The ‘Advanced AI: Deep Reinforcement Learning with Python’ course will teach about the application of deep learning and neural networks to reinforcement learning. The course also teaches how to build various deep learning agents (including DQN and A3C).

In this course, you’ll learn how to use convolutional Neural Networks with Deep Q-Learning. The course is usually available for INR 1,299 on Udemy but students can click on the link and get the ‘Advanced AI: Deep Reinforcement Learning with Python’ for INR 449.

Who all can opt for this course?

  • Individuals with strong technical backgrounds who want to master cutting-edge AI methods, including professionals and students

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 1,299 (INR 1,299) 70% off
Duration10 Hours
Rating4.6/5
Student Enrollment36,725 students
InstructorLazy Programmer Team https://www.linkedin.com/in/lazyprogrammerteam
Topics CoveredArtificial Intelligence, Neural Networks, Q-learning, TD Lambda, Reinforcement learning
Course LevelIntermediate
Total Student Reviews4,967

Learning Outcomes

  • Create a range of deep learning agents (including DQN and A3C)
  • Any problem can be solved using a range of cutting-edge reinforcement learning methods
  • Deep neural network Q-learning
  • Using neural networks and policy gradient methods
  • Learning through reinforcement using RBF networks
  • Deep Q-Learning combined with convolutional neural networks is used

Course Content

S.No.Module (Duration)Topics
1.Introduction and Logistics (32 minutes)Introduction and Outline
Where to get the Code
How to Succeed in this Course
Tensorflow or Theano – Your Choice!
2.The Basics of Reinforcement Learning (01 hour 59 minutes)Reinforcement Learning Section Introduction
Elements of a Reinforcement Learning Problem
States, Actions, Rewards, Policies
Markov Decision Processes (MDPs)
The Return
Value Functions and the Bellman Equation
What does it mean to “learn”?
Solving the Bellman Equation with Reinforcement Learning (pt 1)
Solving the Bellman Equation with Reinforcement Learning (pt 2)
Epsilon-Greedy
Q-Learning
How to Learn Reinforcement Learning
Suggestion Box
3.OpenAI Gym and Basic Reinforcement Learning Techniques (57 minutes)OpenAI Gym Tutorial
Random Search
Saving a Video
CartPole with Bins (Theory)
CartPole with Bins (Code)
RBF Neural Networks
RBF Networks with Mountain Car (Code)
RBF Networks with CartPole (Theory)
RBF Networks with CartPole (Code)
Theano Warmup
Tensorflow Warmup
Plugging in a Neural Network
OpenAI Gym Section Summary
4.TD Lambda (19 minutes)N-Step Methods
N-Step in Code
TD Lambda
TD Lambda in Code
TD Lambda Summary
5.Policy Gradients (01 hour 02 minutes)Policy Gradient Methods
Policy Gradient in TensorFlow for CartPole
Policy Gradient in Theano for CartPole
Continuous Action Spaces
Mountain Car Continuous Specifics
Mountain Car Continuous Theano
Mountain Car Continuous Tensorflow
Mountain Car Continuous Tensorflow (v2)
Mountain Car Continuous Theano (v2)
Policy Gradient Section Summary
6.Deep Q-Learning (01 hour 32 minutes)Deep Q-Learning Intro
Deep Q-Learning Techniques
Deep Q-Learning in Tensorflow for CartPole
Deep Q-Learning in Theano for CartPole
Additional Implementation Details for Atari
Pseudocode and Replay Memory
Deep Q-Learning in Tensorflow for Breakout
Deep Q-Learning in Theano for Breakout
Partially Observable MDPs
Deep Q-Learning Section Summary
7.A3C (01 hour 02 minutes)A3C – Theory and Outline
A3C – Code pt 1 (Warmup)
A3C – Code pt 2
A3C – Code pt 3
A3C – Code pt 4
A3C – Section Summary
Course Summary
8.Theano and Tensorflow Basics Review (34 minutes)(Review) Theano Basics
(Review) Theano Neural Network in Code
(Review) Tensorflow Basics
(Review) Tensorflow Neural Network in Code
9.Setting Up Your Environment (FAQ by Student Request) (37 minutes)Anaconda Environment Setup
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
10.Extra Help With Python Coding for Beginners (FAQ by Student Request) (52 minutes)How to Code by Yourself (part 1)
How to Code by Yourself (part 2)
Proof that using Jupyter Notebook is the same as not using it
Python 2 vs Python 3
Is Theano Dead?
11.Effective Learning Strategies for Machine Learning (FAQ by Student Request) (59 minutes)How to Succeed in this Course (Long Version)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Machine Learning and AI Prerequisite Roadmap (pt 1)
Machine Learning and AI Prerequisite Roadmap (pt 2)
12.Appendix / FAQ Finale (08 minutes)What is the Appendix?
BONUS

Resources Required

  • Understand the fundamentals of TD Learning, MDPs, Dynamic Programming, and Monte Carlo
  • Math at the college level is useful
  • Knowledge of using Python and Numpy to develop machine learning models
  • Understand how to use Theano or Tensorflow to build ANNs and CNNs

Featured Review

Nikhil Reddy (5/5) : The course was excellent with a good quality of presentation in each section. All the topics are explained easily and effortlessly. I would recommend this course to any student who wants to learn deep reinforcement learning.

Pros

  • Bao Nguyen (5/5) : Great great !, it’ really good for me to widen my AI knowledge.
  • Prasad Iyer (5/5) : Excellent course! I learned a lot from rewriting all of the examples myself, and trying to code up each of the concepts in my own way.
  • Heitor Leal Farnese (5/5) : Great teacher! Very knowledgeable! And has a very good voice for teaching!
  • Abhilasha Sharma (5/5) : Excellent content, easy to digest with a good balance of lectures and coding exercises.

Cons

  • Con Land (1/5) : Avoid, his knowledge is subpar and the course is not good.
  • Mazdak Abbasi (1/5) : even though i knew about NN and RL and python, it was really hard to follow the codes due to poor explanation of the actual code.
  • Satrio M. (2/5) : Provider understand the material they teach, but don’t understand how to teach. provider is poor at delivering materials
  • Leandro A. (2/5) : Congratulations to the instructor for addressing such an important and interesting subject, but this course is poor in teaching.

About the Author

The instructor of this course is Lazy Programmer Team who is a Artificial Intelligence and Machine Learning Engineer. With 4.7 Instructor Rating and 51,803 Reviews on Udemy, he/she offers 17 Courses and has taught 188,739 Students so far.

  • Although Instructor have also been recognised as a data scientist, big data engineer, and full stack software engineer, Instructor currently spend the majority of time as an artificial intelligence and machine learning engineer with an emphasis on deep learning
  • Instructor earned first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago
  • Instructor’s second master’s degree in statistics with a focus on financial engineering was awarded to the Instructor
  • Data scientist and big data engineer with experience in online advertising and digital media (optimising click and conversion rates) (building data processing pipelines)
  • Instructor routinely use big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark
  • Instructor developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation
  • In the work with recommendation systems, Instructor used collaborative filtering and reinforcement learning, and they validated the findings using A/B testing
  • Instructor have taught students at universities like Columbia University, NYU, Hunter College, and The New School in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics
  • Instructor’s web programming skills have helped numerous businesses
  • Instructor handle all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work

Comparison Table

ParametersAdvanced AI: Deep Reinforcement Learning in PythonDeep Learning: Convolutional Neural Networks in PythonNatural Language Processing with Deep Learning in Python
OffersINR 449 (INR 1,299) 70% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration10.5 hours13.5 hours12 hours
Rating4.6 /54.6 /54.5 /5
Student Enrollments36,72434,67343,664
InstructorsLazy Programmer TeamLazy Programmer Inc.Lazy Programmer Inc.
Register HereApply Now!Apply Now!Apply Now!

Leave feedback about this

  • Rating