Advance Data Science With Machine Learning Using Python


40 Hours

Self Paced Guided Tutorials

10 Hours

Total : 50 Hours

Price : INR 35000

Advanced Data Science with Machine Learning (using PYTHON)

( Professional Course )                                    Time : 50 hrs

Become a Data Scientist

Data science is the new emerging field in today world with high growth and career perspective. It’s right time to move your career in data science by starting from basics of Data analytics and management to advanced topics like in Machine Learning with industrial case study. This course provides in-depth understanding of how data science integrates in various industrial verticals such as healthcare, banking, telecom, e-commerce, transportation and more. The course is a mix of instructor-led training and guided self paced tutorial .

Advanced Data Science with Machine Learning (using PYTHON)

( Professional Course )                                    Time : 50 hrs


Introduction to Data Science


Module 1                                                                                                2 hrs

Introduction to Data Science

Data Science Era

Data Science involvement in Industries

Business Intelligence vs Data Science

Data Science Life Cycle

Tools of Data Science

Introduction to Python

Introduction to Machine Learning



Module 2

Introduction to Python Programming
Introduction to Python
Basic Operations in Python
Variable Assignment
Functions: in-built functions, user defined functions
Condition: if, if-else, nested if-else, else-if

Module 3                                                                                                   2 hrs

Data Structure – Introduction
List: Different Data Types in a List, List in a List
Operations on a list: Slicing, Splicing, Sub-setting
Condition(true/false) on a List
Applying functions on a List
Dictionary: Index, Value
Operation on a Dictionary: Slicing, Splicing, Sub-setting
Condition(true/false) on a Dictionary
Applying functions on a Dictionary

Modules and Packages

Numpy Array: Data Types in an Array, Dimensions of an Array         2 hrs
Operations on Array: Indexing ,Slicing, Splicing, Sub-setting
Conditional(T/F) on an Array
Loops: For, While
Shorthand for For
Conditions in shorthand for For

Control statements

Shape Manipulation

Linear Algebra


Module 4                                                                                                         2 hrs

Python Pandas – Home
Python Pandas – Introduction
Python Pandas – Environment Setup
Introduction to Data Structures
Python Pandas – Series
Python Pandas – DataFrame
Python Pandas – Panel
Python Pandas – Basic Functionality
Function Application
Python Pandas – Reindexing
Python Pandas – Iteration
Python Pandas – Sorting
Working with Text Data
Options & Customization
Indexing & Selecting Data
Python Pandas – Window Functions
Python Pandas – Aggregations                                                   2 hrs
Python Pandas – Missing Data
Python Pandas – GroupBy
Python Pandas – Merging/Joining
Python Pandas – Concatenation
Python Pandas – Date Functionality
Python Pandas – Categorical Data
Python Pandas – Visualization

Module 5                                                                                          2 hrs


Intro to Statistics

Statistical Inference

Terminologies of Statistics

Descriptive statistics

Statistical functions

Measures of Centers




Measures of Spread


Standard Deviation



Normal Distribution

Binary Distribution

Poisson distribution


Bell curve

Hypothesis Building and Testing

Chi-Square Test

Correlation Matrix


Module 6                                                                                                2 hrs

Scientific computing with Python

SciPy and its Characteristics

SciPy sub-packages

SciPy sub-packages –Integration

SciPy sub-packages – Optimize

Linear Algebra

SciPy sub-packages – Statistics

SciPy sub-packages – Weave

SciPy sub-packages – I O


Module 7                                                                                                2 hrs


Data Analysis Pipeline

What is Data Extraction

Types of Data

Raw and Processed Data

Data Wrangling

Exploratory Data Analysis

Visualization of Data

    Bar Plot
    Histogram Plot
    Box Plot
    Area Plot
    Scatter Plot
    Pie Plot



Module 8                                                                                                   2 hrs

Introduction to Machine Learning

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Module 9                                                                                                  
Data Preprocessing
Data preparation
Intro to Scikit Learn

Module 10                                                                                                  2 hrs                                                                        





Linear Regression

RMSE                                                            (Self Paced)                          1hrs

Logistic Regression                                                                                   2 hrs
Importance of Dimensions                                                                     2 hrs

Introduction to Dimensionality

Why Dimensionality Reduction


Factor Analysis

Scaling dimensional model


Implementation with Case Studies


Intro to Kaggle and UCI repository


Module 11                                                                                                 2 hrs

K-nearest neighbours


Confusion Matrix

Classification report

Support Vector Machines                                                                       2 hrs
Working of SVM
Naive Bayes

Hyperparameter Optimization

Decision Tree Classifier

Random Forest Classifier

2 hrs

Ensemble Techniques and SVM tuning    (Self Paced)                   2 hrs

Underfitting & Overfitting

 AUC –ROC Curve                                          (Self Paced)                    1 hrs


Implementation with Case Studies

Cross –validation                                           (Self Paced)                     2 hrs


Module 12                                                                                                 2 hrs

Unsupervised learning
Clustering Algorithms

K-Means Clustering

Hierarchical Clustering

Implementation with Case Studies


Unsupervised Learning                               (Self Paced)                        1 hrs


Module 13                                                                                                  2 hrs   

NLTK Installation

Tokenize words

Tokenize sentences

Stop words in NLTK

Stemming words with NLTK

Speech tagging POS

ChatterBot                                                   (Self Paced)                         1hrs

Web Scraping                                                                                            2 hrs



Tf-idf Vectorizer

Sentiment analysis

Twitter Sentiment Analysis                         (Self Paced)                         1hrs

Implementation with Case Studies

Pipelines                                                          (Self Paced)                       1 hrs

Module 14                                                                                                     2 hrs


Recommendation Engine                                                                           2hrs

Collaborative filtering

Content Based Filtering

Implementation with Case Study

Surprise Library                                                  (Self Paced)                       1 hrs


Introduction to Artificial Intelligence          (Self Paced)                        2hrs    

Tensorflow library

Keras library


The course will be covering  industrial real time case study .

At the end any one of the Capstone projects will be assigned.

Projects :                                                                                               4 hrs

1.      Customer churn prediction

2.      Bank fraud Loan Prediction

3.      Flight Price Prediction

4.      Doctor Consultation Fee Prediction

5.      Marketing Channel Sales Prediction



Course Highlights :


Use Case Based study
Hands on Live Projects

Study material

Self –Paced module

Interview questions

Mock Interview




Fee In India


Application Fees INR 0
Total Program Fees INR 35,000
The Program Fees can also be paid in installments
Registration  Fees INR 5000
First installment of the Program Fees

(Needs To Be Paid Before Batch Start Date)

INR 17,000
Second installment of the Program Fees

(Needs To Be Paid in the 6th Class)

INR 13,000
Scholarship Up to 50% Available

10%  Extra discount on Lumpsum Payment



Fee In U.S.


Application Fees INR 0
Total Program Fees $ 500
The Program Fees can also be paid in installments
Registration  Fees $ 100 (Non-Refundable)
First installment of the Program Fees

(Needs To Be Paid Before Batch Start Date)

$ 200
Second installment of the Program Fees

(Needs To Be Paid in the 6th Class)

$ 200
Scholarship Up to 50% Available

10%  Extra discount on Lumpsum Payment



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