## DATA SCIENCE

### DATA SCIENCE

*DATA SCIENCE** *: It is an inter-disciplinary field that uses scientific methods, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Power Bi is an all-in-one high-level tool for the data analytics part of **data science**. It can be thought of as less of a programming-language type application, but more of a high-level application akin like Microsoft Excel.

Duration:60 days

#### Data Analysis Courses in Hyderabad

### Course's Key Highlights

100+ hours of learning

Real-time industry professionals curate the course.

Internships and live projects

A cutting-edge training facility

Dedicated staff of placement experts

Placement is guaranteed 100 percent Assistance

28+ Skills That Are Useful in the Workplace

Trainers with a minimum of 12 years of experience

Videos and back-up classes

Subject Matter Experts Deliver Guest Lectures

## Contact Us

### Description of the Data Science Course

##### Data Analysis Courses in Hyderabad

### Why data science is so popular?

*Data Science*: In the wonderful and vast world of data science in 2020, there are a plethora of *options and approaches to analytics and machine-learning*. While most data scientists approach a solution using popular programming languages such as* Python, R, Scala, or even Julia*, there are some higher-level implementations that can get the job done in some cases. Microsoft’s Power-Bi is a great example of this.

You will need a Windows system to install Power-Bi. I’m a Fedora user, so I used Gnome’s Boxes to boot up a new virtual kernel. Boxes is a QEMU GUI that makes it incredibly simple to run multiple virtual systems under one operating system at the same time. Alternatively, you could always use the application’s online version. In my limited experience with the web-friendly version, I discovered that the features are rather lacking in comparison and are frequently split between the two.

### Curriculum for the Data Science

##### Data Science Course in Hyderabad

What is a Data Science?

Who is a Data Scientist?

Who can become a Data Scientist?

What is an Artificial Intelligence?

What is a Machine Learning?

What is a Deep Learning?

Artificial Intelligence Vs Machine Learning Vs Deep Learning

Real Time Process of Data Science

Data Science Real Time Applications

Technologies used in Data Science

Prerequisites Knowledge to Learn Data Science

What is a Machine Learning?

Machine Learning Vs Statistics

Traditional Programming Vs Machine Learning

How Machine Will Learn like Human Learning

Machine Learning Engineer Responsibilities

Types of Machine Learning

Supervised learning

Un-Supervised learning

Reinforcement Learning

PYTHON Programming Introduction

History of Python

Python is Derived from?

Python Features

Python Applications

Why Python is Becoming Popular Now a Day?

Existing Programming Vs Python Programming

Writing Programs in Python Top Companies Using Python

Python Programming Modes

o Interactive Mode Programming

o Scripting Mode Programming

Flavors in Python, Python Versions

Download & Install the Python in Windows & Linux

How to set Python Environment in the System?

Anaconda – Data Science Distributor

Downloading and Installing Anaconda, Jupyter Notebook &

Spyder

Python IDE – Jupyter Notebook Environment

Python IDE – Spyder Environment

Python Identifiers(Literals), Reserved Keywords

Variables, Comments

Lines and Indentations, Quotations

Assigning Values to Variables

Data Types in Python

Mutable Vs Immutable

Fundamental Data Types: int, float, complex, bool, str

Number Data Types: Decimal, Binary, Octal, Hexa Decimal &

Number Conversions

Inbuilt Functions in Python

Data Type Conversions

Priorities of Data Types in Python

Python Operators

o Arithmetic Operators

o Comparison (Relational) Operators

o Assignment Operators

o Logical Operators

o Bitwise Operators

o Membership Operators

o Identity Operators

Slicing & Indexing

o Forward Direction Slicing with +ve Step

o Backward Direction Slicing with -ve Step

Decision Making Statements

o if Statement

o if-else Statement

o elif Statement

Looping Statements

o Why we use Loops in python?

o Advantages of Loops

o for Loop

o Nested for Loop

o Using else Statement with for Loop

o while Loop

o Infinite while Loop

o Using else with Python while Loop

Conditional Statements

o break Statement

o continue Statement

o Pass Statement

Advanced Data Types: List, Tuple, Set, Frozenset, Dictionary,

Range, Bytes & Bytearray, None

List Data Structure

o List indexing and splitting

o Updating List values

o List Operations

o Iterating a List

o Adding Elements to the List

o Removing Elements from the List

o List Built-in Functions

o List Built-in Methods

Tuple Data Structure

o Tuple Indexing and Splitting

o Tuple Operations

o Tuple Inbuilt Functions

o Where use Tuple

o List Vs Tuple

o Nesting List and Tuple

Set Data Structure

o Creating a Set

o Set Operations

o Adding Items to the Set

o Removing Items from the Set

o Difference Between discard() and remove()

o Union of Two Sets

o Intersection of Two Sets

o Difference of Two Sets

o Set Comparisons

Frozenset Data Structure

Dictionary Data Structure

o Creating the Dictionary

o Accessing the Dictionary Values o Updating Dictionary Values

o Deleting Elements Using del Keyword

o Iterating Dictionary

o Properties of Dictionary Keys

o Built-in Dictionary Functions

o Built-in Dictionary Methods

List Vs Tuple Vs Set Vs Frozenset Vs Dict

Range, Bytes, Bytearray & None

Python Functions

o Advantage of Functions in Python

o Creating a Function

o Function Calling

o Parameters in Function

o Call by Reference in Python

o Types of Arguments

Required Arguments

Keyword Arguments

Default Arguments

Variable-Length Arguments

Scope of Variables

Python Built-in Functions

Python Lambda Functions

String with Functions

o Strings Indexing and Splitting

o String Operators

o Python Formatting Operator

o Built-in String Functions

Python File Handling

o Opening a File

o Reading the File

o Read Lines of the File

o Looping through the File

o Writing the File

o Creating a New File

o Using with Statement with Files

o File Pointer Positions

o Modifying File Pointer Position

o Renaming the File & Removing the File

o Writing Python Output to the Files

o File Related Methods

Python Exceptions

o Common Exceptions

o Problem without Handling Exceptions o except Statement with no Exception

o Declaring Multiple Exceptions

o Finally Block

o Raising Exceptions

o Custom Exception

Python Packages

o Python Libraries

o Python Modules

Collection Module

Math Module

OS Module

Random Module

Statistics Module

Sys Module

Date & Time Module

o Loading the Module in our Python Code

import Statement

from-import Statement

o Renaming a Module

Regular Expressions

Command Line Arguments

Object Oriented Programming (OOPs)

o Object-oriented vs Procedure-oriented Programming

languages

o Object

o Class

o Method

o Inheritance

o Polymorphism

o Data Abstraction

o Encapsulation

Python Class and Objects

o Creating Classes in Python

o Creating an Instance of the Class

Python Constructor

o Creating the Constructor in Python

o Parameterized Constructor

o Non-Parameterized Constructor

o In-built Class Functions

o In-built Class Attributes

Python Inheritance

o Python Multi-Level Inheritance

o Python Multiple Inheritance o Method Overriding

o Data Abstraction in Python

Graphical User Interface (GUI) Programming

Python TKinter

o Tkinter Geometry

pack() Method

grid() Method

place() Method

o Tkinter Widgets

NumPy Introduction

o What is NumPy

o The Need of NumPy

NumPy Environment Setup

N-Dimensional Array (Ndarray)

o Creating a Ndarray Object

o Finding the Dimensions of the Array

o Finding the Size of Each Array Element

o Finding the Data Type of Each Array Item

o Finding the Shape and Size of the Array

o Reshaping the Array Objects

o Slicing in the Array

o Finding the Maximum, Minimum, and Sum of the Array

Elements

o NumPy Array Axis

o Finding Square Root and Standard Deviation

o Arithmetic Operations on the Array

o Array Concatenation

NumPy Datatypes

o NumPy dtype

o Creating a Structured Data Type

Numpy Array Creation

o Numpy.empty

o Numpy.Zeros

o NumPy.ones

Numpy Array from Existing Data

o Numpy.asarray

Numpy Arrays within the Numerical Range

o Numpy.arrange

o NumPy.linspace

o Numpy.logspace

NumPy Broadcasting o Broadcasting Rules

NumPy Array Iteration

o Order of Iteration

F-Style Order

C-Style Order

o Array Values Modification

NumPy String Functions

NumPy Mathematical Functions

o Trigonometric Functions

o Rounding Functions

NumPy Statistical functions

o Finding the Min and Max Elements from the Array

o Calculating Median, Mean, and Average of Array Items

NumPy Sorting and Searching

NumPy Copies and Views

NumPy Matrix Library

NumPy Linear Algebra

NumPy Matrix Multiplication in Python

Pandas Introduction & Pandas Environment Setup

o Key Features of Pandas

o Benefits of Pandas

o Python Pandas Data Structure

Series

DataFrame

Panel

Pandas Series

o Creating a Series

Create an Empty Series

Create a Series using Inputs

o Accessing Data from Series with Position

o Series Object Attributes

o Retrieving Index Array and Data Array of a Series Object

o Retrieving Types (dtype) and Size of Type (itemsize)

o Retrieving Shape

o Retrieving Dimension, Size and Number of Bytes

o Checking Emptiness and Presence of NaNs

o Series Functions

Pandas DataFrame

o Create a DataFrame

Create an Empty DataFrame

Create a DataFrame using Inputs Column Selection, Addition & Deletion

Row Selection, Addition & Deletion

DataFrame Functions

Merging, Joining & Combining DataFrames

Pandas Concatenation

Pandas Time Series

o Datetime

o Time Offset

o Time Periods

o Convert String to Date

Viewing/Inspecting Data (loc & iloc)

Data Cleaning

Filter, Sort, and Groupby

Statistics on DataFrame

Pandas Vs NumPy

DataFrame Plotting

o Line: Line Plot (Default)

o Bar: Vertical Bar Plot

o Barh: Horizontal Bar Plot

o Hist: Histogram Plot

o Box: Box Plot

o Pie: Pie Chart

o Scatter: Scatter Plot

Introduction & Models of DBMS

SQL & Sub Language of SQL

Data Definition Language (DDL)

Data Manipulation Language (DML)

Data Query/Retrieval Language (DQL/DRL)

Transaction Control Language (TCL)

Data Control Language (DCL)

Installation of MySQL & Database Normalization

Sub Queries & Key Constraints

Aggregative Functions, Clauses & Views

Data Extraction from CSV (pd.read_csv)

Data Extraction from TEXT File (pd.read_table)

Data Extraction from CLIPBOARD (pd.read_clipboard)

Data Extraction from EXCEL (pd.read_excel)

Data Extraction from URL (pd.read_html)

Writing into CSV (df.to_cs Writing into EXCEL (df.to_excel)

Data Extraction from DATABASES

o Python MySQL Database Connection

Import mysql.connector Module

Create the Connection Object

Create the Cursor Object

Execute the Query

Data Visualization Introduction

Tasks of Data Visualization

Benefit of Data Visualization

Plots for Data Visualization

Matplotlib Architecture

General Concept of Matplotlib

MatPlotLib Environment Setup

Verify the MatPlotLib Installation

Working with PyPlot

Formatting the Style of the Plot

Plotting with Categorical Variables

Multi-Plots with Subplot Function

Line Graph

Bar Graph

Histogram

Scatter Plot

Pie Plot

3Dimensional – 3D Graph Plot

mpl_toolkits

Functions of MatPlotLib

Contour Plot, Quiver Plot, Violin Plot

3D Contour Plot

3D Wireframe Plot

3D Surface Plot

Box Plot

o What is a Boxplot?

o Mean, Median, Quartiles, Outliers

o Inter Quartile Range (IQR), Whiskers

o Data Distribution Analysis

o Boxplot on a Normal Distribution

o Probability Density Function

o 68–95–99.7 Rule (Empirical rule)

Bar Chart Histogram Box whisker plot Line plot Scatter Plot and Heat Maps |

What is Machine Learning

Importance of Machine Learning

Need for Machine Learning

Statistics Vs Machine Learning

Traditional Programming Vs Machine Learning

How Machine Learning like Human Learning

How does Machine Learning Work?

Machine Learning Engineer Responsibilities

Life Cycle of Machine Learning

o Gathering Data

o Data preparation

o Data Wrangling

o Analyze Data

o Train the model

o Test the model

o Deployment

Features of Machine Learning

History of Machine Learning

Applications of Machine Learning

Types of Machine Learning

o Supervised Machine Learning

o Unsupervised Machine Learning

o Reinforcement Learning

How Supervised Learning Works?

Steps Involved in Supervised Learning

Types of supervised Machine Learning Algorithms

o Classification

o Regression

Advantages of Supervised Learning

Disadvantages of Supervised Learning

How Unsupervised Learning Works?

Why use Unsupervised Learning?

Types of Unsupervised Learning Algorithm

o Clustering

o Association

Advantages of Unsupervised Learning

Disadvantages of Unsupervised Learning

Supervised Vs Unsupervised Learning Reinforcement Machine Learning

How to get Datasets for Machine Learning?

o What is a Dataset?

o Types of Data in Datasets

o Popular Sources for Machine Learning Datasets

Why do we need Data Preprocessing?

o Getting the Dataset

o Importing Libraries

o Importing Datasets

o Finding Missing Data

By Deleting the Particular Row

By Calculating the Mean

o Encoding Categorical Data

LableEncoder

OneHotEncoder

o Splitting Dataset into Training and Test Set

o Feature Scaling

Standardization

Normalization

What is the Classification Algorithm?

Types of Classifications

o Binary Classifier

o Multi-class Classifier

Learners in Classification Problems

o Lazy Learners

o Eager Learners

Types of ML Classification Algorithms

o Linear Models

Logistic Regression

Support Vector Machines

o Non-linear Models

K-Nearest Neighbors

Naïve Bayes

Decision Tree Classification

Random Forest Classification

Kernel SVM

Evaluating a Classification Model

o Confusion Matrix

What is a Confusion Matrix? True Positive

True Negative

False Positive – Type 1 Error

False Negative – Type 2 Error

Why need a Confusion matrix?

Precision

Recall

Precision vs Recall

F1-score

Confusion Matrix in Scikit-Learn

Confusion Matrix for Multi-Class Classification

o Log Loss or Cross-Entropy Loss

o AUC-ROC curve

Use cases of Classification Algorithms

Why do we Need a K-NN Algorithm?

How does K-NN work?

o What is Euclidean Distance

o How it Calculates the Distance

How to Select the Value of K in the K-NN Algorithm?

Advantages of KNN Algorithm

Disadvantages of KNN Algorithm

Python Implementation of the KNN Algorithm

Analysis on Social Network Ads Dataset

Steps to Implement the K-NN Algorithm

o Data Pre-processing Step

o Fitting the K-NN algorithm to the Training Set

o Predicting the Test Result

o Test Accuracy of the Result (Creation of Confusion Matrix)

o Visualizing the Test Set Result.

o Improve the Performance of the K-NN Model

Why is it Called Naïve Bayes?

o Naïve Means?

o Bayes Means?

Bayes’ Theorem

o Posterior Probability

o Likelihood Probability

o Prior Probability

o Marginal Probability

Working of Naïve Bayes’ Classifier Advantages of Naïve Bayes Classifier

Disadvantages of Naïve Bayes Classifier

Applications of Naïve Bayes Classifier

Types of Naïve Bayes Model

o Gaussian Naïve Bayes Classifier

o Multinomial Naïve Bayes Classifier

o Bernoulli Naïve Bayes Classifier

Python Implementation of the Naïve Bayes Algorithm

Steps to Implement the Naïve Bayes Algorithm

o Data Pre-processing Step

o Fitting Naive Bayes to the Training set

o Predicting the Test Result

o Test Accuracy of the Result (Creation of Confusion matrix)

o Visualizing the Test Set Result

o Improve the Performance of the Naïve Bayes Model

Why use Decision Trees?

Types of Decision Trees

o Categorical Variable Decision Tree

o Continuous Variable Decision Tree

Decision Tree Terminologies

How does the Decision Tree Algorithm Work?

Attribute Selection Measures

o Entropy

o Information Gain

o Gini index

o Gain Ratio

Algorithms used in Decision Trees

o ID3 Algorithm → (Extension of D3)

o C4.5 Algorithm→ (Successor of ID3)

o CART Algorithm → (Classification & Regression Tree)

How to Avoid/Counter Overfitting in Decision Trees?

o Pruning Decision Trees

o Random Forest

Pruning: Getting an Optimal Decision tree

Advantages of the Decision Tree

Disadvantages of the Decision Tree

Python Implementation of Decision Tree

Steps to Implement the Decision Tree Algorithm

o Data Pre-processing Step

o Fitting a Decision-Tree Algorithm to the Training Set

o Predicting the Test Result Test Accuracy of the Result (Creation of Confusion matrix)

o Visualizing the Test Set Result

o Improve the Performance of the Decision Tree Model

Working of the Random Forest Algorithm

Assumptions for Random Forest

Why use Random Forest?

How does Random Forest Algorithm Work?

o Ensemble Techniques

o Bagging (Bootstrap Aggregation)

Applications of Random Forest

Disadvantages of Random Forest

Python Implementation of Random Forest Algorithm

Steps to Implement the Random Forest Algorithm:

o Data Pre-processing Step

o Fitting the Random Forest Algorithm to the Training Set

o Predicting the Test Result

o Test Accuracy of the Result (Creation of Confusion Matrix)

o Visualizing the Test Set Result

o Improving the Performance of the Random Forest Model

Logistic Function (Sigmoid Function)

Assumptions for Logistic Regression

Logistic Regression Equation

Type of Logistic Regression

o Binomial Logistic Regression

o Multinomial Logistic Regression

o Ordinal Logistic Regression

Python Implementation of Logistic Regression (Binomial)

Steps to Implement the Logistic Regression:

o Data Pre-processing Step

o Fitting Logistic Regression to the Training Set

o Predicting the Test Result

o Test Accuracy of the Result (Creation of Confusion Matrix)

o Visualizing the Test Set Result

o Improve the Performance of the Logistic Regression Model

Types of Support Vector Machines

o Linear Support Vector Machine

o Non-Linear Support Vector Machine Hyperplane in the SVM Algorithm

Support Vectors in the SVM Algorithm

How does SVM Works?

o How does Linear SVM Works?

o How does Non-Linear SVM Works?

Python Implementation of Support Vector Machine

Steps to Implement the Support Vector Machine:

o Data Pre-processing Step

o Fitting Support Vector Machine to the Training Set

o Predicting the Test Result

o Test Accuracy of the Result (Creation of Confusion Matrix)

o Visualizing the Test Set Result

o Improve the Performance of the Support Vector Machine

Model

Terminologies Related to the Regression Analysis

o Dependent Variable

o Independent Variable

o Outliers

o Multi-collinearity

o Under fitting and Overfitting

Why do we use Regression Analysis?

Types of Regression

o Linear Regression

o Logistic Regression

o Polynomial Regression

o Support Vector Regression

o Decision Tree Regression

o Random Forest Regression

o Ridge Regression

o Lasso Regression

Types of Linear Regression

o Simple Linear Regression

o Multiple Linear Regression

Linear Regression Line

o Positive Linear Relationship

o Negative Linear Relationship

Finding the Best Fit Line

o Cost Function

o Gradient Descent o Model Performance

o R-Squared Method

Assumptions of Linear Regression

SLR Model

Implementation of Simple Linear Regression Algorithm using

Python

o Data Pre-processing Step

o Fitting Simple Linear Regression to the Training Set

o Predicting the Test Result

o Test Accuracy of the

o Visualizing the Test Set Result.

o Try to Improve the Performance of the Model

MLR Equation

Assumptions for Multiple Linear Regression

Implementation of Multiple Linear Regression model using Python

o Data Pre-processing Step

o Fitting Multiple Linear Regression to the Training Set

o Predicting the Test Result

o Test Accuracy of the

o Visualizing the Test Set Result.

o Try to Improve the Performance of the Model

What is Backward Elimination?

Steps of Backward Elimination

Need for Backward Elimination: An optimal Multiple Linear

Regression model

Implement the Steps for Backward Elimination method

Need for Polynomial Regression

Equation of the Polynomial Regression Model

Implementation of Polynomial Regression using Python

Steps for Polynomial Regression:

o Data Pre-processing

o Build a Linear Regression Model

o Build a Polynomial Regression Model

o Visualize the Result for Linear Regression Model

o Visualize the Result for Polynomial Regression Model o Predicting the Final Result with the Linear Regression Model

o Predicting the Final Result with the Polynomial Regression

Model

Support Vector Regression (SVR)

Decision Tree Regression

Random Forest Regression

Ridge Regression

Lasso Regression

Linear Regression Vs Logistic Regression

Classification vs Regression

Types of Clustering Methods

o Partitioning Clustering

o Density-Based Clustering

o Distribution Model-Based Clustering

o Hierarchical Clustering

o Fuzzy Clustering

Clustering Algorithms

o K-Means Algorithm

o Mean-shift Algorithm

o DBSCAN Algorithm

o Expectation-Maximization Clustering using GMM

o Agglomerative Hierarchical Algorithm

o Affinity Propagation

Applications of Clustering

Hierarchical Clustering Technique Approaches

Why Hierarchical Clustering?

Agglomerative Hierarchical Clustering

How the Agglomerative Hierarchical Clustering Work?

Measure for the Distance between two Clusters

o Single Linkage

o Complete Linkage

o Average Linkage

o Centroid Linkage

Working of Dendrogram in Hierarchical Clustering

Hierarchical Clustering Example with Scratch Data

Python Implementation of Agglomerative Hierarchical Clustering

Steps for Implementation of Agglomerative Hierarchical

Clustering using Python

o Data Pre-processing o Finding the Optimal Number of Clusters using the

Dendrogram

o Training the Hierarchical Clustering Model

o Visualizing the Clusters

Toggle Content

What is K-Means Algorithm?

How does the K-Means Algorithm Work?

How to Choose the Value of “K Number of Clusters” in K-Means

Clustering?

o Elbow Method

o Within Cluster Sum of Squares (WCSS)

K-Means Clustering Example with Scratch Data

Python Implementation of K-means Clustering Algorithm

Steps to Implement of K-means Clustering Algorithm

o Data Pre-processing

o Finding the Optimal Number of Clusters using the Elbow

Method

o Training the K-means Algorithm on the Training Dataset

o Visualizing the Cluster

Association Rules

Pattern Detection

Market Basket Analysis

Support, Confidence, Expected Confidence, Lift

Finding Item Sets with High Support

Finding Item Rules with High Confidence or Lift

Apriori Algorithm

How does Apriori Algorithm Works?

Apriori Algorithm Example

Implementation of Apriori Algorithm using Python

Limitations of Apriori Algorithm

Dimensionality Reduction

o Principal Component Analysis (PCA)

o Linear Discriminant Analysis (LDA)

o Kernel PCA

Model Selection Boosting

o Model Selection Grid Search

K-Fold Cross Validation

o XGBoost

Mean, Median and Mode

Data Variability, Range, Quartiles

IQR, Calculating Percentiles

Variance, Standard Deviation, Statistical Summaries

Types of Distributions – Normal, Binomial, Poisson

Probability Distributions & Skewness

Data Distribution, 68–95–99.7 rule (Empirical rule)

Descriptive Statistics and Inferential Statistics

Statistics Terms and Definitions, Types of Data

Data Measurement Scales, Normalization, Standardization

Measure of Distance, Euclidean Distance

Probability Calculation – Independent & Dependent

Entropy, Information Gain

Regression

Natural Language Processing Introduction

o What is NLP?

o History of NLP

o Advantages of NLP

o Disadvantages of NLP

Components of NLP

o Natural Language Understanding (NLU)

o Natural Language Generation (NLG)

o Difference between NLU and NLG

Applications of NLP

How to build an NLP Pipeline?

Phases of NLP

o Lexical Analysis and Morphological

o Syntactic Analysis (Parsing)

o Semantic Analysis

o Discourse Integration

o Pragmatic Analysis

Why NLP is Difficult?

NLP APIs

NLP Libraries

Natural Language Vs Computer Language

o Open the Text File for Processing

o Import Required Libraries

o Sentence Tokenizing

o Word Tokenizing

o Find the Frequency Distribution

o Plot the Frequency Graph

o Remove Punctuation Marks

o Plotting Graph without Punctuation Marks

o List of Stopwords

o Removing Stopwords

o Final Frequency Distribution

Word Cloud

o Word Cloud Properties

o Python Code Implementation of the Word Cloud

o Word Cloud with the Circle Shape

o Word Cloud Advantages

o Word Cloud Disadvantages

Stemming

o Stemmer Examples

o Stemming Algorithms

Porter’s Stemmer

Lovin’s Stemmer

Dawson’s Stemmer

Krovetz Stemmer

Xerox Stemmer

Snowball Stemmer

Lemmatization

o Difference between Stemmer and Lemmatizer

o Demonstrating how a lemmatizer works

o Lemmatizer with default PoS value

o Demonstrating the power of lemmatizer

o Lemmatizer with different POS values

Part-of-Speech (PoS) Tagging

o Why do we need Part of Speech (POS)?

o Part of Speech (PoS) Tags

Chunking

o Categories of Phrases

o Phrase Structure Rules

Chinking

Named Entity Recognition (NER)

o Use-Cases

o Commonly used Types of Named Entity WordNet

Bag of Words

o What is the Bag-of-Words method?

o Creating a basic Structure on Sentences

o Words with Frequencies

o Combining all the Words

o Final Model of our Bag of Words

o Applications & Limitations

TF-IDF

o Term Frequency

o Inverse Document Frequency

o Term Frequency – Inverse Document Frequency

What is Model Deployment?

What is Flask?

Installing Flask on your Machine

Understanding the Problem Statement

Build our Machine Learning Model

Create the Webpage

Connect the Webpage with the Model

Working of the Deployed Mode

What is Deep Learning?

Deep learning Process

Types of Deep Learning Networks

o Deep Neural Networks

o Artificial Neural Networks

o Convolutional Neural Networks

o Recurrent Neural Networks

TensorFlow

o History of TensorFlow

o Components of TensorFlow

o Use Cases/Applications of TensorFlow

o Features of TensorFlow

Installation of TensorFlow through pip & conda

Advantage and Disadvantage of TensorFlow

TensorFlow Playground

Introduction to Keras, OpenCV & Theano

Implementation of Deep Learning

What is Artificial Intelligence?

o Why Artificial Intelligence?

o Goals of Artificial Intelligence

o What Comprises to Artificial Intelligence?

o Advantages of Artificial Intelligence

o Disadvantages of Artificial Intelligence

Applications of Artificial Intelligence

History of Artificial Intelligence

Types of Artificial Intelligence

Types of AI Agents

o Simple Reflex Agent

o Model-Based Reflex Agent

o Goal-Based Agents

o Utility-Based Agent

o Learning Agent

Search Algorithms in Artificial Intelligence

o Search Algorithm Terminologies

o Properties of Search Algorithms

o Types of Search Algorithms

Subsets of Artificial Intelligence

Implementation of Artificial Intelligence

Why R Programming is Important?

Why Learn R?

History of Python

Features of R

Applications of R

Comparison between R and Python

Which is Better to Choose

Pros and Cons of R

Companies using R

R Packages

Downloading and Installing R

What is CRAN?

Setting R Environment:

o Search Packages in R Environment

o Search Packages in Machine with inbuilt function and

manual searching

o Attach Packages to R Environment

o Install Add-on Packages from CRAN o Detach Packages from R Environment

o Functions and Packages Help

R Programming IDE

o RStudio

o Downloading and Installing RStudio

Variable Assignment

o Displaying Variables

o Deleting Variables

Comments

o Single Line

o Multi Line Comments

Data Types

o Logical

o Integer

o Double

o Complex

o Character

Operators Naming List Elements

o Accessing List Elements

o Manipulating List Elements

o Merging Lists

o Converting List to Vector

Matrix

o Creating a Matrix

o Accessing Elements of a Matrix

o Matrix Manipulations

o Dimensions of Matrix

o Transpose of Matrix

Data Frames

o Create Data Frame

o Vector to Data Frame

o Character Data Converting into Factors: StringsAsFactors

o Convert the columns of a data frame to characters

o Extract Data from Data Frame

o Expand Data Frame, Column Bind and Row Bind

Merging / Joining Data Frames

o Inner Join

o Outer Join

o Cross Join

Arrays

o Create Array with Multiple Dimensions

o Naming Columns and Rows

o Accessing Array Elements

o Manipulating Array Elements

o Calculations across Array Elements

Factors

o Factors in Data Frame

o Changing the Order of Levels

o Generating Factor Levels

o Deleting Factor Levels

o Arithmetic Operators

o Relational Operators

o Logical Operators

o Assignment Operators

o R as Calculator

o Performing different Calculations

Functions

o Inbuilt Functions

o User Defined Functions

STRUCTURES

o Vector

o List

o Matrix

o Data frame

o Array

o Factors

Inbuilt Constants & Functions

Vectors

o Vector Creation

o Single Element Vector

o Multiple Element Vector

o Vector Manipulation

o Sub setting & Accessing the Data in Vector

Lists

o Creating a List

Data Extraction from CSV

o Getting and Setting the Working Directory

o Input as CSV File, Reading a CSV File

o Analyzing the CSV File, Writing into a CSV File

Data Extraction from URL

Data Extraction from CLIPBOARD

Data Extraction from EXCEL o Install “xlsx” Package

o Verify and Load the “xlsx” Package, Input as “xlsx” File

o Reading the Excel File, Writing the Excel File

Data Extraction from DATABASES

o RMySQL Package, Connecting to MySql

o Querying the Tables, Query with Filter Clause

o Updating Rows in the Tables, Inserting Data into the Tables

o Creating Tables in MySql, Dropping Tables in MySql

o Using dplyr and tidyr package

Data Pre-processing

Classification Algorithms

o K Nearest Neighbors Classification

o Naive Bayes Classification

o Decision Tree Classification

o Random Forest Classification

o Support Vector Machine Classification

o Logistic Regression

o Kernel SVM

Regression Algorithms

o Simple Linear Regression

o Multiple Linear Regression

o Polynomial Regression

o Support Vector Regression

o Decision Tree Regression

o Random Forest Regression

Clustering Algorithms

o K-Means Clustering

o Hierarchical Clustering

Association Rule Algorithms

o Apriori

o Eclat

Dimensionality-Reduction

o Principal Component Analysis

o Linear Discriminant Analysis

o Kernal PCA

Model Selection & Boosting

o Grid Search

o K Fold Cross Validation

o XGBoost

Natural Language Processing

Deep Learning – Artificial Neural Networks

Explore Weka Machine Learning Toolkit

o Installation of WEKA

o Features of WEKA Toolkit

o Explore & Load data sets in Weka

Perform Data Preprocessing Tasks

o Apply Filters on Data Sets

Performing Classification on Data Sets

o J48 Classification Algorithm

o Decision Trees Algorithm

o K-NN Classification Algorithm

o Naive-Bayes Classification Algorithm

o Comparing Classification Results

Performing Regression on Data Sets

o Simple Linear Regression Model

o Multi Linear Regression Model

o Logistic Regression Model

o Cross-Validation and Percentage Split

Performing Clustering on Data Sets

o Clustering Techniques in Weka

o Simple K-means Clustering Algorithm

o Association Rule Mining on Data Sets

o Apriori Association Rule Algorithm

o Discretization in the Rule Generation Process

Graphical Visualization in Weka

o Visualization Features in Weka

o Visualize the data in various dimensions

o Plot Histogram, Derive Interesting Insights

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