Artificial Intelligence Online Training in India

Installation and Working with Python

Understanding Python variables

Python basic Operators

Understanding the Python blocks.

Variables, expression condition and function

Global and Local Variables in Python

Packing and Unpacking Arguments

Type Casting in Python

Byte objects vs. string in Python

Variable Scope

Declaring and usingNumeric data types

Using stringdata type and string operations

Understanding Non-numeric data types

Understanding the concept of Casting and Boolean.

Strings

List

Tuples

Dictionary

Sets

Python Keyword and Identifiers

Python Comments, Multiline Comments.

Python Indentation

Understating the concepts of Operators

List

• What is List.

• List Creation

• List Length

• List Append

• List Insert

• List Remove

• List Append & Extend using “+” and Keyword

• List Delete

• List related Keyword in Python

• List Revers

• List Sorting

• List having Multiple Reference

• String Split to create a List

• List Indexing

• List Slicing

• List count and Looping

• List Comprehension and Nested Comprehension

Dictionary

• Dict Creation

• Dict Access (Accessing Dict Values)

• Dict Get Method

• Dict Add or Modify Elements

• Dict Copy

• Dict From Keys.

• Dict Items

• Dict Keys (Updating, Removing and Iterating)

• Dict Values

• Dict Comprehension

• Default Dictionaries

• Ordered Dictionaries

• Looping Dictionaries

• Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets

• What is Set

• Set Creation

• Add element to a Set

• Remove elements from a Set

• PythonSet Operations

• Frozen Sets

Tuple

• What is Tuple

• Tuple Creation

• Accessing Elements in Tuple

• Changinga Tuple

• TupleDeletion

• Tuple Count

• Tuple Index

• TupleMembership

• TupleBuilt in Function (Length, Sort)

Control Flow

• Loops

• Loops and Control Statements (Continue, Break and Pass).

• Looping techniques in Python

• How to use Range function in Loop

• Programs for printing Patterns in Python

• How to use if and else with Loop

• Use of Switch Function in Loop

• Elegant way of Python Iteration

• Generator in Python

• How to use nested IF and Else in Python

• How to use nested Loop in Python

• Use If and Else in for and While Loop

• Examples of Looping with Break and Continue Statements

• How to use IN or NOTkeywordin Python Loop.

Python Exception Handling

• Python Errors and Built-in-Exceptions

• Exception handing Try, Except and Finally

• Catching Exceptions in Python

• Catching Specific Exception in Python

• Raising Exception

• Try and Finally

Python File Handling

• Opening a File

• Python File Modes

• Closing File

• Writing to a File

• Reading from a File

• Renaming and Deleting Files in Python

• Python Directory and File Management

• List Directories and Files

• Making New Directory

• Changing Directory

Python Function, Modules and Packages

• Python Syntax

• Function Call

• Return Statement

• Write an Empty Function in Python –pass statement.

• Lamda/ Anonymous Function

• *argsand **kwargs

• Help function in Python

• Scope and Life Time of Variable in Python Function

• Nested Loop in Python Function

• Recursive Function and Its Advantage and Disadvantage

• Organizing python codes using functions

• Organizing python projects into modules

• Importing own module as well as external modules

• Understanding Packages

• Programming using functions, modules & external packages

• Map, Filter and Reduce function with Lambda Function

• More example of Python Function

Python Object Oriented Programming—Oops

• Concept of Class, Object and Instances

• Constructor, Class attributes and Destructors

• Real time use of class in live projects

• Inheritance, Overlapping and Overloading operators

• Adding and retrieving dynamic attributes of classes

• Programming using Oops support

Python Database Interaction

• SQL Database connection using

• Creating and searching tables

• Reading and Storing configinformation on database

• Programming using database connections

Reading an excel

• Reading an excel file usingPython

• Writing toan excel sheet using Python

• Python| Reading an excel file

• Python | Writing an excel file

• Adjusting Rows and Column using Python

• ArithmeticOperation in Excel file.

• Plotting Pie Charts

• Plotting Area Charts

• Plotting Bar or Column Charts using Python.

• Plotting Doughnut Chartslusing Python.

• Consolidationof Excel File using Python

• Split of Excel File Using Python.

• Play with Workbook, Sheets and Cells in Excel using Python

• Creating and Removing Sheets

• Formatting the Excel File Data

• More example of Python Function

Working with PDF and MS Word using Python

• Extracting Text from PDFs

• Creating PDFs

• Copy Pages

• Split PDF

• Combining pages from many PDFs

• Rotating PDF’s Pages

Complete Understanding of OS Module of Python

• Check Dirs. (exist or not)

• How to split path and extension

• How to get user profile detail

• Get the path of Desktop, Documents, Downloads etc.

• Handle the File System Organization using OS

• How to get any files and folder’s details using OS

Pandas

• Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files

• How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function

• Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s

• Exploring the Data Plotting, Correlations, and Histograms

• Getting statistical information about the data Analysis Concepts, Handle the None Values

• Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc

• Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows

• Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function

• Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)

• Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data

• Exporting the results to Excel Joins:

• Python | Pandas Data Frame Inner Join

• Under sting the properties of Data Frame Left Join (Left Outer Join)

• Indexing and Selecting Data with Pandas Right Join (Right Outer Join)

• Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)

• Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data

• Pandas | How to Group Data How to apply Lambda / Function on Data Frame

• Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

NumPy

• Introduction to NumPy: Numerical Python

• Importing NumPy and Its Properties

• NumPy Arrays

• Creating an Array from a CSV

• Operations an Array from aCSV

• Operations with NumPy Arrays

• Two-Dimensional Array

• Selecting Elements from 1-D Array

• Selecting Elements from 2-D Array

• Logical Operation with Arrays

• Indexing NumPy elements using conditionals

• NumPy’sMean and Axis

• NumPy’sMode, Median and Sum Function

• NumPy’sSort Function and More

MatPlotLib

• Bar Chart using Python MatPlotLib

• Column Chart using Python MatPlotLib

• Pie Chart using Python MatPlotLib

• Area Chart using Python MatPlotLib

• Scatter Plot Chart using Python MatPlotLib

• Play with Charts Properties Using MatPlotLib

• Export the Chart as Image

• Understanding plt. subplots () notation

• Legend Alignment of Chart using MatPlotLib

• Create Charts as Image

• Other Useful Properties of Charts.

• Complete Understanding of Histograms

• Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Seaborn

• Introduction to Seaborn

• Making a scatter plot with lists

• Making a count plot with a list

• Using Pandas with seaborn

• Tidy vs Untidy data

• Making a count plot with a Dataframe

• Adding a third variable with hue

• Hue and scattera plots

• Hue and count plots

Visualizing Two Quantitative Variables

• Introduction to relational plots and subplots

• Creating subplots with col and row

• Customizing scatters plots

• Changing the size of scatter plot points

• Changing the style of scatter plot points

• Introduction to line plots

• Interpreting line plots

• Visualizing standard deviation with line plots

• Plotting subgroups in line plots

Visualizing a Categorical and a Quantitative Variable

• Current plots and bar plots

• Count plots

• Bar plot with percentages

• Customizing bar plots

• Box plots

• Create and interpret a box plot

• Omitting outliers

• Adjusting the whiskers

• Point plots

• Customizing points plots

• Point plot with subgroups

Customizing Seaborn Plots

• Changing plot style and colour

• Changing style and palette

• Changing the scale

• Using a custom palette

• Adding titles and labels: Part 1

• Face Grids vs. Axes Subplots

• Adding a title to a face Grid object

• Adding title and labels: Part 2

• Adding a title and axis labels

• Rotating x-tics labels

• Putting it all together

• Box plot with subgroups

• Bar plot with subgroups and subplots

Introduction to MySQL

What is the MySQLdb

How do I Install MySQLdb

Connecting to the MYSQL

Selecting a database

Adding data to a table

Executing multiple queries

Exporting and Importing data tables.

SQL Functions

• Single Row Functions

• Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date

Count, Average, Sum, Now etc.

Obtaining data from Multiple Tables

Types of Joins (Inner Join, Left Join, Right Join & Full Join)

Sub-Queries Vs. Joins

Distinct, Order by, Group by, Equal to etc.

Not Null

Unique

Primary Key

Foreign Key

SQL Basic

• SQL Introduction

• SQL Syntax

• SQL Select

• SQL Distinct

• SQL Where

• SQL And & Or

• SQL Order By

• SQL Insert

• SQL Update

• SQL Delete

SQL Advance

• SQL Like

• SQL Wildcards

• SQL In

• SQL Between

• SQL Alias

• SQL Joins

• SQL Inner Join

• SQL Left Join

• SQL Right Join

• SQL Full Join

• SQL Union

SQL Functions

• SQL Avg()

• SQL Count()

• SQL First()

• SQL Last()

• SQL Max()

• SQL Min()

• SQL Sum()

• SQL Group By

What is Analytics & Data Science

Common Terms in Analytics

What is data

Classification of data

Relevance in industry and need of the hour

Types of problems and business objectives in various industries

How leading companies are harnessing the power of analytics

Critical success drivers

Overview of analytics tools & their popularity

Analytics Methodology & problem-solving framework

List of steps in Analytics projects

Identify the most appropriate solution design for the given problem statement

Project plan for Analytics project & key milestones based on effort estimates

Build Resource plan for analytics project

Why Python for data science

Importing Data from various sources (Csv, txt, excel, access etc)

Database Input (Connecting to database)

Viewing Data objects - sub setting, methods

Exporting Data to various formats

Important python modules: Pandas

Cleansing Data with Python

Filling missing values using lambda function and concept of Skewness.

Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

Normalizing data

Feature Engineering

Feature Selection

Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

Label encoding/one hot encoding

Introduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)

Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Descriptive Statistics

Sample vs Population Statistics

Random variables

Probability distribution functions

Expected value

Normal distribution

Gaussian distribution

Z-score

Central limit theorem

Spread and Dispersion

Inferential Statistics-Sampling

Hypothesis testing

Z-stats vs T-stats

Type 1 & Type 2 error

Confidence Interval

ANOVA Test

Chi Square Test

T-test 1-Tail 2-Tail Test

Correlation and Co-variance

Concept of model in analytics and how it is used

Common terminology used in Analytics & Modelling process

Popular Modelling algorithms

Types of Business problems - Mapping of Techniques

Different Phases of Predictive Modelling

Need for structured exploratory data

EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

Identify missing data

Identify outliers’ data

Imbalanced Data Techniques

Data Preparation

Feature Engineering

Feature Scaling

Datasets

Dimensionality Reduction

Anomaly Detection

Parameter Estimation

Data and Knowledge

Selected Applications in Data Mining

Artificial Intelligence

• AI overview

• Meaning, scope, and 3 stages of AI

• Decoding AI

• Features of AI

• Applications of AI

• Image recognition

• Effect of AI on society

• AI for industries

• Overview of machine learning

• ML and AI relationship

Machine Learning

Techniques of Machine Learning

Machine Learning Algorithms

Algorithmic models of Learning

Applications of Machine Learning

Large Scale Machine Learning

Computational Learning theory

Reinforcement Learning

Supervised Learning

• What is Supervised Learning

• Algorithms in Supervised learning

• Regression & Classification

• Regression vs classification

• Computation of correlation coefficient and Analysis

• Multivariate Linear Regression Theory

• Coefficient of determination (R2) and Adjusted R2

• Model Misspecifications

• Economic meaning of a Regression Model

• Bivariate Analysis

• Naive Bayes classifier, Model Training

• ANOVA (Analysis of Variance)

Semi-supervised and Reinforcement Learning

Bias and variance Trade-off

Representation Learning

Regression and its Types

Logistic Regression

Linear Regression

Polynomial Regression

Meaning and Types of Classification

Nearest Neighbor Classifiers

K-nearest Neighbors

Probability and Bayes Theorem

Support Vector Machines

Naive Bayes

Decision Tree Classifier

Random Forest Classifier

About Clustering

Clustering Algorithms

K-means Clustering

Hierarchical Clustering

Distribution Clustering

Ensemble approach

K-fold cross validation

Grid search cross validation

Ada boost and XG Boost

What are the Limitations of Machine Learning

What is Deep Learning

Advantage of Deep Learning over Machine learning

Reasons to go for Deep Learning

Real-Life use cases of Deep Learning

What is Deep Learning Networks

Why Deep Learning Networks

How Deep Learning Works

Feature Extraction

Working of Deep Network

Training using Backpropagation

Variants of Gradient Descent

Types of Deep Networks

Feed forward neural networks (FNN)

Convolutional neural networks (CNN)

Recurrent Neural networks (RNN)

Generative Adversal Neural Networks (GAN)

Restrict Boltzman Machine (RBM)

Define Keras

How to compose Models in Keras

Sequential Composition

Functional Composition

Predefined Neural Network Layers

What is Batch Normalization

Saving and Loading a model with Keras

Customizing the Training Process

Intuitively building networks with Keras

Introduction to Convolutional Neural Networks

CNN Applications

Architecture of a Convolutional Neural Network

Convolution and Pooling layers in a CNN

Understanding and Visualizing CNN

Transfer Learning and Fine-tuning Convolutional Neural Networks

Intro to RNN Model

Application use cases of RNN

Modelling sequences

Training RNNs with Backpropagation

Long Short-Term Memory (LSTM)

Recursive Neural Tensor Network Theory

Recurrent Neural Network Model

Time Series Forecasting

NLP with python

Bags of words

Stemming

Tokenization

Lemmatization

TF-IDF

Sentiment Analysis

Overview of Tensor Flow

Tensor Flow code-basics

Graph Visualization

Constants, Placeholders, Variables

Tensor flow Basic Operations

Linear Regression with Tensor Flow

Logistic Regression with Tensor Flow

K Nearest Neighbor algorithm with Tensor Flow

K-Means classifier with Tensor Flow

Random Forest classifier with Tensor Flow

Quick recap of Neural Networks

Activation Functions, hidden layers, hidden units

Illustrate & Training a Perceptron

Important Parameters of Perceptron

Understand limitations of A Single Layer Perceptron

Illustrate Multi-Layer Perceptron

Back-propagation – Learning Algorithm

Understand Back-propagation – Using Neural Network Example

TensorBoard

What is Big Data

Big Data Customer Scenarios

Understanding BIG Data: Summary

Few Examples of BIG Data

Why BIG data is a BUZZ

How Hadoop Solves the Big Data Problem

What is Hadoop

Hadoop’s Key Characteristics

Hadoop Cluster and its Architecture

Hadoop: Different Cluster Modes

Why Spark is needed

What is Spark

How Spark differs from other frameworks

Spark at Yahoo!

What is BIG data Analytics

Why BIG Data Analytics is a ‘need’ now

BIG Data: The Solution

Implementing BIG Data Analytics – Different Approaches

The Traditional Approach: Business Requirement Drives Solution Design

The BIG Data Approach: Information Sources drive Creative Discovery

Traditional and BIG Data Approaches

BIG Data Complements Traditional Enterprise Data Warehouse

Traditional Analytics Platform v/s BIG Data Analytics Platform

Scala

• What is Scala

• Scala in other Frameworks

• Introduction to Scala REPL

• Basic Scala Operations

• Variable Types in Scala

• Control Structures in Scala

• Understanding the constructor overloading,

• Various abstract classes

• The hierarchy types in Scala,

• For-each loop, Functions and Procedures

• Collections in Scala- Array

Spark

• Overview to Spark

• Spark installation, Spark configuration,

• Spark Components & its Architecture

• Spark Deployment Modes

• Limitations of Map Reduce in Hadoop

• Working with RDDs in Spark

• Introduction to Spark Shell

• Deploying Spark without Hadoop

• Parallel Processing

• Spark MLLib - Modelling Big Data with Spark

What is Kafka Why Kafka

Configuring Kafka Cluster

Kafka architecture

Producing and consuming messages

Operations, Kafka monitoring tool

Need of Apache Flume

What is Apache Flume

Understanding the architecture of Flume

Basic Flume Architecture