“Recommender Systems and Deep Learning in Python Course” Unbelievably, recommender systems are now used in almost all internet businesses in some capacity. Why are “recommender systems” helpful and what do I mean when I use the term? Let’s examine Google, YouTube, and Facebook, the top 3 websites on the Internet, as determined by Alexa. The very basis of these technologies is recommender systems. Google’s search outcomes They are the reason Google is currently the most prosperous technology business. a video interface on YouTube I’m confident I’m not the only one who has unintentionally wasted hours on YouTube when there were more pressing matters to attend to! How exactly do they persuade you to do that? That’s accurate. suggester programmes Facebook is so influential that national governments around the globe are concerned about its overbearing power! (Or perhaps they are concerned about losing their own influence. Currently, udemy is offering the course for up to 88 % off i.e. INR 399 (INR 3,499). (4.9 USD)
Who all can opt for this course?
- any person who runs or owns an online company
- students of data science, artificial intelligence, deep learning, and machine learning
- experts in data science, artificial intelligence, deep learning, and machine learning
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 399 ( |
| Duration | 12 Hours |
| Rating | 4.7/5 |
| Student Enrollment | 23,105 students |
| Instructor | Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc. |
| Topics Covered | N.A |
| Course Level | N.A |
| Total Student Reviews | 4,318 |
Learning Outcomes
- Utilizing straightforward and cutting-edge algorithms, comprehend and put accurate suggestions for your users into practise
- Spark factorization of large data sets using an AWS EC2 ensemble
- Pure Numpy matrix decomposition and SVD
- Keras matrix decomposition
- Keras’s autoencoder, deep neural networks, and residual networks
- Tensorflow’s Limited Boltzmann Machine
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Welcome (21 minutes) | Introduction |
| Outline of the course | ||
| Where to get the code | ||
| How to Succeed in this Course | ||
| 2. | Simple Recommendation Systems (01 hour 57 minutes) | Section Introduction and Outline |
| Perspective for this Section | ||
| Basic Intuitions | ||
| Associations | ||
| Hacker News – Will you be penalized for talking about the NSA? | ||
| Reddit – Should censorship based on politics be allowed? | ||
| Problems with Average Rating & Explore vs. Exploit (part 1) | ||
| Problems with Average Rating & Explore vs. Exploit (part 2) | ||
| Bayesian Ranking (Beginner Version) | ||
| Demographics and Supervised Learning | ||
| PageRank (part 1) | ||
| PageRank (part 2) | ||
| Evaluating a Ranking | ||
| Section Conclusion | ||
| Suggestion Box | ||
| 3. | Collaborative Filtering (01 hour 29 minutes) | Collaborative Filtering Section Introduction |
| User-User Collaborative Filtering | ||
| Collaborative Filtering Exercise Prep | ||
| Data Preprocessing | ||
| User-User Collaborative Filtering in Code | ||
| Item-Item Collaborative Filtering | ||
| Item-Item Collaborative Filtering in Code | ||
| Collaborative Filtering Section Conclusion | ||
| 4. | Beginner Q&A (14 minutes) | How do I Choose Which Model to Use? |
| How do I Solve the Cold-Start Problem? | ||
| What if I Don’t Like Math or Programming? | ||
| 5. | Matrix Factorization and Deep Learning (02 hours 06 minutes) | Matrix Factorization Section Introduction |
| Matrix Factorization – First Steps | ||
| Matrix Factorization – Training | ||
| Matrix Factorization – Expanding Our Model | ||
| Matrix Factorization – Regularization | ||
| Matrix Factorization – Exercise Prompt | ||
| Matrix Factorization in Code | ||
| Matrix Factorization in Code – Vectorized | ||
| SVD (Singular Value Decomposition) | ||
| Probabilistic Matrix Factorization | ||
| Bayesian Matrix Factorization | ||
| Matrix Factorization in Keras (Discussion) | ||
| Matrix Factorization in Keras (Code) | ||
| Deep Neural Network (Discussion) | ||
| Deep Neural Network (Code) | ||
| Residual Learning (Discussion) | ||
| Residual Learning (Code) | ||
| Autoencoders (AutoRec) Discussion | ||
| Autoencoders (AutoRec) Code | ||
| 6. | Restricted Boltzmann Machines (RBMs) for Collaborative Filtering (01 hour 36 minutes) | RBMs for Collaborative Filtering Section Introduction |
| Intro to RBMs | ||
| Motivation Behind RBMs | ||
| Intractability | ||
| Neural Network Equations | ||
| Training an RBM (part 1) | ||
| Training an RBM (part 2) | ||
| Training an RBM (part 3) – Free Energy | ||
| Categorical RBM for Recommender System Ratings | ||
| RBM Code pt 1 | ||
| RBM Code pt 2 | ||
| RBM Code pt 3 | ||
| Speeding up the RBM Code | ||
| 7. | Big Data Matrix Factorization with Spark Cluster on AWS / EC2 (47 minutes) | Big Data and Spark Section Introduction |
| Setting up Spark in your Local Environment | ||
| Matrix Factorization in Spark | ||
| Spark Submit | ||
| Setting up a Spark Cluster on AWS / EC2 | ||
| Making Predictions in the Real World | ||
| 8. | Basics Review (35 minutes) | (Review) Keras Discussion |
| (Review) Keras Neural Network in Code | ||
| (Review) Keras Functional API | ||
| (Review) How to easily convert Keras into Tensorflow 2.0 code | ||
| (Review) Confidence Intervals | ||
| (Review) Gaussian Conjugate Prior | ||
| 9. | Bayesian Ranking (Scary Version) (55 minutes) | Bayesian Approach part 0 (Preparation) |
| Bayesian Approach part 1 (Optional) | ||
| Optional: Bayesian Approach part 2 (Sampling and Ranking) | ||
| Optional: Bayesian Approach part 3 (Gaussian) | ||
| Optional: Bayesian Approach part 4 (Code) | ||
| Why don’t we just use a library? | ||
| 10. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |
| How to How to install Numpy, Theano, Tensorflow, etc… | ||
| 11. | Extra Help With Python Coding for Beginners (FAQ by Student Request) (42 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 | ||
| 12. | 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) | ||
| 13. | Appendix / FAQ Finale (08 minutes) | What is the Appendix? |
| BONUS |
Resources Required
- Just be familiar with some fundamental maths for the early sections
- For a deeper understanding of the advanced sections, be familiar with probability, linear algebra, and calculus
- Know Python and the Numpy stack well (see my free course)
- Know the fundamentals of utilising Keras for the deep learning segment
Pros of course
- Arnav Bhagat (5/5) : For machine learning beginners and experts this is a very good course.
- Elias Elfarri (3/5) : I like the time series course which basically is the ideal balance between theory and practice.
- Elahe Aghapour (3/5) : It is good for people to learn the basics of recommender systems but nothing beyond basics!
- Hua Xu (5/5) : This is a great great (not typo, I said great twice) course.
Cons of course
- Rajiv Popat (1/5) : Literally every slide in section 3 is just one boring formula after another written using complex notions to describe simple things which could have been described intuitively.
- Viktor Banai (1/5) : This lecture could have been a real gem but not with this teaching approach, it is awful.
- Indrajit (1/5) : Communication skill is very poor and not able to explain the mathematical notations and equations in layman’s term.
- Y N (1/5) : The epitome of teaching is to make difficult things simple but this instructor tends to make difficult things even more difficult.
Positive Review
Juan Pablo Arango Saldarriaga (5/5) : This is really a great course! The best I’ve taken on Udemy. It has the perfect balance between theory and practice. Lazy Programmer explains quite great and it’s a course where you learn how to program things by yourself, aside many other courses. I’m not done yet with the course and I simply love it! Definately checking other courses by Lazy Programmer 🙂
Negative Review
Vivek Singh (1/5) : The explanations in this course are not all proper.The Bayes Rule Explanation, the second video on Markov Model, the explanations were awful
About the Author
The instructor of this course is Lazy Programmer Inc. who is a Artificial intelligence and machine learning engineer. With 4.6 Instructor Rating and 148,419 Reviews on Udemy, he/she offers 33 Courses and has taught 527,254 Students so far.
- Although I have also been recognised as a data scientist, big data engineer, and full stack software engineer, I currently spend the majority of my time as an artificial intelligence and machine learning engineer with an emphasis on deep learning
- I earned my first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago
- My second master’s degree in statistics with a focus on financial engineering was awarded to me
- Data scientist and big data engineer with experience in online advertising and digital media (optimising click and conversion rates) (building data processing pipelines)
- I routinely use big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark
- I’ve developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation
- In my work with recommendation systems, I’ve used collaborative filtering and reinforcement learning, and we validated the findings using A/B testing
- I have instructed 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
- My web programming skills have helped numerous businesses
- I handle all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work
Comparison Table
| Parameters | Recommender Systems and Deep Learning in Python | Artificial Intelligence: Reinforcement Learning in Python | Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 12.5 hours | 14.5 hours | 15.5 hours |
| Rating | 4.7 /5 | 4.8 /5 | 4.7 /5 |
| Student Enrollments | 23,105 | 43,647 | 33,366 |
| Instructors | Lazy Programmer Inc. | Lazy Programmer Team | Lazy Programmer Inc. |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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