What you’ll learn
Learn to pass Google’s official TensorFlow Developer Certificate exam (and add it to your resume)
Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
Increase your skills in Machine Learning and Deep Learning, to test your abilities with the TensorFlow assessment exam
Understand how to integrate Machine Learning into tools and applications
Learn to build all types of Machine Learning Models using the latest TensorFlow 2
Build image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks
Using real-world images in different shapes and sizes to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
Applying Deep Learning for Time Series Forecasting
Gain the skills you need to become a TensorFlow Certified Developer
Be recognized as a top candidate for recruiters seeking TensorFlow developers
Mac / Windows / Linux – all operating systems work with this course!
No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful
Just launched with all modern best practices for working with TensorFlow and passing the TensorFlow Developer Certificate exam! Join a live online community of over 500,000+ students and a course taught by a TensorFlow certified expert. This course will take you from absolute beginner with TensorFlow, to becoming part of Google’s TensorFlow Certification Network.

TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD according to 2021 statistics. By passing this certificate, which is officially recognized by Google, you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow developer! If you pass the exam, you will also be part of Google’s TensorFlow Developer Network where recruiters are able to find you.

The goal of this course is to teach you all the skills necessary for you to go and pass this exam and get your TensorFlow Certification from Google so you can display it on your resume, LinkedIn, Github and other social media platforms to truly make you stand out.

Here is a full course breakdown of everything we will teach (yes, it’s very comprehensive, but don’t be intimidated, as we will teach you everything from scratch!):

This course will be very hands on and project based. You won’t just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. Most importantly, we will show you what the TensorFlow exam will look like for you. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter.

0 — TensorFlow Fundamentals
Introduction to tensors (creating tensors)
Getting information from tensors (tensor attributes)
Manipulating tensors (tensor operations)
Tensors and NumPy
Using @tf.function (a way to speed up your regular Python functions)
Using GPUs with TensorFlow

1 — Neural Network Regression with TensorFlow

Build TensorFlow sequential models with multiple layers
Prepare data for use with a machine learning model
Learn the different components which make up a deep learning model (loss function, architecture, optimization function)
Learn how to diagnose a regression problem (predicting a number) and build a neural network for it
2 — Neural Network Classification with TensorFlow

Learn how to diagnose a classification problem (predicting whether something is one thing or another)
Build, compile & train machine learning classification models using TensorFlow
Build and train models for binary and multi-class classification
Plot modelling performance metrics against each other
Match input (training data shape) and output shapes (prediction data target)

3 — Computer Vision and Convolutional Neural Networks with TensorFlow

Build convolutional neural networks with Conv2D and pooling layers
Learn how to diagnose different kinds of computer vision problems
Learn to how to build computer vision neural networks
Learn how to use real-world images with your computer vision models

4 — Transfer Learning with TensorFlow Part 1: Feature Extraction

Learn how to use pre-trained models to extract features from your own data
Learn how to use TensorFlow Hub for pre-trained models
Learn how to use TensorBoard to compare the performance of several different models

5 — Transfer Learning with TensorFlow Part 2: Fine-tuning

Learn how to setup and run several machine learning experiments
Learn how to use data augmentation to increase the diversity of your training data
Learn how to fine-tune a pre-trained model to your own custom problem
Learn how to use Callbacks to add functionality to your model during training

6 — Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)

Learn how to scale up an existing model
Learn to how evaluate your machine learning models by finding the most wrong predictions
Beat the original Food101 paper using only 10% of the data

7 — Milestone Project 1: Food Vision

Combine everything you’ve learned in the previous 6 notebooks to build Food Vision: a computer vision model able to classify 101 different kinds of foods. Our model well and truly beats the original Food101 paper.

8 — NLP Fundamentals in TensorFlow

Learn to:
Preprocess natural language text to be used with a neural network
Create word embeddings (numerical representations of text) with TensorFlow
Build neural networks capable of binary and multi-class classification using:
RNNs (recurrent neural networks)
LSTMs (long short-term memory cells)
GRUs (gated recurrent units)
Learn how to evaluate your NLP models

9 — Milestone Project 2: SkimLit

Replicate a the model which powers the PubMed 200k paper to classify different sequences in PubMed medical abstracts (which can help researchers read through medical abstracts faster)

10 — Time Series fundamentals in TensorFlow

Learn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow)
Prepare data for time series neural networks (features and labels)
Understanding and using different time series evaluation methods
MAE — mean absolute error
Build time series forecasting models with TensorFlow
RNNs (recurrent neural networks)
CNNs (convolutional neural networks)

11 — Milestone Project 3: (Surprise)

If you’ve read this far, you are probably interested in the course. This last project will be good.. we promise you, so see you inside the course 😉
TensorFlow is growing in popularity and more and more job openings are appearing for this specialized knowledge. As a matter of fact, TensorFlow is outgrowing other popular ML tools like PyTorch in job market. Google, Airbnb, Uber, DeepMind, Intel, IBM, Twitter, and many others are currently powered by TensorFlow. There is a reason these big tech companies are using this technology and you will find out all about the power that TensorFlow gives developers.

We guarantee you this is the most comprehensive online course on passing the TensorFlow Developer Certificate to qualify you as a TensorFlow expert. So why wait? Make yourself stand out by becoming a Google Certified Developer and advance your career.

See you inside the course!

Who this course is for:
Anyone who wants to pass the TensorFlow Developer exam so they can join Google’s Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world
Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow
Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
Anyone looking to master building ML models with the latest version of TensorFlow
Course content
20 sections • 279 lectures • 37h 24m total length
Deep Learning and TensorFlow Fundamentals
Neural network regression with TensorFlow
Neural network classification in TensorFlow
Computer Vision and Convolutional Neural Networks in TensorFlow
Transfer Learning in TensorFlow Part 1: Feature extraction
Transfer Learning in TensorFlow Part 2: Fine tuning
Transfer Learning with TensorFlow Part 3: Scaling Up
Milestone Project 1: Food Vision Big™ ??
NLP Fundamentals in TensorFlow
Milestone Project 2: SkimLit ??
Time Series fundamentals in TensorFlow
Milestone Project 3: BitPredict
Passing the TensorFlow Developer Certificate Exam
Where To Go From Here?
Appendix: Machine Learning Primer
Appendix: Machine Learning and Data Science Framework
Appendix: Pandas for Data Analysis
Appendix: NumPy
Created by: Andrei Neagoie, Daniel Bourke
Last updated 5/2021
English [Auto]
Highest Rated
Rating: 4.7 out of 54.7
(170 ratings)
3,475 students

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