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IT & Software IT Certification Artificial Intelligence

Artificial Intelligence Introduction

Introduction to AI, ML, Data Science , BI and Analytics for Non-Technicals, Leaders, Managers, freshers and Beginners
Rating: 4.4 out of 54.4 (97 ratings)
303 students
Created by Sudhanshu Saxena
Last updated 7/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • What is Data
  • Types of Data
  • What is Artificial Intelligence?
  • Application of AI
  • What is Machine Learning
  • Applications of Machine learning
  • What is Bigdata
  • Properties of Big data
  • What is Analytics
  • What is Business Intelligence
  • What is Data Science
  • Data Science Applications

Requirements

  • This course starts from very scratch, it does not need any specific prerequisites.

Description

Section 1-L1:

To learn the strategy of various skills of current and future world like Artificial Intelligence, Machine learning, Data Science, we are starting from understanding data. To expertise in Artificial Intelligence needs to be understood the basics of data. In this INTRODUCTION section, we will talk about

What is the data?

How does data divide into multiple parts?

Types of data!

How do and where the data generate from?

What kind of data available globally?

How we can deal with the data?

Apart from that, we will discuss the Characteristics of the structured data.

Sources of the Structured data.


Section 1-L2:

The questions you should seek are, How Machine Learning, Artificial Intelligence can handle this.

As we understood the Data, its type, and the structured data, here we will talk about the Unstructured Data.

This second lecture will be covering Types of Unstructured data.

Advantages and disadvantages of unstructured data.

Problem faced in storing unstructured Data


Section 1-L3:

Out of all available data, the most crucial data is Semi-structured Data which allows the user to have a flexible Schema. In the previous lecture, we talked about what kinds of data can be deal with, the type of data, its advantages--disadvantages of unstructured data.

Here we will be learning about the most useful type of data called -Semi-Structured Data.

Characteristics of Semi-structured Data

Source of Semi-structured Data.

Advantages and disadvantages of Semi-structured data.


Section 2-L4:

In the previous section, we understood the Data, its type, Advantages-Disadvantages of different kinds of data and where data comes from.

So here, in this section, we will cover: -

What is Big Data? Why even we care about it?

What can be done with this Bigdata?

The Hype around Big Data?


Section 2-L5:

This lecture is intended to cover the term Bigdata-Why any data called Bigdata?

How to identify if my data is Bigdata?

What are the properties of the Bigdata?

Do I see the similar properties in my Data also?

What are the characteristics of Bigdata?

How do you store the Bigdata?

Where to store Bigdata?

What tools and tricks are used to handle Bigdata?

Four dimensions of Bigdata?


Section 2-L6:

if we understand the data its size and types of data, we should know who is creating this data. This section is covering all aspects of Bigdata including:

How much bigdata I am (an individual) accumulating?

How tough it is for us to deal with this kind of data?

What are the challenges in handling this kind of Bigdata?


Section 2-L7:

we will also cover the Model of BigData generation?

This lecture describes how this Big-data gets generated

Why this data is huge now?

What do organizations want?

Which all companies are working on Bigdata?

Get answers to all these questions in this video.


Section 3-L8:

This new section is intended to describe what is Analytics and why it came into existence and Understanding Analytics from scratch. To understand that we will seek the answers to all possible questions like

What are the four major questions we want to answer?

What kind of analytics are possible?

What is the difference among all this Analytics-Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics?


Section 4-L9

As data is the basic foundation of AI, with its existence, types analysis and analytics Business intelligence plays an important role in using huge sized data. To understand BI we will cover

How to answer the first basic question of analytics?

How to get started with analytics?

What all organizations can achieve with Business Intelligence?

What are the Functions of Business Intelligence?

How to implement a Business Intelligence system in an organization?

What kind of data Business Intelligence require?


Section 5- L10

Here you are ready to learn why and what Artificial Intelligence is?

we will start from

Understanding Artificial Intelligence from scratch covering all questions like

What are the different definitions of Artificial Intelligence?

Why it is needed to create Artificial Intelligence?

What are the types of AI?

How to see through Artificial Intelligence?

Applications of Artificial Intelligence.


Section 6-L11:

Machine learning is a part of AI and Data Science. It is necessary to understand ML if you are dealing with AI. here we will be Understanding Machine Learning from scratch.

Different definitions of Machine Learning.

Types of Machine Learning

How each type of ML is different from another and where are they going to use?

How does the computer understand the data?


Section 6-L12

Where we can use and see ML applications in our Daily Life

How to use Machine Learning in real-time applications.?

ML use case in similar Pins

ML use case in face recognition.

ML use case in people you may know

ML use case in spam Email filtering

ML use case in Product recommendations

ML use case in online fraud detections

ML use case in Disease identifications

ML use case in Personalised treatment

ML use case in clinical trial research

ML use case in character recognitions


Section 7: L13

This section is all about AI from very scratch, we discuss all sections of AI, ML and now we talk about Data Science, How does data science relate to Artificial Intelligence? To answer this question, we will discuss

What is Data Science?

The definition of Data Science?

How does Data Science connect with analysis?

Components of Data Science.

Data Science Lifecycle.

Use of Mathematics in Data Science?

Use of Machine Learning in Data Science.

Difference between Business Intelligence and Data Science.


Who this course is for:

  • This is the best course for Beginner from any Stream, any Domain, any age group; professional with any experience or for freshers who wants to learn Artificial intelligence and data science concepts.
  • This course it designed for Graduate,Technical -Nontechnical, BFSI, Programmers, Non-Programmers
  • any Professionals like: • Developer • Data Analyst • Manager • Techno Professionals • Techno Manager • Techno-functional Manager • VPs/CEOs/CTOs

Course content

8 sections • 14 lectures • 3h 18m total length

  • Preview06:24

  • Preview10:44
  • Preview08:08
  • Understanding Semistructured data
    08:46

  • Understanding Big Data from Scratch
    09:43
  • Properties of Big Data
    15:02
  • Understanding Big data Volume
    08:23
  • How this Big data gets generated
    07:22

  • What is Analytics
    24:02

  • What is Business Intelligence
    16:14

  • Understanding Artificial Intelligence
    23:03

  • Understanding Machine Learning from Scratch
    29:07
  • Machine Learning Applications
    16:58

  • Understanding Data Science
    14:50

Instructor

Sudhanshu Saxena
Data Scientist, Machine Learning & Big Data Consultant
Sudhanshu Saxena
  • 4.4 Instructor Rating
  • 133 Reviews
  • 6,556 Students
  • 3 Courses

Data Scientist | Machine learning Expert | Big Data Evangelist | Statistics | Predictive Analysis| Artificial Intelligence and Big Data Training. Data Science and consultant with more then 12+ years of experience.

3000+ hours of classroom and online training. Visiting Faculty for Big Data Hadoop for many institutes pan India level

Big Data and Artificial Intelligence speaker.

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