Name: AI Masters Program
Price: 50% INR
Author: IT GURU

Program Syllabus

Python Online Course

ITGuru provides the hands-on experience of python programming language by live experts through python online course. ...

Preview
Course Syllabus
- Chapter 01–Introduction to Python
  - What is Python?
  - Why Python?
  - History
  - Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source
  - Why is Python General Language?
  - Limitations of Python
  - What is PSF?
  - Python implementations
  - Python applications
  - Python versions
  - PYTHON IN REALTIME INDUSTRY
  - Difference between Python 2.x and 3.x
  - Difference between Python 3.7 and 3.8
  - Software Development Architectures
- Chapter 2 – Python Software’s
  - Python Distributions
  - Download &Python Installation Process in Windows, Unix, Linux and Mac
  - Online Python IDLE
  - Python Real-time IDEs like Spyder, Jupyter Notebook, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc
- Chapter 3 – Python Language Fundamentals
  - Python Implementation Alternatives/Flavors
  - Keywords
  - Identifiers
  - Constants / Literals
  - Data types
  - Python VS JAVA
  - Python Synta
- Chapter 4 – Different Modes of Python
  - Interactive Mode
  - Scripting Mode
  - Programming Elements
  - Structure of Python program
  - First Python Application
  - Comments on Python
  - Python file extensions
  - Setting Path in Windows
  - Edit and Run Python program without IDE
  - Edit and Run Python Program Using IDEs
  - INSIDE PYTHON
  - Programmers View of Interpreter
  - Inside INTERPRETER
  - What is Byte Code in PYTHON?
  - Python Debugger
- Chapter 5 – Python Variables
  - bytes Data Type
  - byte array
  - String Formatting in Python
  - Math, Random, Secrets Modules
  - Introduction
  - Initialization of variables
  - Local variables
  - Global variables
  - ‘global’ keyword
  - Input and Output operations
  - Data conversion functions – int (), float (), complex (), str (), chr (), Ord ()
- Chapter 6 – Operators
  - Arithmetic Operators
  - Comparison Operators
  - Python Assignment Operators
  - Logical Operators
  - Bitwise Operators
  - Shift operators
  - Membership Operators
  - Identity Operators
  - Ternary Operator
  - Operator precedence
  - Difference between “is” vs “==”
- Chapter 7 – Input & Output Operators
  - Print
  - Input
  - Command-line arguments
- Chapter 8 – Control Statements
  - Conditional control statements
  - If
  - If-else
  - If-Elif-else
  - Nested if
  - Loop control statements
  - for
  - while
  - Nested loops
  - Branching statements
  - Break
  - Continuing
  - Pass
  - Return
  - Case studies
- Chapter 9 – Data Structures or Collections
  - Introduction
  - Importance of Data structures
  - Applications for Data structures
  - Types of Collections
  - Sequence
  - Strings, List, Tuple, Range
  - Non sequence
  - Set, Frozen set, Dictionary
  - Strings
  - What is string
  - Representation of Strings
  - Processing elements using indexing
  - Processing elements using Iterators
  - Manipulation of String using Indexing and Slicing
  - String operators
  - Methods of String object
  - String Formatting
  - String functions
  - String Immutability
  - Case studies
- Chapter 10 – List Collection
  - What is List
  - Need for List collection
  - Different ways of creating List
  - List comprehension
  - List indices
  - Processing elements of List through Indexing and Slicing
  - List of object methods
  - List is Mutable
  - Mutable and Immutable elements of List
  - Nested Lists
  - List of lists
  - Hardcopy, shallow Copy and Deep Copy
  - zip () in Python
  - How to unzip?
  - Python Arrays:
  - Case studies
- Chapter 11 – Tuple Collection
  - What is tuple?
  - Different ways of creating Tuple
  - Method of Tuple object
  - Tuple is Immutable
  - Mutable and Immutable elements of Tuple
  - Process tuple through Indexing and Slicing
  - List v/s Tuple
  - Case studies
- Chapter 12 – Set Collection
  - What is set?
  - Different ways of creating set
  - Difference between list and set
  - Iteration Over Sets
  - Accessing elements of set
  - Python Set Methods
  - Python Set Operations
  - Union of sets
  - functions and methods of set
  - Python Frozen set
  - Difference between set and frozen set?
  - Case study
- Chapter 13 – Dictionary Collection
  - What is dictionary?
  - Difference between list, set and dictionary
  - How to create a dictionary?
  - PYTHON HASHING?
  - Accessing values of dictionary
  - Python Dictionary Methods
  - Copying dictionary
  - Updating Dictionary
  - Reading keys from Dictionary
  - Reading values from Dictionary
  - Reading items from Dictionary
  - Delete Keys from the dictionary
  - Sorting the Dictionary
  - Python Dictionary Functions and methods
  - Dictionary comprehension
- Chapter 14 – Functions
  - What is Function?
  - Advantages of functions
  - Syntax and Writing function
  - Calling or Invoking function
  - Classification of Functions
  - No arguments and No return values
  - With arguments and No return values
  - With arguments and with return values
  - No arguments and with return values
  - Recursion
  - Python argument type functions
  - Default argument functions
  - Required (Positional) argument’s function
  - Keyword argument’s function
  - Variable arguments functions
  - ‘pass’ keyword in functions
  - Lambda functions/Anonymous functions
  - map ()
  - filter ()
  - reduce ()
  - Nested functions
  - Nonlocal variables, global variables
  - Closures
  - Decorators
  - Generators
  - Iterators
  - Monkey patching
- Chapter 15 –Advanced Python Modules
  - Importance of modular programming
  - What is module
  - Types of Modules – Predefined, User defined.
  - User defined modules creation
  - Functions based modules
  - Class based modules
  - Connecting modules
  - Import module
  - From … import
  - Module alias / Renaming module
  - Built In properties of module
- Chapter 16 –Packages
  - Organizing python project into packages
  - Types of packages – predefined, user defined.
  - Package v/s Folder
  - Py file
  - Importing package
  - PIP
  - Introduction to PIP
  - Installing PIP
  - Installing Python packages
  - Un installing Python packages
- Chapter 17 –OOPs
  - Procedural v/s Object oriented programming
  - Principles of OOP – Encapsulation, Abstraction (Data Hiding)
  - Classes and Objects
  - How to define class in python
  - Types of variables – instance variables, class variables.
  - Types of methods – instance methods, class method, static method
  - Object initialization
  - ‘self’ reference variable
  - ‘Cls’ reference variable
  - Access modifiers – private (__), protected (_), public
  - AT property class
  - Property () object
  - Creating object properties using setaltr, getaltr functions
  - Encapsulation (Data Binding)
  - What is polymorphism?
  - Overriding
  - 1. i) Method overriding
    2. ii) Constructor overriding
    - Overloading
    1. i) Method Overloading
    2. ii) Constructor Overloading
    iii) Operator Overloading
  - 1. i) Method overriding
    2. ii) Constructor overriding
    - Overloading
    1. i) Method Overloading
    2. ii) Constructor Overloading
    iii) Operator Overloading
  - Class re-usability
  - Composition
  - Aggregation
  - Inheritance – single, multi-level, multiple, hierarchical and hybrid inheritance and Diamond inheritance
  - Constructors in inheritance
  - Object class
  - super ()
  - Runtime polymorphism
  - Method overriding
  - Method resolution order (MRO)
  - Method overriding in Multiple inheritance and Hybrid Inheritance
  - Duck typing
  - Concrete Methods in Abstract Base Classes
  - Difference between Abstraction & Encapsulation
  - Inner classes
  - Introduction
  - Writing inner class
  - Accessing class level members of inner class
  - Accessing object level members of inner class
  - Local inner classes
  - Complex inner classes
  - Case studies
- Chapter 18 –Exception Handling & Types of Errors
  - What is Exception?
  - Why exception handling?
  - Syntax error v/s Runtime error
  - Exception codes – Attribute Error, Value Error, Index Error, Type Error…
    - Handling exception – try except block
    - Try with multi except
    - Handling multiple exceptions with single except block
  - Finally block
    - Try-except-finally
    - Try with finally
    - Case study of finally block
  - Raise keyword
    - Custom exceptions / User defined exceptions
    - Need to Custom exceptions
  - Case studies
- Chapter 19–Regular expressions
  - Understanding regular expressions
  - String v/s Regular expression string
  - “re” module functions
  - Match()
  - Search()
  - Split()
  - Findall()
  - Compile()
  - Sub()
  - Subn()
  - Expressions using operators and symbols
  - Simple character matches
  - Special characters
  - Character classes
  - Mobile number extraction
  - Mail extraction
  - Different Mail ID patterns
  - Data extraction
  - Password extraction
  - URL extraction
  - Vehicle number extraction
  - Case study
- Chapter 20 –File & Directory handling
  - Introduction to files
  - Opening file
  - File modes
  - Reading data from file
  - Writing data into file
  - Appending data into file
  - Line count in File
  - CSV module
  - Creating CSV file
  - Reading from CSV file
  - Writing into CSV file
  - Object serialization – pickle module
  - XML parsing
  - JSON parsing
- Chapter 21–Python Logging
  - Logging Levels
  - implement Logging
  - Configure Log File in over writing Mode
  - Timestamp in the Log Messages
  - Python Program Exceptions to the Log File
  - Requirement of Our Own Customized Logger
  - Features of Customized Logger
- Chapter 22 –Date & Time module
  - How to use Date & Date Time class
  - How to use Time Delta object
  - Formatting Date and Time
  - Calendar module
  - Text calendar
  - HTML calendar
- Chapter 23 –OS module
  - Shell script commands
  - Various OS operations in Python
  - Python file system shell methods
  - Creating files and directories
  - Removing files and directories
  - Shutdown and Restart system
  - Renaming files and directories
  - Executing system commands
- Chapter 24 –Multi-threading & Multi Processing
  - Introduction
  - Multi tasking v/s Multi threading
  - Threading module
  - Creating thread – inheriting Thread class , Using callable object
  - Life cycle of thread
  - Single threaded application
  - Multi threaded application
  - Can we call run() directly?
  - Need to start() method
  - Sleep()
  - Join()
  - Synchronization – Lock class – acquire(), release() functions
  - Case studies
- Chapter 25 –Garbage collection
  - Introduction
  - Importance of Manual garbage collection
  - Self reference objects garbage collection
  - ‘gc’ module
  - Collect() method
  - Threshold function
  - Case studies
- Chapter 26–Python Data Base Communications(PDBC) (I)
  - Introduction to DBMS applications
  - File system v/s DBMS
  - Communicating with MySQL
  - Python – MySQL connector
  - connector module
  - connect() method
  - Oracle Database
  - Install cx_Oracle
  - Cursor Object methods
  - execute() method
  - executeMany() method
  - fetchone()
  - fetchmany()
  - fetchall()
  - Static queries v/s Dynamic queries
  - Transaction management
  - Case studies
- Chapter 27 –Python-Data Base Communication II
  - What is Database? Types of Databases
  - What is Database Management System
  - What is Relational DBMS?
  - What is Big Data? Types of data?
  - Oracle - SQLSERVER - MYSQL - DB2
  - Postgre SQL - DataBase Sample
  - Executing the Queries - Bind Variables
  - Installing of Oracle Python Modules
  - Executing DML Operations..!!
  - Connecting to the Database
  - Create a connection object.
  - Create a cursor object to read/write.
- Chapter 28 –Python – Network Programming
  - What is Sockets?
  - What is Socket Programming?
  - The socket Module
  - Server Socket Methods
  - Connecting to a server
  - A simple server-client program
  - Server
  - Client
- Chapter 29 –Tkinter & Turtle
  - Introduction to GUI programming
  - Tkinter module
  - Tk class
  - Components / Widgets
  - Label , Entry , Button , Combo, Radio
  - Types of Layouts
  - Handling events
  - Widgets properties
  - Case studies
- Chapter 30 –Data analytics modules
  - Numpy
  - Introduction
  - Scipy
  - Introduction
  - Arrays
  - Datatypes
  - Matrices
  - N dimension arrays
  - Indexing and Slicing
  - Pandas
  - Introduction
  - Data Frames
  - Merge , Join, Concat
  - MatPlotLib introduction
  - Drawing plots
  - Introduction to Machine learning
  - Types of Machine Learning?
  - Introduction to Data science
  - argsort()
  - lexsort()
  - argmax() and numpy.argmin()
  - nonzero()
  - where()
  - extract()
Tableau Online Training

ITGuru Tableau Online Training helps you to know the simplification of raw data in an easily understandable format, b...

Preview
Course Syllabus
- Introduction to Data Visualization and Power of Tableau
  - What is data visualization, Comparison and benefits against reading raw numbers, Real usage examples from various business domains
  - Some quick powerful examples using Tableau without going into the technical details of Tableau, installing Tableau, Tableau interface
  - Connecting to DataSource, Tableau Data Types, data preparation
- Architecture of Tableau
  - Installation of Tableau Desktop, Architecture of Tableau, Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane etc)
  - How to start with Tableau, Ways to share and exporting the work done in Tableau
- Working with Metadata & Data Blending
  - Connection to Excels, PDFs and Cubes, Managing Metadata and Extracts, Data Preparation and dealing with NULL values
  - Data Joins (Inner, Left, Right, Outer) and Union, Cross Database joining, Data Blending, data extraction, refresh extraction, incremental extraction
  - How to build extract
- Creation of sets
  - Marks, Highlighting, Sort and Group, Working with Sets (Creation of sets, Editing sets, IN/OUT, Sets in Hierarchies), constant sets, computed Sets, bins
- Working with Filters
  - Filters (Addition and Removal), Filtering continuous dates, dimensions, measures, Interactive Filters, marks card, hierarchies
  - How to create folders in Tableau, sorting in Tableau, types of sorting, filtering in Tableau, types of filters, filtering order of operations
- Organizing Data and Visual Analytics
  - Formatting Data (Labels, Annotations, Tooltips, Edit axes), Formatting Pane (Menu, Settings, Font, Alignment, Copy-Paste)
  - Trend and Reference Lines, Forecasting, k-means Cluster Analysis in Tableau, visual analytics in Tableau, reference lines and bands, confidence interval
- Working with Calculations & Expressions
  - Calculation Syntax and Functions in Tableau, Types of Calculations (Table, String, Logic, Date, Number, Aggregate)
  - LOD Expressions (concept and syntax), Aggregation and Replication with LOD Expressions
  - Nested LOD Expressions, Level of Details, Fixed Level of Details, Lower Level of Details, Higher Level of Details Quick Table Calculations
  - How to create Calculated Fields, predefined Calculations, how to validate
- Working with Parameters
  - Create Parameters, Parameters in Calculations, Using Parameters with Filters, Column Selection Parameters, Chart Selection Parameters
  - How to use Parameters in Filter Session
  - How to use parameters in Calculated Fields, how to use parameters in Reference Line
- Charts and Graphs
  - Dual Axes Graphs, Histogram (Single and Dual Axes), Box Plot, Pareto Chart, Motion Chart, Funnel Chart, Waterfall Chart, Tree Map, Heat Map, Market Basket analysis, Using Show me
  - Types of Charts, Text Table, Heat map, Highlighted Table, Pie Chart, Tree map, Bar chart, Line Chart, Bubble Chart, Bullet chart, Scatter Chart
  - Dual Axis Graphs, Funnel Charts, Pareto Chart, Maps, Hands on Lab, Assignment, Funnel Chart, Waterfall Chart, Maps
- Dashboards and Stories
  - Build and Format a Dashboard (Size, Views, Objects, Legends and Filters), Best Practices for Creative and Interactive Dashboards using Actions
  - Create Stories (Intro of Story Points, Creating and Updating Story Points, Adding Visuals in Stories, Annotations with Description)
  - DashBoards& Stories, what is Dashboard, Filter Actions, Highlight Actions, UrlActions , Selecting & Clearing values, DashBoardExamples
  - Best Practices in Creating DashBoards, Tableau WorkSpace, Tableau Interface, Tableau Joins
  - Types of Joins, Live vs Extract Connection, Tableau Field Types, Saving and Publishing Data Source, File Types
Data Science Course

The Data Science Online Training at IT Guru will provide you the best knowledge on Data Science basics, data analysis...

Preview
Course Syllabus
- Module 1: Introduction to DataScience
  - What is Data Science?
  - Why Python for data science?
  - Relevance in industry and need of the hour
  - How leading companies are harnessing the power of Data Science with Python?
  - Different phases of a typical Analytics/Data Science projects and role of python
  - Anaconda vs. Python
- Module 2: Python Essentials (Core)
  - Python Datatypes Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  - Functions
  - Exceptions
  - Decarators
  - Classes and Inheritance
  - Multithreading
  - Python with Databases (PostgresSQL, MySQL)
- Module 3: Accessing / Importing and Exporting Data using Python Modules
  - Importing Data from various sources (Csv, txt, excel, access etc)
  - Database Input (Connecting to database)
  - Viewing Data objects - subsetting, methods
  - Exporting Data to various formats
  - Important python modules: Pandas, beautifulsoup
- Module 4: Data Analysis and Visualization using Python
  - 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 and scipy.stats etc)
  - Libraries we focus under module 4
  - Numpy - Numerical library
  - a) ND array
  - b) Subset, slicing
  - c) Indexing
  - d) List vs ND array
  - e) Manipulating arrays
  - f) Mathematical operations and apply functions
  - g) Linear algebra operations
  - Scipy – Scientific Lirary
  - Pandas - Data Analysis library
  - a) Data loading
  - b) Series and Data frame
  - c) Selecting rows and columns
  - d) Position and label-based indexing
  - e) Slicing and dicing
  - f) Merging and concatenating
  - g) Grouping and summarizing
  - h) Data Processing, cleaning
  - i) Missing Values
  - j) Outliers
  - Matplotlib – Basic 2D Data Visualization library
  - a) Introduction to Matplotlib Basic plotting Figures and sub plotting
  - Box plot, Histograms, Scatter plots, image loading
  - b) Introduction to Seaborn
  - Histogram, rugged plot, hex plot and density plot
  - Joint plot, pair plot, count plot, Heat maps
  - c) Plotting categorical data and aggregation
  - Seaborn – Advanced Data Visualization library
  - Stat – Stastics library
- Module 5: Statistics & MathMatics
  - Types of data
  - Levels of measurement
  - Categorical variables. Visualization techniques for categorical variables
  - Numerical variables. Using a frequency distribution table
  - Histogram charts
  - Cross tables and scatter plots
  - Measures of central tendency
  - The main measures of central tendency: mean, median and mode
  - Measuring skewness
  - Measuring how data is spread out: calculating variance
  - Standard deviation and coefficient of variation
  - Calculating and understanding covariance
  - The correlation coefficient
  - Basic Statistics - Measures of Central Tendencies and Variance
  - Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  - Inferential Statistics -Sampling - Concept of Hypothesis Testing
  - Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
  - Important modules for statistical methods: Numpy, Scipy, Pandas
- Module 6: Machine Learning – Predictive Modelling – Basics
  - Introduction to Machine Learning & Predictive Modeling
  - Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
  - Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  - Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
  - Overfitting (Bias-Variance Trade off) & Performance Metrics
  - Feature engineering & dimension reduction
  - Concept of optimization & cost function
  - Concept of gradient descent algorithm
  - Concept of Cross validation(Bootstrapping, K-Fold validation etc)
  - Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
- Module 7: Machine Learning Algorithms & Applications – Implementation in Python
  - Linear & Logistic Regression
  - Segmentation - Cluster Analysis (K-Means)
  - Decision Trees (CART/CD 5.0)
  - Ensemble Learning (Random Forest, Bagging & boosting)
  - Artificial Neural Networks(ANN)
  - Support Vector Machines(SVM)
  - Other Techniques (KNN, Naïve Bayes, PCA)
  - Introduction to Text Mining using NLTK
  - Introduction to Time Series Forecasting (Decomposition & ARIMA)
  - Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
  - Fine tuning the models using Hyper parameters, grid search, piping etc.
- Machine Learning Case Studies
  - Market Basket Analysis
  - Dimensionality reduction on CTG
  - Email filtering – spam or not spamd
  - Product recommendations
  - Fraud detection
  - Breast cancer diagnostic detection
  - House price prediction analysis
  - Predicting wine quality
Machine Learning Online Course

The Machine Learning Online Training at IT Guru will provide you the best knowledge on Machine learning basics, algor...

Preview
Course Syllabus
Deep Learning Course

The Deep Learning Training at IT Guru will provide you the best knowledge on deep learning fundamentals, neural netwo...

Preview
Course Syllabus
TensorFlow Course

The TensorFlow Online Course at IT Guru will provide you the best knowledge on the variables, Tensors, ML models, etc...

Preview
Course Syllabus
Microsoft Excel Training

The Microsoft Excel Training at IT Guru will provide you the best knowledge on the MS Excel basics, advanced features...

Preview
Course Syllabus
- To excel using Advanced Excel
  - You will be introduced to the features of MS Excel 2016 and get to apply them in real time, in data analysis and visualisation
  - What’s different in MS Excel 2016,
  - Learn and get refreshed in the Advanced MS Excel 2016 concepts
  - Hands on projects (3)
- Tools & Functions Visited
  - The below is a sample of all the functions and formulae we will touch upon and is not an exhaustive list.
  - Customize Quick Access Toolbar
  - Conditional Formatting
  - Logical Functions
  - Text Functions
  - Lookup and Reference Functions
  - Formula Auditing
  - Name Ranges and application
  - Text to Column
  - Data Validation
  - What-If Analysis
  - Duplicate Removal
  - Data Sanitation through excel - Sorting, Filtering
  - Implementation of Pivot tables and charts
  - Application of advanced filters
  - Data analysis using normal charts, formatting of charts, secondary axis use, scatter plots
  - Data analysis using a Pivot table and pivot charts, sparklines
  - Regression, ANOVA, etc.
  - More Statistical Functions for example regression functions, standard deviation, median, mode, rank, weighted average etc.,
- Introduction to Macros
  - To be efficient with repetitive tasks, Excel allows us to automate such tasks using Macros. You will get to know all about Macros and how to use them.
  - Introduction to Macros
  - Record Macros
  - Run Macros
  - Edit Macros
- Data Security
  - In this session, you will learn how to restrict and protect data.
  - Protect Cells
  - Protect Sheet
  - Protect Workbook
  - Mark as Final
- Applying Excel
  - In this we get to use all or some of the Excel functions, we have learnt in this training. To solve business problems with data analysis, the use of dynamic visualization techniques makes sense of the data.
  - Creating a project management dashboard
  - Creating a Heat Map
  - Create a Pie Chart
SAS Training and Certification

The Clinical SAS Training at IT Guru will provide you the best knowledge on the various concepts of Clinical SAS, sta...

Preview
Course Syllabus

FAQ's

What is OnlineITGuru’s Artificial Intelligence Master’s Program and how is it different from other individual courses of ITGuru?
The online Artificial Intelligence Master’s program is an organized learning path designed by industry experts with good care. This program will help you become a professional Artificial Intelligence engineer. There are many individual courses we offer with other specializations. But the Artificial Intelligence master’s program is different from those. It includes different skills and specializations that can turn you into Master Artificial Intelligence. Moreover, this path will take you to the AI and automation space within the IT industry.
What are the prerequisites to enroll in the Artificial Intelligence Master’s Program?
There are no such pre-requirements to enroll in the Artificial Intelligence Master’s program here. A little knowledge of Machine Learning/AI will be an added benefit but it’s not a necessity. You may be a professional with IT industry experience or an Artificial Intelligence aspirant. This Artificial Intelligence Master’s Program is designed to assist people irrespective of professional background.
Why should I enroll in the Master’s Program in Artificial Intelligence?
Artificial Intelligence Master’s Program has been designed with overall research and suggestions made by the industry experts. This online learning will help you to master AI, Neural Networks, and Machine Learning, etc. concepts. Moreover, you will get hands-on experience with various tools and techniques in our AI master program. Moreover, our IT Gurus will guide you throughout the program with good assistance and support to finish it successfully.
What are the different modes of OnlineITGuru Artificial Intelligence Master’s Program Training?
ITGuru’s Artificial Intelligence Master’s Program is a combination of Self-paced and Instructor-led online training programs. Aspirants can choose at their own either expert guidance training or self-paced learning.
What is the duration to become an Artificial Intelligence certified professional?
The duration of the Artificial Intelligence master’s program recommended is 32/34 weeks. However, it depends on the aspirants learning to finish this program at their own pace.
If I enroll today, when shall I get the Artificial Intelligence Master’s course access?
As soon as you enroll in our AI Master’s program, you will get access to all the courses under it. Every learner can access these courses for a lifetime after enrolling in our AI Master’s Program.
What are the projects included in the AI Master’s Program?
Online IT Guru offers you the best, updated, and real-time projects as a part of this Artificial Intelligence master training program. The project work will help you to apply your skills learned in our online training very exclusively and with support. Further, this program includes different projects that test your knowledge and IQ level, theory & practical knowledge to make you industry-ready. Aspirants of AI will have exciting projects to work on in different domains. After completion of these, you will be an expert in the AI field.
Is there any specific order to complete the Artificial Intelligence Master’s program?
There is no such specific order to follow in this (AI Master’s) program for the learners. The preference is left to the learners only to select & finish their courses in any order they like.
Do OnlineITGuru provide job assistance after completing the Artificial Intelligence Masters course?
Yes, team OnlineITGuru will assist you to get a good placement after the AI Master’s course completion. Here, you will get job interview sessions, and updated Resume development assistance, etc. IT Guru has a good network with a placement assistance team across the globe (UK, USA, Australia, etc.) and will help you to get placed after you finish this program successfully.
Is it possible to switch from a self-paced program to instructor-led training in the AI Master’s program?
Yes, learners have the option to switch from self-paced learning to the instructor-led AI master’s program by paying some extra fees. Further, learners will also get a notification of batch details while enrolling or switching.
How to pay the training fee for the Online Artificial Intelligence Master’s Program?
OnlineITGuru training platform allows you to pay the training fee for the AI Master’s Program with a secure online payment gateway. You can check our portal to get offers like any discounts on the program/course fees which is updated on the website at regular intervals.

Self-Paced Learning

25810 29000

Start My Free Trial

+91 955 010 2466

Artificial Intelligence Masters Program

8

285

152

16

Program Features

As per your convenience

Never miss a class

Personal Learning Manager

Lifetime Access

Program Syllabus

Python Online Course

Course Syllabus

Tableau Online Training

Course Syllabus

Data Science Course

Course Syllabus

Machine Learning Online Course

Course Syllabus

Deep Learning Course

Course Syllabus

TensorFlow Course

Course Syllabus

Microsoft Excel Training

Course Syllabus

SAS Training and Certification

Course Syllabus

Program Fees

Don't find suitable time ?

FAQ's

Reviews

4.7/5

Our Masters Course Alumni work for amazing companies

Log In to start Learning

Self-Paced Learning

25810 29000

Start My Free Trial

+91 955 010 2466

Artificial Intelligence Masters Program

8

285

152

16

Program Features

As per your convenience

Never miss a class

Personal Learning Manager

Lifetime Access

Program Syllabus

Python Online Course

Course Syllabus

Tableau Online Training

Course Syllabus

Data Science Course

Course Syllabus

Machine Learning Online Course

Course Syllabus

Deep Learning Course

Course Syllabus

TensorFlow Course

Course Syllabus

Microsoft Excel Training

Course Syllabus

SAS Training and Certification

Course Syllabus

Program Fees

Don't find suitable time ?

FAQ's

Reviews

4.7/5

Our Masters Course Alumni work for amazing companies

Related Masters Programs

Data Scientist Masters Program

Cloud Architect Masters Program

Microsoft Azure Certification Masters Program

Request More