TensorFlow Developer Certificate in 2023: Zero to Mastery
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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
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Introduction to tensors (creating tensors)
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Getting information from tensors (tensor attributes)
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Manipulating tensors (tensor operations)
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Tensors and NumPy
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Using @tf.function (a way to speed up your regular Python functions)
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Using GPUs with TensorFlow
1 — Neural Network Regression with TensorFlow
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Build TensorFlow sequential models with multiple layers
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Prepare data for use with a machine learning model
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Learn the different components which make up a deep learning model (loss function, architecture, optimization function)
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Learn how to diagnose a regression problem (predicting a number) and build a neural network for it
2 — Neural Network Classification with TensorFlow
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Learn how to diagnose a classification problem (predicting whether something is one thing or another)
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Build, compile & train machine learning classification models using TensorFlow
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Build and train models for binary and multi-class classification
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Plot modelling performance metrics against each other
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Match input (training data shape) and output shapes (prediction data target)
3 — Computer Vision and Convolutional Neural Networks with TensorFlow
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Build convolutional neural networks with Conv2D and pooling layers
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Learn how to diagnose different kinds of computer vision problems
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Learn to how to build computer vision neural networks
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Learn how to use real-world images with your computer vision models
4 — Transfer Learning with TensorFlow Part 1: Feature Extraction
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Learn how to use pre-trained models to extract features from your own data
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Learn how to use TensorFlow Hub for pre-trained models
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Learn how to use TensorBoard to compare the performance of several different models
5 — Transfer Learning with TensorFlow Part 2: Fine-tuning
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Learn how to setup and run several machine learning experiments
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Learn how to use data augmentation to increase the diversity of your training data
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Learn how to fine-tune a pre-trained model to your own custom problem
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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)
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Learn how to scale up an existing model
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Learn to how evaluate your machine learning models by finding the most wrong predictions
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Beat the original Food101 paper using only 10% of the data
7 — Milestone Project 1: Food Vision
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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
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Learn to:
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Preprocess natural language text to be used with a neural network
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Create word embeddings (numerical representations of text) with TensorFlow
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Build neural networks capable of binary and multi-class classification using:
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RNNs (recurrent neural networks)
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LSTMs (long short-term memory cells)
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GRUs (gated recurrent units)
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CNNs
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Learn how to evaluate your NLP models
9 — Milestone Project 2: SkimLit
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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
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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)
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Prepare data for time series neural networks (features and labels)
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Understanding and using different time series evaluation methods
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MAE — mean absolute error
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Build time series forecasting models with TensorFlow
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RNNs (recurrent neural networks)
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CNNs (convolutional neural networks)
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11 — Milestone Project 3: (Surprise)
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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!
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6What is deep learning?Video lesson
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7Why use deep learning?Video lesson
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8What are neural networks?Video lesson
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9Python + Machine Learning MonthlyText lesson
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10What is deep learning already being used for?Video lesson
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11What is and why use TensorFlow?Video lesson
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12What is a Tensor?Video lesson
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13What we're going to cover throughout the courseVideo lesson
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14How to approach this courseVideo lesson
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15Need A Refresher?Text lesson
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16Creating your first tensors with TensorFlow and tf.constant()Video lesson
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17Creating tensors with TensorFlow and tf.Variable()Video lesson
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18Creating random tensors with TensorFlowVideo lesson
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19Shuffling the order of tensorsVideo lesson
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20Creating tensors from NumPy arraysVideo lesson
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21Getting information from your tensors (tensor attributes)Video lesson
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22Indexing and expanding tensorsVideo lesson
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23Manipulating tensors with basic operationsVideo lesson
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24Matrix multiplication with tensors part 1Video lesson
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25Matrix multiplication with tensors part 2Video lesson
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26Matrix multiplication with tensors part 3Video lesson
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27Changing the datatype of tensorsVideo lesson
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28Tensor aggregation (finding the min, max, mean & more)Video lesson
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29Tensor troubleshooting example (updating tensor datatypes)Video lesson
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30Finding the positional minimum and maximum of a tensor (argmin and argmax)Video lesson
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31Squeezing a tensor (removing all 1-dimension axes)Video lesson
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32One-hot encoding tensorsVideo lesson
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33Trying out more tensor math operationsVideo lesson
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34Exploring TensorFlow and NumPy's compatibilityVideo lesson
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35Making sure our tensor operations run really fast on GPUsVideo lesson
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36TensorFlow Fundamentals challenge, exercises & extra-curriculumText lesson
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37Monthly Coding Challenges, Free Resources and GuidesText lesson
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38LinkedIn EndorsementsText lesson
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39Introduction to Neural Network Regression with TensorFlowVideo lesson
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40Inputs and outputs of a neural network regression modelVideo lesson
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41Anatomy and architecture of a neural network regression modelVideo lesson
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42Creating sample regression data (so we can model it)Video lesson
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43Note: Code update for upcoming lecture(s) for TensorFlow 2.7.0+ fixText lesson
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44The major steps in modelling with TensorFlowVideo lesson
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45Steps in improving a model with TensorFlow part 1Video lesson
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46Steps in improving a model with TensorFlow part 2Video lesson
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47Steps in improving a model with TensorFlow part 3Video lesson
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48Evaluating a TensorFlow model part 1 ("visualise, visualise, visualise")Video lesson
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49Evaluating a TensorFlow model part 2 (the three datasets)Video lesson
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50Evaluating a TensorFlow model part 3 (getting a model summary)Video lesson
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51Evaluating a TensorFlow model part 4 (visualising a model's layers)Video lesson
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52Evaluating a TensorFlow model part 5 (visualising a model's predictions)Video lesson
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53Evaluating a TensorFlow model part 6 (common regression evaluation metrics)Video lesson
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54Evaluating a TensorFlow regression model part 7 (mean absolute error)Video lesson
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55Evaluating a TensorFlow regression model part 7 (mean square error)Video lesson
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56Setting up TensorFlow modelling experiments part 1 (start with a simple model)Video lesson
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57Setting up TensorFlow modelling experiments part 2 (increasing complexity)Video lesson
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58Comparing and tracking your TensorFlow modelling experimentsVideo lesson
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59How to save a TensorFlow modelVideo lesson
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60How to load and use a saved TensorFlow modelVideo lesson
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61(Optional) How to save and download files from Google ColabVideo lesson
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62Putting together what we've learned part 1 (preparing a dataset)Video lesson
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63Putting together what we've learned part 2 (building a regression model)Video lesson
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64Putting together what we've learned part 3 (improving our regression model)Video lesson
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65Preprocessing data with feature scaling part 1 (what is feature scaling?)Video lesson
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66Preprocessing data with feature scaling part 2 (normalising our data)Video lesson
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67Preprocessing data with feature scaling part 3 (fitting a model on scaled data)Video lesson
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68TensorFlow Regression challenge, exercises & extra-curriculumText lesson
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69Learning GuidelineText lesson
