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 

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Description of the Data Science Course

Data Science Course in JNTU

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 JNTU

 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 &
 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
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
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

DBMS - Structured Query Language

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

Importing & Exporting Data

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 


Box whisker

 plot Line plot

Scatter Plot and Heat Maps

Machine Learning

 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

Supervised Machine 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

Unsupervised Machine 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 

Data Preprocessing in Machine Learning

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

Classification Algorithms in Machine Learning

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

K-Nearest Neighbor(KNN) Algorithm in Machine Learning

 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

Decision Tree Classification Algorithm in Machine Learning

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

Random Forest Classifier Algorithm in Machine Learning

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 Regression Algorithm in Machine Learning

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

Support Vector Machine Algorithm

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

Regression Algorithms in Machine Learning

 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

Linear Regression in Machine Learning

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

Simple Linear Regression in Machine Learning

SLR Model
 Implementation of Simple Linear Regression Algorithm using
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

Multiple Linear Regression in Machine Learning

 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

Backward Elimination

 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

Polynomial Regression in Machine Learning

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
 Support Vector Regression (SVR)
 Decision Tree Regression
 Random Forest Regression
 Ridge Regression
 Lasso Regression
 Linear Regression Vs Logistic Regression
 Classification vs Regression 

Clustering Algorithms in Machine Learning

 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 Algorithm in Machine Learning

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
o Training the Hierarchical Clustering Model
o Visualizing the Clusters 

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K-Means Clustering Algorithm in Machine Learning

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
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
o Training the K-means Algorithm on the Training Dataset
o Visualizing the Cluster

Association Rules in Machine Learning

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 in Machine Learning

Apriori Algorithm
 How does Apriori Algorithm Works?
 Apriori Algorithm Example
 Implementation of Apriori Algorithm using Python
 Limitations of Apriori Algorithm

Dimensionality Reduction & Model Selection Boosting

 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 Libraries
 Natural Language Vs Computer Language

Exploring Features of NLTK

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
o Term Frequency
o Inverse Document Frequency
o Term Frequency – Inverse Document Frequency

Deploying a Machine Learning Model on a Web using Flask

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
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

Loading and Reading Data in R 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

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 

Machine Learning using R

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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