Deep Learning

The ‘Deep Learning Prerequisites: Logistic Regression in Python’ course serves as an introduction to deep learning and neural networks. It covers logistic regression, a well-known and basic method used in machine learning, data science, and statistics.

This course is for you if you want to use your technical or mathematical expertise to make data-driven decisions and optimise your company using scientific principles. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘Deep Learning Prerequisites: Logistic Regression in Python’ for INR 499.

Who all can opt for this course?

  • Aspiring big data and data science professionals
  • Students who are considering a career in data science or machine learning
  • Students who want to learn how things actually work by implementing them in Python because they are sick of boring conventional statistics and prewritten functions in R
  • Those who are familiar with machine learning but want to understand how it relates to artificial intelligence
  • Individuals with an interest in addressing the computational neuroscience and machine learning divide

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 499 (INR 2,79980% off
Duration07 Hours
Student Enrollment28,699 students
InstructorLazy Programmer Inc.
Topics CoveredPython, Machine Learning, Deep Learning, Statistics
Course LevelIntermediate
Total Student Reviews3,993

Learning Outcomes

  • Python programming from start for logistic regression
  • Elucidate the data science applications of logistic regression
  • Derive the logistic regression’s error and update algorithm
  • Learn how logistic regression functions by using the biological neuron as an example
  • Real-world business issues like identifying facial expressions and predicting user behaviours from e-commerce data can be resolved using logistic regression
  • Comprehend the purpose of regularisation in machine learning

Course Content

S.No.Module (Duration)Topics
1.Start Here (36 minutes)Introduction and Outline
How to Succeed in this Course
Statistics vs. Machine Learning
Review of the classification problem
Introduction to the E-Commerce Course Project
Easy first quiz
2.Basics: What is linear classification? What’s the relation to neural networks? (01 hour 02 minutes)Linear Classification
Biological inspiration – the neuron
How do we calculate the output of a neuron / logistic classifier? – Theory
How do we calculate the output of a neuron / logistic classifier? – Code
Interpretation of Logistic Regression Output
E-Commerce Course Project: Pre-Processing the Data
E-Commerce Course Project: Making Predictions
Feedforward Quiz
Prediction Section Summary
Suggestion Box
3.Solving for the optimal weights (52 minutes)Training Section Introduction
A closed-form solution to the Bayes classifier
What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
The cross-entropy error function – Theory
The cross-entropy error function – Code
Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
Maximizing the likelihood
Updating the weights using gradient descent – Theory
Updating the weights using gradient descent – Code
E-Commerce Course Project: Training the Logistic Model
Training Section Summary
4.Practical concerns (54 minutes)Practical Section Introduction
Interpreting the Weights
L2 Regularization – Theory
L2 Regularization – Code
L1 Regularization – Theory
L1 Regularization – Code
L1 vs L2 Regularization
The donut problem
The XOR problem
Why Divide by Square Root of D?
Practical Section Summary
5.Checkpoint and applications: How to make sure you know your stuff (08 minutes)BONUS: Sentiment Analysis
BONUS: Exercises + how to get good at this
6.Project: Facial Expression Recognition (40 minutes)Facial Expression Recognition Project Introduction
Facial Expression Recognition Problem Description
The class imbalance problem
Utilities walkthrough
Facial Expression Recognition in Code
Facial Expression Recognition Project Summary
7.Background Review (04 minutes)Gradient Descent Tutorial
8.Setting Up Your Environment (FAQ by Student Request) (37 minutes)Anaconda Environment Setup
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
9.Extra Help With Python Coding for Beginners (FAQ by Student Request) (45 minutes)How to Uncompress a .tar.gz file
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
10.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)
11.Appendix / FAQ Finale (08 minutes)What is the Appendix?

Resources Required

  • Probability, matrix maths, and derivatives
  • You ought to be familiar with some fundamental Numpy Stack Python writing

Featured Review

Adriaan Vorster (3/5) : Learning is best reinforced by not trying to get the code examples used in the lectures to work. Refer to the examples that are available on Github.


  • Jeremy J Samuelson (5/5) : Really great! If I could make a request, I’d like to see a section that deals with model performance metrics, like accuracy, precision, recall, and AUROC.
  • Nikhil Kini (5/5) : I find these courses the best refreshers on the week of a machine learning job interview.
  • Zlatan Kremonic (5/5) : This is the best class I’ve ever taken on logistic regression, and I have a masters in Economics.
  • Sanjay Prasad (5/5) : The examples provided are the best, to start the course with.


  • Greg Donald (1/5) : 7) Given all the things I did not like, I will say the instructor appears to know the topic well, just not a good instructor or video producer.
  • Abdul Tawab Ajmal Safi (1/5) : I always have a hard time coding along and understanding his material although I am taking it in order.
  • Daniel Havir (1/5) : The material is not explained at all, usually just a formula is presented without any comment.
  • Greg Donald (1/5) : 1) The instructor speaks in a monotone voice and is not engaging whatsoever.

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, Lazy Programmer offers 33 Courses and has taught 527,254 Students so far.

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

Comparison Table

ParametersDeep Learning Prerequisites: Logistic Regression in PythonEnsemble Machine Learning in Python: Random Forest, AdaBoostData Science: Supervised Machine Learning in Python
OffersINR 499 (INR 2,799) 80% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration7 hours5.5 hours6.5 hours
Student Enrollments28,69715,12020,366
InstructorsLazy Programmer Inc.Lazy Programmer TeamLazy Programmer Team
Register HereApply Now!Apply Now!Apply Now!

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