TensorFlow Developer Certificate in 2023: Zero to Mastery
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, text recognition algorithms with deep neural networks and convolutional neural networks
- Using real world images 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 building neural networks with TensorFlow and passing the TensorFlow Developer Certificate exam!
Join a live online community of over 900,000+ students and a course taught by a TensorFlow certified expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks and 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 2023 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
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
Andrei is the instructor of some of the highest rated programming and technical courses online. He is now the founder of ZTM Academy which is one of the fastest growing education platforms in the world. ZTM Academy is known for having some of the best instructors and success rates for students. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Tesla, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life.
Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities.
Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way.
Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.
See you inside the courses!
A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.
My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.
I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.
Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.
Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.
My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".
Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.
I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.
My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.
Questions are always welcome.