MASTERS IN ARTIFICIAL INTELLIGENCE
WHICH COVERS EVERYTHING FROM START TO BUILDING APPLICATIONS AND CODING WITH EXPLANATION AND THEN PRACTICAL LAB, FROM START TO ADVANCE IN 4 PARTS OF COURSE, BY THE END OF THIS COURSE NOT ONLY WILL YOU FAMILIAR WITH AI BUT ALSO BE READY TO BUILD REAL-LIFE ADVANCED SYSTEMS AND ALL THE CONCEPTS THAT COMES WITH IT WITH LATEST METHODS AND STYLE OF PROGRAMMING TO ENHANCE YOUR DREAM OR CAREER INTO AI.
YOU PEOPLE FROM DR IN MIT TO 20 + YEARS OF EXPERIENCE, IT HAS BEEN WELL DESIGNED TO SUIT IN DEMAND, REAL LIFE, PRACTICAL SCENARIOS AND PROBLEMS AND APPLICATIONS PROBLEMS
When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.
These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.
PART 1 PYTHON BASICS TO ADVANCE
PART2 PYTHON AND TensorFlow BOOT CAMP
PART 3Reinforcement learning has recently become popular for doing all of that and more. Their practical use of concepts in building artificial intelligence systems
PART 4 Buiding recommender systems with machine learning
which covers the last top of masters of artificial intelligence
Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe firsthand the amazing results that are possible.
In 2016 we saw Google’s AlphaGo beat the world champion in Go.
We saw AIs playing video games like Doom and Super Mario.
Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.
If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.
It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true artificial general intelligence.
If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.
See you in class!
“If you can’t implement it, you don’t understand it”
- Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
- OUR courses are the ONLY courses where you will learn how to implement AI from scratch
- Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
SO GET READY, WHAT ARE YOU WAITING FOR, SIGN UP AND BE A TRUE MASTERS OF ARTIFICIAL INTELLIGENCE
- Lectures 68
- Quizzes 0
- Duration Lifetime access
- Skill level All levels
- Language English
- Students 8745
- Certificate Yes
- Assessments Yes
PART 1 MODULE 1
- python applications ,overview,python features, python examples
- final python example and interpreter and complier
- mini task and convience of python
- python 1st program , python installation,liclipse ,python shell and anaconda lab
- python operators full plus practical lab
- comparsion and logical operator full and practical lab
- bitwise,identity,membership operator full and practical lab
- datatype , immutable , numbers and string full and practical lab
PART 1 MODULE 2
- tuple and list full and practical lab
- dictionary explanation
- sets explanation
- dictionary and sets practical lab
- datatypes revision and control flow explanation
- CONTROL FLOW BASICS,if-if-if,if-elif,3problem explain
- control flow class A level strategy , nested loops and practical lab
- control flow more examples practical lab
- while loop explaination and practical lab full
- for loop full,practical lab , range full and practical lab
- nested loop full in detail , practical lab and 14 patterns problem
PART 1 MODULE 3
- functions best explaination, overwritten inside function, local global explaination nd practical lab
- functions arguments,required argument,keyword argument,default argument,variable length argument
- functions lambda and scope of variable explaination and practical lab
- modules and packages in detail explaination
- superset,subset,packages,modules,class,objects,self,oops start intro crip explanation
- modules 5 practical problems
- modules 6-10 practical problem plus encountering gliches
- modules 11-17 practical problem
PART 1 MODULE 4
- CLASS A LEVEL STARTEGY OOPS AND WHAT IS CLASS AND OBJECT
- CLASS A LEVEL STARTEGY CLASS-OBJECT FULL SECRETS DISCUSSION
- class-object practical lab and init self full
- caps introduction to oops
- single,hierarchy inheritance disscussion and practical lab
- multiple,multi-level,hybrid discussion and practical lab
- overidding,overloading discussion and practical lab
- polymorphism,abstraction discussion and practical lab
- installation of abstract classes and encapsulation full
- CAPS file handling introduction and open,close,read,write full 1
- file handling conclusion and 2 mini labs
- exception handling discussion and practical lab
PART 1 MODULE 5
- basics project 1 guess the number computer
- basics project 2 guess the number user
- project 3 tictactoe
- project 4 AI tictactoe
- intermediate project 1 python with sqlite and sql
- intermediate project 2 python with mongodb
- INTERMEDIATE project 3 motion detection
- ADVANCE 2 projects FACE RECOGNITION , ATTENDANCE PROJECT
PART 2 python and AI tenorflow bootcamp
PART 3 Practical AI Reinforcement Learning using Python
PART 4 Building AI and Recommender Systems with Machine Learning
- 1 Evaluating Recommender Systems
- 2 A Recommender Engine Framework
- 3 Content-Based Filtering
- 4 Neighborhood-Based Collaborative Filtering
- 5 Matrix Factorization Methods
- 6 Introduction to Deep Learning PART 1
- 7 Introduction to Deep Learning PART 2
- 8 Introduction to Deep Learning PART 3
- 9 Deep Learning for Recommender Systems
- 10 Scaling it Up
- 11 Real-World Challenges of Recommender Systems
- 12 Case Studies
- 13 Hybrid Approaches