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 Science Course in Kukatpally
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 Kukatpally
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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