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Artificial Intelligence Masters Program

Artificial Intelligence Master’s Program by OnlineITGuru will provide you the best curriculum to become a certified Artificial Intelligence professional. An Artificial Intelligence master’s program is designed by the industry experts in this field. This program includes various aspects of AI, ML, ANN (Artificial Neural Networks), and so on. Moreover, you will get hands-on experience with project work offered by our platform. All this learning will help you to stand in the market with excellent skills and knowledge in AI. So, hurry up to grab the online Artificial Intelligence Masters Program and make yourself more intelligent.

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Program Features

  • As per your convenience

    Weekday or weekend; morning or evening. Multiple options for everyone.

  • Never miss a class

    You can always switch to another batch, depending upon your availability.

  • Personal Learning Manager

    A human, who is Ridiculously Committed to answer all your queries.

  • Lifetime Access

    You'll have the keys to all our presentations, quizzes, installation guides. All for a lifetime!

Program Syllabus

  • Python Online Course

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


    Course Syllabus

    • Chapter 1 – Getting started with Python Programming
      • Introduction to Python  Python features
      • Scope of Python
      • Python products
      • Python Download, Installation and Environment Setup
      • First python program execution "Hello World" 
    • Chapter 2 – Variables, keywords and Operators
      • Variables and Rules of writing Variables
      • Keywords in Python
      • Operators
      • Arithmetic operators
      • Logical operators
      • Membership operators
      • Basics I/O and Type casting
    • Chapter 3 – String data type in Python
      • Introduction to Python 'string' data type  Properties of a string
      • String built-in functions
      • Programming with strings
      • String formatting
    • Chapter 4 – Control flow statements
      • Flow of program control type
      • Decision making statements: if-elif-else
      • ‘for’ loop
      • Repetition using for loop: range() function
      • ‘while’ loop
      • Infinite loop
      • Loop control keywords: break, continue, pass 
    • Chapter 5 – Lists and Tuples data type in Python
      • Introduction to Python 'list' data type
      • Properties of a list
      • List built-in functions
      • Programming with lists
      • Introduction to Python 'tuple' data type
      • Tuples as Read only lists 
    • Chapter 6 – Dictionary data type in Python
      • Introduction to Python 'dictionary' data type
      • Creating a new dictionary
      • Dictionary built-in functions
      • Properties of Dictionary 
    • Chapter 7 – Set data type in Python
      • Introduction to Python 'set' data type
      • Set and set properties
      • Set built-in functions 
    • Chapter 8 – Functions in Python
      • Introduction to functions
      • Function definition and return
      • Function call and reuse
      • Function parameters
      • Function recipe and docstring
      • *args and **kwargs
      • Modules and Packages 
    • Chapter 9 – Working with files
      • Working with text files
      • File objects and different Modes of file
      • Reading, writing and use of 'with' keyword
      • read(), readline(), readlines(), seek(), tell() methods
      • Working with CSV files
      • Use of CSV module in Python
      • Reading and writing CSV files 
    • Chapter 10 – Email sending Automation
      • Understanding SMTP
      • Sending email with sendmail() function
      • Sending email using Gmail
      • Email sending with attachment and MIME 
    • Chapter 11 – Exception Handling in Python
      • Understanding exceptions
      • try, except, else and finally
      • raising exceptions with: raise
      • Creating your own exception classes 
    • Chapter 12 – Object oriented programming with Python
      • OOPs concepts: Classes and objects
      • Making of a class and module namespace
      • Static and instance variables
      • Deep understanding of self and init ()
      • Inheritance and Overriding
      • Overloading functions
      • Operator overloading
      • Encapsulation: Hiding attributes 
    • Chapter 13 – Regular Expressions in Python
      • Pattern matching
      • Meta characters for making patterns
      • re flags 
    • Chapter 14 – Database connectivity with Python
      • Working with MySQL database
      • Working with Sqlite3 database 
  • Tableau Online Training

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


    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...


    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 Course

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


    Course Syllabus

  • Deep Learning Course

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


    Course Syllabus

  • TensorFlow Training

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


    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...


    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

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    Course Syllabus

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