The‘Learn Linear Regression in Python: Deep Learning Basics’ course teaches you to derive and solve a linear regression model, and apply it appropriately to data science problems. This course also helps you to program your own version of a linear regression model in Python.
This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression in Python. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘Learn Linear Regression in Python: Deep Learning Basics’ for INR 449.
Who all can opt for this course?
- Individuals with an interest in artificial intelligence, statistics, machine learning, and data science
- Newcomers to data science who want a simple introduction to the subject
- People who want to advance their careers by entering data science, one of the hottest fields in technology
- Self-taught programmers who wish to increase their theoretical knowledge of computer science
- Analytics experts those are interested in understanding the theory behind one of the most popular algorithms in statistics
|Registration Link||Apply Now!|
|Price||INR 449 (|
|Student Enrollment||31,452 students|
|Instructor||Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc.|
|Topics Covered||1-D Linear,Multiple Linear, and Polynomial Regression in Python,Machine Learning|
|Total Student Reviews||5,806|
- Create a linear regression model, solve it, and use it to tackle data science challenges as needed
- Python allows you to create your own customised linear regression model
|1.||Welcome (26 minutes)||Introduction and Outline|
|How to Succeed in this Course|
|Statistics vs. Machine Learning|
|2.||1-D Linear Regression: Theory and Code (01 hour 11 minutes)||What is machine learning? How does linear regression play a role?|
|What can linear regression be used for?|
|Define the model in 1-D, derive the solution (Updated Version)|
|Define the model in 1-D, derive the solution|
|Coding the 1-D solution in Python|
|Exercise: Theory vs. Code|
|Determine how good the model is – r-squared|
|R-squared in code|
|Introduction to Moore’s Law Problem|
|Demonstrating Moore’s Law in Code|
|Moore’s Law Derivation|
|R-squared Quiz 1|
|3.||Multiple linear regression and polynomial regression (51 minutes)||Define the multi-dimensional problem and derive the solution (Updated Version)|
|Define the multi-dimensional problem and derive the solution|
|How to solve multiple linear regression using only matrices|
|Coding the multi-dimensional solution in Python|
|Polynomial regression – extending linear regression (with Python code)|
|Predicting Systolic Blood Pressure from Age and Weight|
|R-squared Quiz 2|
|4.||Practical machine learning issues (01 hour 14 minutes)||What do all these letters mean?|
|Interpreting the Weights|
|Generalization error, train and test sets|
|Generalization and Overfitting Demonstration in Code|
|One-Hot Encoding Quiz|
|Probabilistic Interpretation of Squared Error|
|L2 Regularization – Theory|
|L2 Regularization – Code|
|The Dummy Variable Trap|
|Gradient Descent Tutorial|
|Gradient Descent for Linear Regression|
|Bypass the Dummy Variable Trap with Gradient Descent|
|L1 Regularization – Theory|
|L1 Regularization – Code|
|L1 vs L2 Regularization|
|Why Divide by Square Root of D?|
|5.||Conclusion and Next Steps (09 minutes)||Brief overview of advanced linear regression and machine learning topics|
|Exercises, practice, and how to get good at this|
|6.||Setting Up Your Environment (FAQ by Student Request) (37 minutes)||Anaconda Environment Setup|
|How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow|
|7.||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|
|8.||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)|
|9.||Appendix / FAQ Finale (08 minutes)||What is the Appendix?|
- How to take a derivative using calculus
- Python programming basiccs
- You must be familiar with probability to succeed in the course’s advanced section
Lebede Ngartera (5/5) : It’s a good match for me. I have a passion for math. Actually, I need this course to supplement my practical math skills. I will apply math in real situation with python. I think this course will help me to have a best job. Thank you very much.
- Ahmad Wahyudin (5/5) : hey, it’s a really awesome experience mastering machine learning with you.
- Ivan Radovic (5/5) : The implementation of all the concepts from the ground up in code is an excellent way to drive home the concepts.
- Ilija Simonovic (5/5) : This is a great course! It covers theory, math, intuition, and coding implementation of linear regression in Python.
- Mitchel Kokken (4/5) : What I think is the best aspect of this course is that it also provides you with excellent examples of the theory you have just learned.
- C M Berglund (1/5) : The tool code is presented in unnecessary and is a distraction.
- Jay Ramachandran (1/5) : Some one who doesn’t have any programming experience, just looking at the samples or not knowing how to run the program is really frustrating
- Clive Harris (2/5) : It didn’t start well – it was disjointed, poor video quality and poorly narrated.
- Martijn Bos (1/5) : That’s what he assumes every time somebody seems to have negative feedback.
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 148,353 Reviews on Udemy, he/she offers 33 Courses and has taught 526,884 Students so far.
- Although Instructor have been recognised as a data scientist, big data engineer, and full stack software engineer, Insturctor currently spend the majority of my 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 also earned second master’s degree in statistics with a focus on financial engineering
- 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
- Instructor 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
- 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
|Parameters||Deep Learning Prerequisites: Linear Regression in Python||Data Science: Modern Deep Learning in Python||Data Science: Deep Learning and Neural Networks in Python|
|Offers||INR 449 (||INR 455 (||INR 455 (|
|Duration||6.5 hours||11.5 hours||12 hours|
|Rating||4.6 /5||4.7 /5||4.6 /5|
|Instructors||Lazy Programmer Inc.||Lazy Programmer Inc.||Lazy Programmer Inc.|
|Register Here||Apply Now!||Apply Now!||Apply Now!|