree with Social Network Advertise Dataset
Using the Decision Tree Algorithm for analysing a real-life dataset
Module 6
Random Forest

• What is Bagging?
• How Random Forest Algorithm is a very major upgrade from Decision Tree?
• How does Random Forest handle Missing Data?
• Random Forest as Classifier
• Random Forest as Regressor.
• Random Forest Algorithm using SciKit Learn Library. Calculation of Accuracy and F1 Score
• Comparing Results of Decision Tree and Random Forest Algorithms for
• Breast-Cancer Dataset.

Module 7
Artificial Neural Network

• What is an Artificial Neural Network?
• Introduction to Neurons
• Single Neuron Model
• Activations Functions:

1. Sigmoid
2. Softmax
3. Relu
4. Leaky Relu Functions

• Neural Network Architecture
• Types of Neural Network
• Application, Advantages and Limitations
• Hands on practice of Artificial Neural Network with Churn Modelling Dataset.

Module 8
Support Vector Machines

• What are Convex Hulls?
• What is a hyperplane?
• What is weight and bias?
• Why is SVM is one of the best classification algorithms till date?
• Support Vector Machine Algorithm Derivation.
• A discussion on techniques to apply support vector classification on linearly
• inseparable datasets. (using Kernels or increasing the dimensionality of the dataset)

OUR COURSE IN COMPARISON

COURSE OBJECTIVES