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The ‘Deep Learning with Python and Keras’ course gives a thorough introduction to deep learning. The course begin by reviewing Deep Learning applications and giving a quick rundown of machine learning tools and methods. The course describes what Deep Learning is in a simple yet accurate way.

By the end of the course, you will be able to identify issues that Deep Learning can answer, develop and train a range of Neural Network models, and use cloud computing to expedite training and enhance the performance of your model. The course is usually available for INR 2,699 on Udemy but students can click on the link and get the ‘Deep Learning with Python and Keras’ for INR 449.

Who all can opt for this course?

  • Software developers those are interested in learning more about data science and the buzz surrounding deep learning
  • Anyone who wish to have a solid basic understanding of deep learning and are experienced with machine learning

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 2,69980% off
Duration10 Hours
Student Enrollment23,432 students
InstructorData Weekends
Topics CoveredMachine Learning, Deep Learning, Cloud GPUs, Neural Networks
Course LevelIntermediate
Total Student Reviews3,132

Learning Outcomes

  • To succinctly and precisely define what deep learning is
  • To describe how predictive models can be created using deep learning
  • T o determine which applications in the real world can profit from deep learning
  • Create deep learning models using Python and Keras then install them
  • Using deep learning to handle supervised and unsupervised learning issues using time series, tabular data, text, and pictures
  • To train and build fully connected, convolutional, and recurrent neural networks
  • To be able to adjust a deep learning model’s parameters while being able to examine its internals without feeling intimidated
  • To utilise a GPU to train and run models in the cloud
  • To calculate the expense of training huge models
  • To reuse previously trained models in order to reduce training costs and time (transfer learning)

Course Content

S.No.Module (Duration)Topics
1.Welcome to the course! (37 minutes)Welcome to the course!
Real world applications of deep learning
Download and install Anaconda
Installation Video Guide
Obtain the code for the course
Course Folder Walkthrough
Your first deep learning model
2.Data (01 hour 04 minutes)Section 2 Intro
Tabular data
Data exploration with Pandas code along
Visual data Exploration
Plotting with Matplotlib
Unstructured Data
Images and Sound in Jupyter
Feature Engineering
Exercise 1 Presentation
Exercise 1 Solution
Exercise 2 Presentation
Exercise 2 Solution
Exercise 3 Presentation
Exercise 3 Solution
Exercise 4 Presentation
Exercise 4 Solution
Exercise 5 Presentation
Exercise 5 Solution
3.Machine Learning (02 hours 04 minutes)Section 3 Intro
Machine Learning Problems
Supervised Learning
Linear Regression
Cost Function
Cost Function code along
Finding the best model
Linear Regression code along
Evaluating Performance
Evaluating Performance code along
Classification code along
Cross Validation
Cross Validation code along
Confusion matrix
Confusion Matrix code along
Feature Preprocessing code along
Exercise 1 Presentation
Exercise 1 solution
Exercise 2 Presentation
Exercise 2 solution
4.Deep Learning Intro (01 hour 16 minutes)Section 4 Intro
Deep Learning successes
Neural Networks
Deeper Networks
Neural Networks code along
Multiple Outputs
Multiclass classification code along
Activation Functions
Feed forward
Exercise 1 Presentation
Exercise 1 Solution
Exercise 2 Presentation
Exercise 2 Solution
Exercise 3 Presentation
Exercise 3 Solution
Exercise 4 Presentation
Exercise 4 Solution
5.Gradient Descent (01 hour 45 minutes)Section 5 Intro
Derivatives and Gradient
Backpropagation intuition
Chain Rule
Derivative Calculation
Fully Connected Backpropagation
Matrix Notation
Numpy Arrays code along
Learning Rate
Learning Rate code along
Gradient Descent
Gradient Descent code along
Optimizers code along
Initialization code along
Inner Layers Visualization code along
Exercise 1 Presentation
Exercise 1 Solution
Exercise 2 Presentation
Exercise 2 Solution
Exercise 3 Presentation
Exercise 3 Solution
Exercise 4 Presentation
Exercise 4 Solution
6.Convolutional Neural Networks (01 hour 20 minutes)Section 6 Intro
Features from Pixels
MNIST Classification
MNIST Classification code along
Beyond Pixels
Images as Tensors
Tensor Math code along
Convolution in 1 D
Convolution in 1 D code along
Convolution in 2 D
Image Filters code along
Convolutional Layers
Convolutional Layers code along
Pooling Layers
Pooling Layers code along
Convolutional Neural Networks
Convolutional Neural Networks code along
Weights in CNNs
Beyond Images
Exercise 1 Presentation
Exercise 1 Solution
Exercise 2 Presentation
Exercise 2 Solution
7.Cloud GPUs (01 minutes)Google Colaboratory GPU notebook setup
Floyd GPU notebook setup
8.Recurrent Neural Networks (45 minutes)Section 8 Intro
Time Series
Sequence problems
Vanilla RNN
Time Series Forecasting code along
Time Series Forecasting with LSTM code along
Rolling Windows
Rolling Windows code along
Exercise 1 Presentation
Exercise 1 Solution
Exercise 2 Presentation
Exercise 2 Solution
9.Improving performance (01 hour 01 minutes)Section 9 Intro
Learning curves
Learning curves code along
Batch Normalization
Batch Normalization code along
Dropout and Regularization code along
Data Augmentation
Continuous Learning
Image Generator code along
Hyperparameter search
Embeddings code along
Movies Reviews Sentiment Analysis code along
Exercise 1 Presentation
Exercise 1 Solution
Exercise 2 Presentation
Exercise 2 Solution
Exercise 3 Presentation

Resources Required

  • Experience with control flow (if/else, for loops), Python knowledge, and pythonic constructions (functions, classes, iterables, generators)
  • To copy and transfer files, use the bash shell (or a comparable command prompt) and simple commands
  • Basic familiarity with linear algebra (what is a vector, what is a matrix, how to calculate dot product)
  • Connecting to a cloud machine using SSH

Featured Review

Swapnil Jadhav (5/5) : This is one of the best courses I have ever completed. I knew the concepts but coding and analyzing them was the problem for me. With this course, now I can implement and prototype my ideas better and faster with keras. Looking forward for other relevent courses from “Data Weekends”.


  • Noriyuki Nakae (5/5) : This is the best Deap Learning course in Udemy I have ever taken.
  • kim (5/5) : This lecture explains functions being used in ML perfectly, including both codes along and meanings taught in statistics classes.
  • Neal Cariello (5/5) : I’m about 15% through and the pace and detail are perfect.
  • Gabriela Surpi (5/5) : Wonderful explanation on how to setup a separate environment with all the packages and versions that will be needed and that work well together.


  • Francisco Ramos (2/5) : Unfortunately, the fact that the course is not fully finished and Q&A seems to be abandoned, I feel a bit disappointed and I have no other option but giving it only 2 stars.
  • David Aubin (2/5) : I think if he had a qualified reviewer, review the class and fix the missing pieces.
  • Eric Peeters (1/5) : Course is not complete (final solution sections are missing), unbalanced (some simple topics get a lot of coverage, complicated topics are rushed).
  • Abhijit Bhattacharya (2/5) : I’m comparing this with machine learning A to Z which was extremely well delivered

About the Author

The instructor of this course is Data Weekends who is a Learn the essentials of Data Science in just one weekend. With 4.4 Instructor Rating and 3,174 Reviews on Udemy, he/she offers 3 Courses and has taught 24,661 Students so far.

  • Data WeekendsTM are accelerated data science workshops for programmers that teach you how to use predictive analytics on actual data in a short amount of time
  • Data analytics, machine learning, deep learning, and reinforcement learning are all topics that are discussed in the course
  • They also provide business training and consultancy on data science, machine learning, and deep learning through their parent firm, Catalit LLC
  • Francesco Mosconi, PhD, is the founder and principal lecturer of Data Weekends

Comparison Table

ParametersDeep Learning with Python and KerasArtificial Intelligence: Reinforcement Learning in PythonAdvanced AI: Deep Reinforcement Learning in Python
OffersINR 455 (INR 2,699) 80% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration10 hours14.5 hours10.5 hours
Student Enrollments23,41443,64836,737
InstructorsData WeekendsLazy Programmer TeamLazy Programmer Team
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