Advance Data Science With Machine Learning Using Python

LIVE ONLINE INTERACTIVE

50 Hours

Self Paced Guided Tutorials

10 Hours

Total : 50 Hours

Price : INR 35000

Sponsored Scholarship Available

OVERVIEW

ADVANCED DATA SCIENCE WITH MACHINE LEARNING (USING PYTHON)

(Professional Course)                                                                                         Time: 50 hrs

DATA SCIENCE OVERVIEW:

Data Science is the most comprehensive course in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Introduction to Artificial Intelligence, Natural Language Processing, Predictive Modelling like Python is covered extensively as part of this Data Science training.

WHAT EXACTLY DO YOU MEAN BY DATA SCIENCE?

Data Science can simply be interpreted as the methodology that involves extracting valuable insights from relatively large sets of data. The functioning elements of Data Science are quite multidisciplinary in nature. It is totally related to the unification of different statistical methods & in-depth analysis of data interprets so as to extract the hidden valuable insights form the inbound data. And for a better functionality Data Science has integrated other technologies within itself which include Machine Learning, Data Mining, Cluster Analysis, Information Science, Data Visualization and more.

The ability to enable organizations or enterprises to enhance their decision driving skills is what that has driven an immense craze for the Data Science across the business and industrial sector. At present, Data Science has become the most in-demand career profession.

WHO SHOULD DO THE DATA SCIENCE COURSE?

Professionals who can consider Data Science course as a next logical move to enhance in their careers include:

  • Professional from any domain who has logical, mathematical and analytical skills
  • Professionals working on Business intelligence, Data
  • Warehousing and reporting tools
  • Statisticians, Economists, Mathematicians
  • Software programmers
  • Business analysts
  • Six Sigma consultants
  • Fresher from any stream with good Analytical and logical skills

COURSE HIGHLIGHTS:

  • User Case-Based study
  • Hands-on Live Projects
  • Study material
  • Self Paced modules
  • Interview questions
  • Mock Interview

COURSE MAP

Data Science with Machine Learning (using PYTHON)

 (Professional Course)                                                                                         Time: 50 hrs
  Introduction to Data Science                                                                                                             Duration
 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                                                                                                                                                                2 hrs

 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                                                                                                                                                               4 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

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                                                                                                                                                               6 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

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 Mean

Median Mode

Measures of Spread Variance

Standard Deviation Histogram Probability

Normal Distribution Binary Distribution Poisson distribution Skewness

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

MatplotLib Bar Plot Histogram Plot Box Plot

Area Plot Scatter Plot Pie Plot Seaborn

Module 8                                                                                                                                                               2 hrs

Introduction to Machine Learning

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

 

Module 9                                                                                                                                                                1 hr

 Data Preprocessing

Data preparation

Intro to Scikit Learn

Module 10                                                                                                                                                            2 hrs

 Regression Types Algorithms

Linear Regression

Logistic Regression

Importance of Dimensions

Introduction to Dimensionality

Why Dimensionality Reduction PCA

Factor Analysis

Scaling dimensional model

Implementation with Case study

Intro to Kaggle and UCI repository

 Module 11                                                                                                                                                            8 hrs
Classification
K-nearest neighbors Metrics
Confusion Matrix
Classification report
Support Vector Machines
Working of SVM Naive Bayes
Hyperparameter Optimization Decision Tree Classifier Entropy
Gini Entropy ROC
AUC
Random Forest classifier Linear Discriminant Analysis
Ensemble Techniques and SVM tuning    (Self Paced)
Underfitting & Overfitting
Implementation with Case study
Cross –validation
 Module 12                                                                                                                                                            2 hrs          
Unsupervised learning
Clustering Algorithms
K-Means Clustering
Hierarchical Clustering

 

Implementation with Case study

 Unsupervised Learning   (Self Paced)                                                                                                               1 hr

 Module 13                                                                                                                                                            7 hrs

 NLTK Installation

Tokenize words

Tokenize sentences

Stop words in NLTK

Stemming words with NLTK

Speech tagging

ChatterBot (Self Paced)

Web Scraping

Urllib

BeautifulSoup

Tf-idf Vectorizer

Sentiment analysis

Twitter Sentiment Analysis (Self Paced)

Implementation with Case Studies

Pipelines (Self Paced)

Implementation with Case study

Module 14                                                                                                                                                            2 hrs

Association rule

Association Analysis

Association Rule Parameters

Apriori Algorithm

Market Basket Analysis

 Implementation with 1 Case study

 Module 15                                                                                                                                                           5 hrs

Recommendation Engine

Collaborative filtering

Content Based Filtering

Implementation with Case Study

Surprise Library (Self Paced)

 Introduction to Artificial Intelligence

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. Wine Type Prediction
  4. Titanic dataset
  5. Marketing channel sales prediction

CERTIFICATION

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FEES

 

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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