MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (3h 28m) | Size: 1.3 GB
Learn Artificial intelligence (AI) and train computer, human intelligence processes through application of algorithms
What you’ll learn:
What id Artificial intelligence
What is Machine learning
What is Neural Network
Who to build program for predictive modelling
Requirements
Basics of Object Oriented Programming
Basics of Business Intelligence
Basics of Python
Description
Artificial intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Stated simply, AI is trying to make computers think and act like humans.
Achieving this end requires three key components:
Computational systems
Data and data management
Advanced AI algorithms (code)
The more humanlike the desired outcome, the more data and processing power required.
Why is artificial intelligence important?
Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. As an example, most humans can figure out how to not lose at tic-tac-toe (noughts and crosses), even though there are 255,168 unique moves, of which 46,080 end in a draw. Far fewer folks would be considered grand champions of checkers, with more than 500 x 1018, or 500 quintillion, different potential moves. Computers are extremely efficient at calculating these combinations and permutations to arrive at the best decision. AI (and its logical evolution of machine learning) and deep learning are the foundational future of business decision making.
Requirement of Artificial intelligence
Applications of AI can be seen in everyday scenarios such as financial services fraud detection, retail purchase predictions, and online customer support interactions. Here are just a few examples:
Fraud detection. The financial services industry uses artificial intelligence in two ways. Initial scoring of applications for credit uses AI to understand creditworthiness. More advanced AI engines are employed to monitor and detect fraudulent payment card transactions in real time.
Virtual customer assistance (VCA). Call centers use VCA to predict and respond to customer inquiries outside of human interaction. Voice recognition, coupled with simulated human dialog, is the first point of interaction in a customer service inquiry. Higher-level inquiries are redirected to a human.
When a person initiates dialog on a webpage via chat (chatbot), the person is often interacting with a computer running specialized AI. If the chatbot can’t interpret or address the question, a human intervenes to communicate directly with the person. These noninterpretive instances are fed into a machine-learning computation system to improve the AI application for future interactions.
Machine Learning
Machine Learning is naturally a subset of AI. It provides the statistical methods and algorithms and enables the machines/computers to learn automatically from their previous experiences and data and allows the program to change its behavior accordingly.
Who this course is for
Programmers
Students
Engineers
Learning lovers
Business Heads
Analytics Professionals
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