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.
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
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.
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?
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?
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?
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.
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?
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.
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?
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.