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Kigyan School of Data Science

Professional Certificate Course in
Artificial Intelligence


  • * NO TECHNICAL KNOWLEDGE REQUIRED
  • * LEARN FROM EXPERTS
  • * END TO END PROJECT EXECUTION
  • * 100% UNLIMITED JOB ASSISTANCE
  • * RESUME BUILDING
  • * INTERVIEW PREPARATION
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Admission open now

Admission open now

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Categories:
Combo Course
Mode:
Online / Classroom

Overview
Learning Objectives
FAQ
Overview

Kigyan introduces students to integrated blended learning, making them experts in Artificial Intelligence and Data Science. The program in consideration with current industry requirement for Artificial Intelligence and Data Science job roles.

Upon completion of this Certificate Program, you will receive the certificates from Kigyan in the Artificial Intelligence courses on the learning path. These certificates will testify to your skills as an expert in Artificial Intelligence.

This program is designed for the students or professionals who has a minimal or no knowledge of computer programming and want to build their career in the field of Artificial intelligence.

The program starts from introducing the Business analytics and refreshing your knowledge on mathematical and statistics concepts needed for Analytics and then data management using Microsoft Excel and SQL programming and also covering the basics of Database and data warehousing concepts, introduction to Big data management and basics of python programming.

This program will make you industry-ready with the business domain master data and business process training and to work on 20+ projects

Learning Objectives

The Certification program in Artificial Intelligence course will furnish you with in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning and natural language processing using Python and Deep learning with TensorFlow and enable you to build artificial intelligence solutions.

What Skills you will Learn

    This training program will enable you to:

  • Understand Business Analytics, Data science, Machine learning, deep learning and Artificial intelligence
  • Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing.
  • Install the required Python environment and other auxiliary tools and libraries
  • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
  • Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
  • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
  • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
  • Gain expertise in machine learning using the Scikit-Learn package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
  • Use the Scikit-Learn package for natural language processing
  • Use the matplotlib library of Python for data visualization
  • Extract useful data from websites by performing web scrapping using Python
  • Building machine learning models with 20+ industry demand algorithms
  • Deep Learning with TensorFlow
  • Creating Watch points and insight detection
  • Building end to end reinforced learning model automation and model refresh
  • Building an artificial intelligence engine to insight detection and recommendation
Frequently asked Questions
1. Who should attend this course?

There is a booming demand for skilled data scientists and artificial intelligence developers across all industries that make this course suited for participants at all levels of experience. We recommend this program particularly for the following professionals:

  • Analytics professionals who want to become an AI Expert
  • Software professionals looking to get into the field of AI
  • IT professionals interested in pursuing a career in analytics
  • Graduates looking to build a career in analytics, data science and AI
  • Experienced professionals who would like to harness data science in their fields
  • Anyone with a genuine interest in the field of Artificial Intelligence

Prerequisites: There are no prerequisites for this program. But having knowledge on any one programming language or databases will be an added advantage

The Python basics course included with this program provides additional coding guidance.

2. What type of Jobs we can expect after this training?

Upon completion of this Program, you will have the skills required to help you land your dream job, including:

  • IT Professionals
  • Data Scientists
  • Data Engineers
  • Data Analysts
  • Artificial Intelligence Engineer
  • Machine Learning Engineer
  • Project Managers
  • Program Managers
3. What are the Pre-requisites for this Training?

There are no prerequisites for learning this course. However, knowledge of any one programming language and SQL will be beneficial

4. Who will provide the certification?

Upon successful completion of the Artificial intelligence certification training, you will be awarded the course completion certificate from Kigyan

5. What are my system requirements?

The tools you will need to attend training are:

  • Windows: Windows XP SP3 or higher
  • Mac: OSX 10.6 or higher
  • Intel i3 with minimum 8 GB RAM
  • Internet speed: Preferably 512 Kbps or higher for online training
  • Headset, speakers, and microphone: you will need headphones or speakers to hear instructions clearly, as well as a microphone to talk to others. You can use a headset with a built-in microphone, or separate speakers and microphone
6. What are the training modes offered for this course?

We offer this training in the following modes:

  • Live Classroom training in our Training Centre
  • Live Virtual Classroom or Online Classroom: Attend the course remotely from your desktop via video conferencing to increase productivity and reduce the time spent away from work or home
7. Any Group Discount offered in this classroom training?

Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the website or send am mail with your requirement and the student count to shailaja@kigyan.com. Our customer service representatives can provide more details.

8. What payment options are available?

Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.

  • Any Credit or Debit Card
  • Bank Transfer using NEFT / RTGS
  • Direct payment in our centre through cash / cheque
  • Online payment wallet like Google Pay, PayTM….
Curriculam
Introduction to Artificial Intellegence
Introduction to Business Analytics
  • Types of Business Analytics
    • Descriptive
    • Diagnostic
    • Predictive
    • Prescriptive
  • Areas of Analytics
  • Business Decision Making
  • Business Intelligence (BI)
  • Data Science
  • Big data & Analytics
  • Machine Learning
  • Deep Learning with Artificial Intellegence
BigData Hadoop Introduction
  • Introduction to Big data Analytics
  • Introduction to Hadoop
  • Understanding Hadoop Core components
Python Program Fundamentals
  • introduction to Python
  •   Features of Python
  •   History of Python
  •   Installation of Python
    • Anaconda
    • Jupyter
    • Yhat
  • Modes of Python
    • Interpretation Mode
    • Batch Script mode
  • Indentation in Python
  • Writing Comments in Python Programs
  • Types of Variables & Datatypes
    • String, Numeric & Boolean
    • Tuple
    • List
    • Directory
    • Set
  • Basic Operators
    • In, +,*
  • Functions
    • Built-in Sequence Functions
  • Control Flow in Python
    • If, Else, Elif
    • For Loops
    • While loops
    • Exception handling

DataScience With Python
Mathematical Computing with NumPy
  • NumPy Overview
  • Basics of NumPy
  • NumPy fundamental objects
  • Create and Print a NumPy array
  • Basic operators in NumPy
  • Shape manipulation & copy methods
  • Linear algebraic function
  • Writing Programs using NumPy
Scientific Computation with SciPy
  • Introduction to SciPy
  • Characteristics of SciPy
  • Sub Packages of SciPy
    • Optimization
    • Integration
    • Linear Algebra
    • Statistics
    • Weave
    • IO
Data Manipulation with Pandas
  • Introduction to Pandas
  • Data Structures of Pandas
  • Creating Series and Data Frames
  • Accessing elements form data structures
  • Vectorised Operations
  • Handling Missing Values
  • Analysing Data
  • Pandas SQL operations
Machine Learning with Sciket Learn
  • Introduction to Machine Learning
  • The machine Learning approach
  • Introduction to Sciket learn
  • Technologies for understanding a dataset
  • Supervised learning models
  • Unsupervised Learning models
  • Algorithms
    • Regression
    • Classification
    • Clustering
    • Directionality reduction
Natural Language Processing (NLP) with Sciket Learn
  • Introduction to NLP
  • How NLP is helpful
  • Modules to load contents and category
  • Feature extraction techniques
  • Approaches of NLP
Data Visulization with MATPLOTLIB
  • Introduction to Data Visualization & its inference
  • Why Python
  • Python libraries
  • MATPLOTLIB
  • Steps for Plotting
    • Line plot
    • 2D plot
    • Multiple plots
    • Sub plots
  • Types of Plots
  • Seaborn
Web scrapping with Beautiful Soup
  • Introduction to Web scrapping
  • Web Scrapping Process
  • Introduction to Beautiful Soup
    • The Parsers
    • Objects
    • Tree
  • Various Operations on Tree
    • Searching
    • Modifying
    • Navigating
  • Parsing a Part of Document
  • Output- Formatting and Printing
  • Encoding
Python Integration with Hadoop
  • Hadoop Steaming – Python API
  • Mapper in Python
  • Reducer in Python
  • PySpark – Python API for Spark

Machine Learning
Introduction to Data Science
  • What is Data Science?
  • What does Data Science involve?
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Python
Data Extraction, Wrangling & Visualization
  • Data Analysis Pipeline
  • Types of Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
Introduction to Machine Learning with Python
  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Categories
  • Linear regression
  • Introduction to Neural Network and Deep Learning
Supervised Learning
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Confusion Matrix
  • What is Random Forest?
Unsupervised Learning
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How K-means algorithm works?
Reinforcement Learning
  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
Time Series Analysis
  • What is Time Series Analysis?
  • ARMA model
  • ARIMA model
Model Selection and Boosting
  • What is Model Selection?
  • What is Boosting?
  • How Boosting Algorithms work?

Artificial Intelligence & Deep Learning Using TensorFlow
Introduction to Artificial Intelligence & Deep Learning
  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • Real-Life use cases of Deep Learning
Introduction to TensorFlow
  • What is TensorFlow?
  • TensorFlow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
Convolutional Neural Networks (CNN)
  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
Recurrent Neural Networks (RNN)
  • Introduction to RNN Model
  • Application use cases of RNN
  • Training RNNs with Backpropagation
  • Long Short-Term memory (LSTM)
  • Recurrent Neural Network Model
Restricted Boltzmann Machine
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
Autoencoders
  • Introduction to Autoencoders
  • Autoencoders Applications
  • Understanding Autoencoders

Advanced Learning
  • Introduction to API AI Architecture
  • Automating Model Building and Refresh
  • Create Watch Points and Insight

Project
  • Building End to End AI Engine to insight detection and recommendation