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 Highlights | Details |
---|---|

Registration Link | Apply Now! |

Price | INR 499 (INR 2,799) 80% off |

Duration | 07 Hours |

Rating | 4.6/5 |

Student Enrollment | 28,699 students |

Instructor | Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc. |

Topics Covered | Python, Machine Learning, Deep Learning, Statistics |

Course Level | Intermediate |

Total Student Reviews | 3,993 |

## 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? |

BONUS |

## 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.

## Pros

**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.

## Cons

**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

Parameters | Deep Learning Prerequisites: Logistic Regression in Python | Ensemble Machine Learning in Python: Random Forest, AdaBoost | Data Science: Supervised Machine Learning in Python |
---|---|---|---|

Offers | INR 499 (80% off | INR 455 (87% off | INR 455 (87% off |

Duration | 7 hours | 5.5 hours | 6.5 hours |

Rating | 4.6/5 | 4.8/5 | 4.6/5 |

Student Enrollments | 28,697 | 15,120 | 20,366 |

Instructors | Lazy Programmer Inc. | Lazy Programmer Team | Lazy Programmer Team |

Register Here | Apply Now! | Apply Now! | Apply Now! |

## Leave feedback about this