Enrolled: 8 students
Duration: 216 hours
Lectures: 147
Level: Beginner

Working hours

Monday 10:00 am - 7:00 pm
Tuesday 10:00 am - 7:00 pm
Wednesday 10:00 am - 7:00 pm
Thursday 10:00 am - 7:00 pm
Friday 10:00 am - 7:00 pm
Saturday 10:00 am - 7:00 pm
Sunday 10:00 am - 7:00 pm

INTRODUCTION TO DATA SCIENCE

1
What Is Data Science
2
Different Domains In Data Science
3
Need Of Data Science In Business
4
Use Of Data Science Project
5
Data Science Tools And Technologies
6
Basic Of Excel For Analysis

OOPS

1
Classes
2
Abstract
3
Inheritance
4
Collection
5
List
6
Dictionary
7
Datatables
8
Delegates and events

Python Programming

1
Introduction to python programing
2
Command line basics
3
Numbers, Operators & Comments
4
Variables & Strings
5
Boolean & Conditional Logic
6
Loops in Python (for loop and while loop)
7
Lists
8
Dictionaries
9
Tuples & sets
10
Functions
11
Modules
12
OOP
13
File I/O

STATISTIC

1
Introduction to probability
2
Probability theory
3
Probability Distribution
4
Bayes thorem
5
Measures of Dispartion
6
Understanding skewness in data
7
Hypothesis Testing and Estimation

R FOR DATA SCIENCE

1
Introduction to R programing
2
R studio overview
3
Understanding CRAN
4
R studio IDE
5
Basic building block
6
Understanding vector
7
Handling missing values
8
Sub setting vectors
9
Metrics, Dataframes and Arrays
10
Logical statements
11
Lapply, Sapply, Vapply, Tapply, Function
12
Functions
13
Laboratory analysis with R
14
Linear Regression & Logistic Regression
15
Clustering
16
Decision tree & Random Forest
17
Linear discriminant analysis

R FOR DATASCIENCE

1
Bagging and boosting basic concepts
2
Ramdom Forests Overview
3
Association Rules Mining R
4
Data visualization using R

MACHINE LEARNING PART -1

1
Data manipulation with pandas
2
Exploratory Data Analysis
3
Data manipulation, Missing value treatments
4
Outlier Detection and feature Engineering
5
Supervised learning
6
Linear regression
7
Multiple linear regression
8
Logistic regression

MACHINE LEARNING PART 2

1
Machine learning modelling flow
2
How to treat data in machine learning
3
Naive Bayes Classifier
4
Perfomance measures
5
Over fitting and underfitting
6
Optimization Techniques
7
Time series forecasting
8
Dimensionality reduction
9
Support vector machine
10
Image detection and image analysis
11
Under fitting over fitting
12
Ensemble learning models
13
ANN Overview

DATA VISUALIZATION

1
Introduction to data visualization
2
Visualization in depth study
3
Data organization
4
Mapping
5
Excel
6
Tableau
7
Pover BI

Introduction to Neural Networks

1
Training the model
2
Types of Machine learning
3
The linear model
4
Linear model with multiple inputs
5
Linear model with multiple inputs & outputs
6
Graphical representation of simple neural network
7
Objective function
8
Common objective function :L2 norm loss
9
Common objective function : Cross Entropy loss

Optimization Algorithm

1
1- parameter Gradient descent
2
n – parameter Gradient descent

How to build a NN from scratch

1
Basic NN example Part 1
2
Basic NN example Part 2
3
Basic NN example Part 3
4
Exercises

Deep learning Tensorflow 2.0 introduction

1
Outline & comparison with other libraries
2
Tensorflow 1 vs Tensorflow 2
3
A note on tensorflow syntax
4
Types of file formats supporting tensorflow
5
Outlining the model with tensorflow 2
6
Interpreting results
7
Extracting the weights and Bias
8
Customizing a tensorflow 2.0 model
9
Exercises

Introduction to Deep NN

1
Layer
2
Deep Net
3
Digging into a deep net
4
Non linearities and their purpose
5
Activation functions
6
Softmax activation
7
Backpropogation
8
A Peek into the mathematics of optimization

Deep learning Over fitting

1
Over fitting basic concepts
2
Underfitting and overfitting for classification
3
Validation
4
Training, validation & test data sets
5
N-fold cross validation
6
Early stopping / when to stop training

Deep learning Initialization

1
Introduction
2
Types of simple initializations
3
State of the art method(Xavier) Glorot initialization

Gradient descent & learning rate schedules

1
Stochastic Gradient descent
2
Problems
3
Momentum
4
Choosing optimal learning rate
5
Learning rate schedule visualized
6
AdaGrad and RMSprop}
7
Adam(Adaptive moment estimation)

Preprocessing

1
Introduction
2
Types of Basic preprocessing
3
Standardization
4
Preprocessing categorical data
5
Binary and one hot encoding

Case Study Mnist Data set

1
How to tackle MNIST
2
Importing relevant packages and loading the data
3
Preprocess the data(create a validation set & scale it)
4
Scale the test data- exercise
5
Shuffle and Batch
6
Exercises
7
Outline the model
8
Select the loss and the optimizer
9
MNIST learning
10
Exercises
11
Testing the model

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