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Master Program in DATA ANALYTICS & Artificial Intelligence

Enrolled: 26535 students
Duration: 6 Months
Lectures: 183
Level: Beginner

Introduction to Python

1
Python Installation
2
Anaconda and Jupyter Notebook
3
Variable in Python
4
Data Types
5
Python Numbers
6
Limit of Integers
7
Arithmetic Operators
8
Taking Inputs

Conditions and Loops

1
Boolean Datatype
2
Conditional statement (IF ELSE)
3
Relational and logical operators
4
Using Else If
5
Nested Conditionals
6
While Loop
7
Primality Checking
8
Nested Loops

Strings & Lists

1
String inbuilt functions
2
String slicing
3
Lists
4
Lists inbuilt functions
5
List Slicing
6
Multi-dimensional Lists

Functions

1
Functions
2
Functions using strings and lists
3
Score of Variable
4
Default parameters in functions

Tuples

Dictionary and Sets

1
Tuples
2
Tuples Functions
3
Dictionary
4
Adding or removing Data in Dictionary
5
Sets
6
Functions in sets

OOPS

1
Introduction
2
Create class & object
3
Instance Attributes
4
Class Attributes
5
Methods
6
Constructors
7
Class Methods & Static Methods
8
Working with files

NumPy

1
Introduction
2
Create NumPy arrays
3
Slicing & indexing
4
Mathematical Operations
5
Boolean Indexing
6
NumPy Broadcasting

Pandas

1
Introduction
2
Accessing data in Pandas
3
Manipulating Data in Data Frame
4
Handling NAN
5
Handling string in Data

Plotting Graph (Data Visualization)

1
Matplot Lib -Plotting Graphs
2
Customizing Graph
3
Pie Chart
4
Bubble Chart
5
Histogram
6
Bar Graph
7
Box Plot
8
Line Plot
9
Scatter Plot

Statistics - Sampling & Population

1
Introduction to Statistics
2
Data Types
3
Sample & Population
4
Simple Random Sampling
5
Satisfied Sampling
6
Cluster Sampling
7
Systematic Sampling
8
Categories of Statistics

Descriptive Statistics

1
Measures in Descriptive Statistics
2
Measures in Central Tendency
3
Measures of Spread
4
Range & IQR
5
Variance and Standard Deviation
6
Measure of Position

Inferential Statistics

1
Inferential Statistics
2
Hypothesis Testing
3
Type 1 and Type 2 errors
4
Correlation

Linear Regression

1
Implementing Simple & Multiple Linear Regression with Python
2
Making sense of result parameters
3
Model validation
4
Handling other issues/assumptions in Linear Regression
5
Handling outliers
6
categorical variables
7
autocorrelation
8
multicollinearity
9
heteroskedasticity
10
Prediction and Confidence Intervals

Project 1 - Linear Regression

1
Case Study – Property Price Prediction using Linear Regression

Logistic Regression

1
Implementing Logistic Regression with Python
2
Making sense of result parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-square Test
3
Goodness of fit measures
4
Model validation: Cross Validation, ROC Curve, Confusion Matrix

Project 2 - Logistic Regression

1
Case Study – Identifying Good & Bad Customers for Granting Credit

Decision Trees

1
Implementing Decision Trees using Python
2
Homogeneity
3
Entropy
4
Information Gain
5
Gini Index
6
Standard Deviation Reduction
7
Vizualizing & Prunning a Tree
8
Implementing Random Forests using Python
9
Random Forest Algorithm
10
Important hyper-parameters of Random Forest for tuning the model
11
Variable Importance
12
Out of Bag Error

Project 3 – Decision Tree

1
Case Study – Identifying Good & Bad Customers for Granting Credit

Time Series

1
Handling time series data
2
Holt-Winters Model
3
ARIMA Model
4
ACF/PACF Functions

Project 4 - Time Series

1
Case Study – Forecasting and predicting the sales of furniture of the Superstore

Dimensionality Reduction - PCA

1
Introduction to Dimensionality Reduction
2
Principal Component Analysis (PCA)
3
Factor Analysis (FA)
4
Difference between PCA and FA

Project - PCA

1
Project : Reduce Data Dimensionality for a House Attribute Dataset using PCA

Introduction to Machine Learning

1
Introduction to Machine Learning
2
Machine Learning Modelling Flow
3
How to treat Data in ML
4
Parametric & Non-parametric ML Algorithm
5
Types of Machine Learning
6
Performance Measures
7
Bias-Variance Trade-Off
8
Overfitting & Underfitting
9
Resampling Methods
10
Ensemble Methods

SciKit Learn

1
Introduction to SciKit Learn
2
Data Processing using Scikit-learn
3
Feature Extraction
4
Run Machine Learning Algorithms Both for Unsupervised and Supervised Data
5
Supervised Methods: Classification & Regression
6
Unsupervised Methods: Clustering, Gaussian Mixture Models
7
Performance Measurement Metrics
8
Decide What’s the Best Model for Every Scenario

Optimization Techniques

1
Introduction to Optimization
2
Optimization Strategies
3
Batch Gradient Descent
4
Stochastic Gradient Descent
5
Nesterov Accelerated Gradient
6
Root Mean Squared Propagation
7
Adaptive Moment Estimation Procedure

Project:: Supervised Learning: Linear Regression

1
Linear Regression with Stochastic Gradient Descent Project

Project Supervised Learning: Logistic Regression

1
Logistic Regression with Stochastic Gradient Descent & Project

Supervised Learning: K-NN

Supervised Learning: SVM

1
Support Vector Machine & Project

Unsupervised Learning

1
What is Clustering?
2
K-means Algorithm
3
Types of Clustering
4
Evaluating K-means Clusters

Project - Clustering

1
Project on Clustering

Ensemble Algorithms

1
Resampling Methods
2
Bootstrap Sampling
3
Bootstrap Aggregation
4
Bagging
5
Boosting
6
Stack generalization

Project - Decision Tree & Random Forest

1
Project on Decision Tree & Random Forest

Bootcamp

1
ML bootcamp

Job Preparation

1
Resume building workshop
2
Interview preparation workshop
3
Mock interviews
4
Capstone Project

AI -Deep Learning

1
Neural Networks
2
Understanding Neural Networks
3
The Biological Inspiration
4
Perceptron Learning & Binary Classification
5
Back propagation Learning & Object Recognition

Project - Neural Network

Convolutional Neural Network(CNN)

1
Intro to CNN
2
Convolutional operations and Image Features
3
ReLu
4
Pooling
5
Fully Connected Layer
6
Training a CNN & Image Classification

Project 6 CNN on Tensor flow

1
Case Study – DIGIT RECOGNITION USING TENSORFLOW

Recurrent Neural Network (RNN)

1
Introduction to RNN
2
RNN Network Structure
3
Different Types of RNNs
4
Bidirectional RNN
5
Limitations of RNN

Project- RNN

1
Project- RNN

Long Short Term Memory (LSTM)

1
Introduction to LSTM
2
LSTM Architecture
3
Variants on LSTM

Project on LSTM

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