The ‘Complete Guide to TensorFlow for Deep Learning with Python Course’ is a project-based course that teaches students how to use the TensorFlow framework to build Deep Learning models. The course requires students to have basic Python knowledge and know basic mathematical concepts such as standard deviation. It covers the basics of TensorFlow, neural networks, data flow graphs, reinforcement learning, and more.

The course is designed and instructed by Jose Portilla. He has years of experience working with Linux distributions and Unix operating systems. The course is usually available for INR 2,299 on Udemy but students can click on the link and get the ‘Complete Guide to TensorFlow for Deep Learning with Python Course’ for INR 449.

Who all can opt for this course?

Curious students who want to learn the latest Deep Learning techniques using TensorFlow.

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 2,29980% off
Duration14 hours
Student Enrollment93,845 students
InstructorJose Portilla
Topics CoveredMachine learning, neural networks, TensorFlow basics, convolutional neural networks, recurrent neural networks, etc.
Course LevelIntermediate (Candidates should know basic Python programming and standard deviations)
Total Student Reviews16,634

Learning Outcomes

  • Know how neural networks function
  • Create your own neural network from scratch using Python
  • Employ TensorFlow for tasks involving regression and classification
  • Convolutional neural networks for image classification using TensorFlow
  • Recurrent neural networks for time series analysis using TensorFlow
  • Utilize TensorFlow to use AutoEncoders to solve unsupervised learning issues
  • Use OpenAI Gym to learn how to apply reinforcement learning
  • Use TensorFlow to build generative adversarial networks
  • Become an expert in deep learning

Course Content

S.No.Module (Duration)Topics
1.Introduction (12 minutes)Introduction
Course Overview — PLEASE DON’T SKIP THIS LECTURE! Thanks 🙂
FAQ – Frequently Asked Questions
2.Installation and Setup (12 minutes)Quick Note for MacOS and Linux Users
Installing TensorFlow and Environment Setup
3.What is Machine Learning? (17 minutes)Machine Learning Overview
4.Crash Course Overview (45 minutes)Crash Course Section Introduction
NumPy Crash Course
Pandas Crash Course
Data Visualization Crash Course
SciKit Learn Preprocessing Overview
Crash Course Review Exercise
Crash Course Review Exercise – Solutions
5.Introduction to Neural Networks (01 hour 17 minutes)Introduction to Neural Networks
Introduction to Perceptron
Neural Network Activation Functions
Cost Functions
Gradient Descent Backpropagation
TensorFlow Playground
Manual Creation of Neural Network – Part One
Manual Creation of Neural Network – Part Two – Operations
Manual Creation of Neural Network – Part Three – Placeholders and Variables
Manual Creation of Neural Network – Part Four – Session
Manual Neural Network Classification Task
6.TensorFlow Basics (02 hours 39 minutes)Introduction to TensorFlow
TensorFlow Basic Syntax
TensorFlow Graphs
Variables and Placeholders
TensorFlow – A Neural Network – Part One
TensorFlow – A Neural Network – Part Two
TensorFlow Regression Example – Part One
TensorFlow Regression Example _ Part Two
TensorFlow Classification Example – Part One
TensorFlow Classification Example – Part Two
TF Regression Exercise
TF Regression Exercise Solution Walkthrough
TF Classification Exercise
TF Classification Exercise Solution Walkthrough
Saving and Restoring Models
7.Convolutional Neural Networks (02 hours 09 minutes)Introduction to Convolutional Neural Network Section
Review of Neural Networks
New Theory Topics
Quick note on MNIST lecture
MNIST Data Overview
MNIST Basic Approach Part One
MNIST Basic Approach Part Two
CNN Theory Part One
CNN Theory Part Two
CNN MNIST Code Along – Part One
CNN MNIST Code Along – Part Two
Introduction to CNN Project
CNN Project Exercise Solution – Part One
CNN Project Exercise Solution – Part Two
8.Recurrent Neural Networks (02 hours 38 minutes)Introduction to RNN Section
RNN Theory
Manual Creation of RNN
Vanishing Gradients
LSTM and GRU Theory
Introduction to RNN with TensorFlow API
RNN with TensorFlow – Part One
RNN with TensorFlow – Part Two
Quick Note on RNN Plotting Part 3
RNN with TensorFlow – Part Three
Time Series Exercise Overview
Time Series Exercise Solution
Quick Note on Word2Vec
Word2Vec Theory
Word2Vec Code Along – Part One
Word2Vec Part Two
9.Miscellaneous Topics (53 minutes)Intro to Miscellaneous Topics
Deep Nets with Tensorflow Abstractions API – Part One
Deep Nets with Tensorflow Abstractions API – Estimator API
Deep Nets with Tensorflow Abstractions API – Keras
Deep Nets with Tensorflow Abstractions API – Layers
10.AutoEncoders (54 minutes)Autoencoder Basics
Dimensionality Reduction with Linear Autoencoder
Linear Autoencoder PCA Exercise Overview
Linear Autoencoder PCA Exercise Solutions
Stacked Autoencoder
11.Reinforcement Learning with OpenAI Gym (01 hour 27 minutes)Introduction to Reinforcement Learning with OpenAI Gym
Extra Resources for Reinforcement Learning
Introduction to OpenAI Gym
OpenAI Gym Steup
Open AI Gym Env Basics
Open AI Gym Observations
OpenAI Gym Actions
Simple Neural Network Game
Policy Gradient Theory
Policy Gradient Code Along Part One
Policy Gradient Code Along Part Two
12.GAN – Generative Adversarial Networks (39 minutes)Introduction to GANs
GAN Code Along – Part One
GAN Code Along – Part Two
GAN Code Along – Part Three
13.BONUS (10 seconds)Bonus Lecture

Resources Required

  • Basic Python programming
  • Basic math (mean, standard deviation, etc.)

Featured Review

Navneet Singh (5/5): I like the pace so far. It gets confusing sometimes in between but you cannot blame the instructor for that as the topic in itself is quite complicated in few places. Having said that, the instructor does try his best to simplify the explanations.


  • Michael Brownfield (5/5): Excellent Course! Provides everything that is needed to get you running.
  • Rick Vink (5/5): All the networks (RNN, CNN, Autoencoder and GANs) that I definitly wanted to know are included so great course!
  • Jonas Stepanik (5/5): Thank you for this awesome course Here is my github:
  • Ismalia Bouba (5/5): Excellent, i will recommend this to any data scientist out there.


  • Bill Sheppard (1/5): This makes all of the labs COMPLETELY BROKEN! Plenty of users asking for it to be updated but the Author has been unresponsive.
  • Duddu Venkata sai ayyappa hemanth (1/5): This is too bad.!!! That’s why I shifted to coursera, they are giving back the help within hours.
  • Emanuel Hernandez (1/5): I have been programming for 4 years now and it is still so difficult to see what he is doing.
  • Robbe Vanhaesebroeck (1/5): This course teaches Tensorflow 1.0 which by now is completely outdated.

About the Author

The instructor of this course is Jose Portilla who is the head of the Data Science department at Pierian Training. With a 4.6 instructor rating and 1,021,912 reviews on Udemy, he offers 60 courses and has taught 3,289,015 students so far.

  • Jose 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 on 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.

Comparison Table

ParametersComplete Guide to TensorFlow for Deep Learning with PythonData Science: Modern Deep Learning in PythonData Science: Deep Learning and Neural Networks in Python
OffersINR 449 (INR 2,299) 80% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration14 hours11.5 hours12 hours
Rating4.2/54.7 /54.7 /5
Student Enrollments93,84533,61052,689
InstructorsJose PortillaLazy Programmer Inc.Lazy Programmer Inc.
Register HereApply Now!Apply Now!Apply Now!

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