The ‘PyTorch for Deep Learning with Python Bootcamp’ course will teach you how to create state of the art neural networks for deep learning with Facebook’s PyTorch Deep Learning library. In this course, you will learn how to use NumPy to format data into arrays and classic machine learning theory principals.

This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘PyTorch for Deep Learning with Python Bootcamp’ for INR 499.

Who all can opt for this course?

  • Python developers those are intermediate to advanced and wish to learn PyTorch’s deep learning capabilities

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 499 (INR 2,79980% off
Duration17 Hours
Student Enrollment25,499 students
InstructorJose Portilla
Topics CoveredPyTorch Basics, NumPy, Pandas, Artificial Neural Networks, Deep Learning
Course LevelIntermediate
Total Student Reviews3,888

Learning Outcomes

  • Learn how to format data into arrays using NumPy
  • For data cleaning and manipulation, use pandas
  • Study the basic concepts of machine learning theory
  • Use the PyTorch Deep Learning Library to classify images
  • Recurrent neural networks for sequence time series data should be used with PyTorch
  • Build cutting-edge Deep Learning models that can handle tabular data

Course Content

S.No.Module (Duration)Topics
1.Course Overview, Installs, and Setup (25 minutes)COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!
Installation and Environment Setup
3.Crash Course: NumPy (46 minutes)Introduction to NumPy
NumPy Arrays
NumPy Arrays Part Two
Numpy Index Selection
NumPy Operations
Numpy Exercises
Numpy Exercises – Solutions
4.Crash Course: Pandas (01 hour 13 minutes)Pandas Overview
Pandas Series
Pandas DataFrames – Part One
Pandas DataFrames – Part Two
GroupBy Operations
Pandas Operations
Data Input and Output
Pandas Exercises
Pandas Exercises – Solutions
5.PyTorch Basics (54 minutes)PyTorch Basics Introduction
Tensor Basics
Tensor Basics – Part Two
Tensor Operations
Tensor Operations – Part Two
PyTorch Basics – Exercise
PyTorch Basics – Exercise Solutions
6.Machine Learning Concepts Overview (46 minutes)What is Machine Learning?
Supervised Learning
Evaluating Performance – Classification Error Metrics
Evaluating Performance – Regression Error Metrics
Unsupervised Learning
7.ANN – Artificial Neural Networks (04 hours 44 minutes)Introduction to ANN Section
Theory – Perceptron Model
Theory – Neural Network
Theory – Activation Functions
Multi-Class Classification
Theory – Cost Functions and Gradient Descent
Theory – BackPropagation
PyTorch Gradients
Linear Regression with PyTorch
Linear Regression with PyTorch – Part Two
DataSets with PyTorch
Basic Pytorch ANN – Part One
Basic PyTorch ANN – Part Two
Basic PyTorch ANN – Part Three
Introduction to Full ANN with PyTorch
Full ANN Code Along – Regression – Part One – Feature Engineering
Full ANN Code Along – Regression – Part 2 – Categorical and Continuous Features
Full ANN Code Along – Regression – Part Three – Tabular Model
Full ANN Code Along – Regression – Part Four – Training and Evaluation
Full ANN Code Along – Classification Example
ANN – Exercise Overview
ANN – Exercise Solutions
8.CNN – Convolutional Neural Networks (04 hours 21 minutes)Introduction to CNNs
Understanding the MNIST data set
ANN with MNIST – Part One – Data
ANN with MNIST – Part Two – Creating the Network
ANN with MNIST – Part Three – Training
ANN with MNIST – Part Four – Evaluation
Image Filters and Kernels
Convolutional Layers
Pooling Layers
MNIST Data Revisited
MNIST with CNN – Code Along – Part One
MNIST with CNN – Code Along – Part Two
MNIST with CNN – Code Along – Part Three
CIFAR-10 DataSet with CNN – Code Along – Part One
CIFAR-10 DataSet with CNN – Code Along – Part Two
Loading Real Image Data – Part One
Loading Real Image Data – Part Two
CNN on Custom Images – Part One – Loading Data
CNN on Custom Images – Part Two – Training and Evaluating Model
CNN on Custom Images – Part Three – PreTrained Networks
CNN Exercise
CNN Exercise Solutions
9.Recurrent Neural Networks (02 hours 10 minutes)Introduction to Recurrent Neural Networks
RNN Basic Theory
Vanishing Gradients
RNN Batches Theory
RNN – Creating Batches with Data
Basic RNN – Creating the LSTM Model
Basic RNN – Training and Forecasting
RNN on a Time Series – Part One
RNN on a Time Series – Part Two
RNN Exercise
RNN Exercise – Solutions
10.Using a GPU with PyTorch and CUDA (30 minutes)Why do we need GPUs?
Using GPU for PyTorch
11.NLP with PyTorch (01 hour 08 minutes)Introduction to NLP with PyTorch
Encoding Text Data
Generating Training Batches
Creating the LSTM Model
Training the LSTM Model
Generating Predictions

Resources Required

  • Knowledge of Python’s fundamental concepts (data types, loops, and functions) is advised
  • Able to perform simple derivative computations
  • Your computer’s admin permissions (ability to download our files)

Featured Review

Christopher Berry (5/5) : You couldn’t fault this course. The author explains everything perfectly for someone who has never been exposed to neural networks before. All of his courses are excellent, I think I own 12 of them.


  • Dian Wei (5/5) : Excellent course, a lot of details were provided which helps a lot.
  • Prasanta Dutta (5/5) : Though it is not the perfect time to rate the course.
  • David Morgan (5/5) : This was a very good course for me, coming to it with intermediate Python skills.
  • Abdoul Nasser Ibrahim (5/5) : this is a great course and I am once again convinced that Jose Portilla is the best udemy instructor in pyton and data science.


  • Pratikkumar Prajapati (1/5) : His answers are late (2 weeks per reply) and his replies are out of context.
  • Nikhil Gupta (2/5) : For example, batch size in calculations is hard coded and so the code breaks if used for another problem.
  • Nikhil Gupta (2/5) : Although there is a small section towards the end that shows what changes are needed to run on GPU, there are a lot of changes that need to be made to the code “templated” to get it working.
  • Nikhil Gupta (2/5) : Pros: The course provides some templates that can be sort of used for other problems.

About the Author

The instructor of this course is Jose Portilla who is a Head of Data Science at Pierian Training. With 4.6 Instructor Rating and 1,023,193 Reviews on Udemy, he/she offers 60 Courses and has taught 3,294,076 Students so far.

  • Jose Marcial Portilla holds degrees in mechanical engineering from Santa Clara University (BS and MS), and he has years of experience working as a qualified instructor and trainer for Python programming, machine learning, and data science
  • He has written articles and received patents in a number of disciplines, including data science, materials science, and microfluidics
  • He has acquired a set of abilities for data analysis throughout the course of his career, and he wants to combine both his teaching and data science knowledge to educate others the power of programming, how to analyse data, and how to display the data in attractive visualisations
  • He currently serves as the Head of Data Science for Pierian Training, where he trains people at prestigious organisations like General Electric, Cigna, The New York Times, Credit Suisse, McKinsey, and others in data science and python programming on-site
  • Please click the website link to learn more about the available training options

Comparison Table

ParametersPyTorch for Deep Learning with Python BootcampTensorflow 2.0: Deep Learning and Artificial IntelligenceComplete Tensorflow 2 and Keras Deep Learning Bootcamp
OffersINR 499 (INR 2,799) 80% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration17 hours23.5 hours19 hours
Student Enrollments25,49943,01243,659
InstructorsJose PortillaLazy Programmer Inc.Jose Portilla
Register HereApply Now!Apply Now!Apply Now!

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