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Pass the TensorFlow Developer Certification Exam by Google. Become an AI, Machine Learning, and Deep Learning expert!

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)

CNNs

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.

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

## Table of Contents

**Introduction**

1 Course Outline

2 Join Our Online Classroom!

3 Exercise Meet The Community

4 All Course Resources + Notebooks

**Deep Learning and TensorFlow Fundamentals**

5 What is deep learning

6 Why use deep learning

7 What are neural networks

8 What is deep learning already being used for

9 What is and why use TensorFlow

10 What is a Tensor

11 What we’re going to cover throughout the course

12 How to approach this course

13 Need A Refresher

14 Creating your first tensors with TensorFlow and tf.constant()

15 Creating tensors with TensorFlow and tf.Variable()

16 Creating random tensors with TensorFlow

17 Shuffling the order of tensors

18 Creating tensors from NumPy arrays

19 Getting information from your tensors (tensor attributes)

20 Indexing and expanding tensors

21 Manipulating tensors with basic operations

22 Matrix multiplication with tensors part 1

23 Matrix multiplication with tensors part 2

24 Matrix multiplication with tensors part 3

25 Changing the datatype of tensors

26 Tensor aggregation (finding the min, max, mean & more)

27 Tensor troubleshooting example (updating tensor datatypes)

28 Finding the positional minimum and maximum of a tensor (argmin and argmax)

29 Squeezing a tensor (removing all 1-dimension axes)

30 One-hot encoding tensors

31 Trying out more tensor math operations

32 Exploring TensorFlow and NumPy’s compatibility

33 Making sure our tensor operations run really fast on GPUs

34 TensorFlow Fundamentals challenge, exercises & extra-curriculum

35 Python + Machine Learning Monthly

36 LinkedIn Endorsements

**Neural network regression with TensorFlow**

37 Introduction to Neural Network Regression with TensorFlow

38 Inputs and outputs of a neural network regression model

39 Anatomy and architecture of a neural network regression model

40 Creating sample regression data (so we can model it)

41 The major steps in modelling with TensorFlow

42 Steps in improving a model with TensorFlow part 1

43 Steps in improving a model with TensorFlow part 2

44 Steps in improving a model with TensorFlow part 3

45 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise)

46 Evaluating a TensorFlow model part 2 (the three datasets)

47 Evaluating a TensorFlow model part 3 (getting a model summary)

48 Evaluating a TensorFlow model part 4 (visualising a model’s layers)

49 Evaluating a TensorFlow model part 5 (visualising a model’s predictions)

50 Evaluating a TensorFlow model part 6 (common regression evaluation metrics)

51 Evaluating a TensorFlow regression model part 7 (mean absolute error)

52 Evaluating a TensorFlow regression model part 7 (mean square error)

53 Setting up TensorFlow modelling experiments part 1 (start with a simple model)

54 Setting up TensorFlow modelling experiments part 2 (increasing complexity)

55 Comparing and tracking your TensorFlow modelling experiments

56 How to save a TensorFlow model

57 How to load and use a saved TensorFlow model

58 (Optional) How to save and download files from Google Colab

59 Putting together what we’ve learned part 1 (preparing a dataset)

60 Putting together what we’ve learned part 2 (building a regression model)

61 Putting together what we’ve learned part 3 (improving our regression model)

62 Preprocessing data with feature scaling part 1 (what is feature scaling)

63 Preprocessing data with feature scaling part 2 (normalising our data)

64 Preprocessing data with feature scaling part 3 (fitting a model on scaled data)

65 TensorFlow Regression challenge, exercises & extra-curriculum

**Neural network classification in TensorFlow**

66 Introduction to neural network classification in TensorFlow

67 Example classification problems (and their inputs and outputs)

68 Input and output tensors of classification problems

69 Typical architecture of neural network classification models with TensorFlow

70 Creating and viewing classification data to model

71 Checking the input and output shapes of our classification data

72 Building a not very good classification model with TensorFlow

73 Trying to improve our not very good classification model

74 Creating a function to view our model’s not so good predictions

75 Make our poor classification model work for a regression dataset

76 Non-linearity part 1 Straight lines and non-straight lines

77 Non-linearity part 2 Building our first neural network with non-linearity

78 Non-linearity part 3 Upgrading our non-linear model with more layers

79 Non-linearity part 4 Modelling our non-linear data once and for all

80 Non-linearity part 5 Replicating non-linear activation functions from scratch

81 Getting great results in less time by tweaking the learning rate

82 Using the TensorFlow History object to plot a model’s loss curves

83 Using callbacks to find a model’s ideal learning rate

84 Training and evaluating a model with an ideal learning rate

85 Introducing more classification evaluation methods

86 Finding the accuracy of our classification model

87 Creating our first confusion matrix (to see where our model is getting confused)

88 Making our confusion matrix prettier

89 Putting things together with multi-class classification part 1 Getting the data

90 Multi-class classification part 2 Becoming one with the data

91 Multi-class classification part 3 Building a multi-class classification model

92 Multi-class classification part 4 Improving performance with normalisation

93 Multi-class classification part 5 Comparing normalised and non-normalised data

94 Multi-class classification part 6 Finding the ideal learning rate

95 Multi-class classification part 7 Evaluating our model

96 Multi-class classification part 8 Creating a confusion matrix

97 Multi-class classification part 9 Visualising random model predictions

98 What patterns is our model learning

99 TensorFlow classification challenge, exercises & extra-curriculum

**Computer Vision and Convolutional Neural Networks in TensorFlow**

100 Introduction to Computer Vision with TensorFlow

101 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow

102 Downloading an image dataset for our first Food Vision model

103 Becoming One With Data

104 Becoming One With Data Part 2

105 Becoming One With Data Part 3

106 Building an end to end CNN Model

107 Using a GPU to run our CNN model 5x faster

108 Trying a non-CNN model on our image data

109 Improving our non-CNN model by adding more layers

110 Breaking our CNN model down part 1 Becoming one with the data

111 Breaking our CNN model down part 2 Preparing to load our data

112 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator

113 Breaking our CNN model down part 4 Building a baseline CNN model

114 Breaking our CNN model down part 5 Looking inside a Conv2D layer

115 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN

116 Breaking our CNN model down part 7 Evaluating our CNN’s training curves

117 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling

118 Breaking our CNN model down part 9 Reducing overfitting with data augmentation

119 Breaking our CNN model down part 10 Visualizing our augmented data

120 Breaking our CNN model down part 11 Training a CNN model on augmented data

121 Breaking our CNN model down part 12 Discovering the power of shuffling data

122 Breaking our CNN model down part 13 Exploring options to improve our model

123 Downloading a custom image to make predictions on

124 Writing a helper function to load and preprocessing custom images

125 Making a prediction on a custom image with our trained CNN

126 Multi-class CNN’s part 1 Becoming one with the data

127 Multi-class CNN’s part 2 Preparing our data (turning it into tensors)

128 Multi-class CNN’s part 3 Building a multi-class CNN model

129 Multi-class CNN’s part 4 Fitting a multi-class CNN model to the data

130 Multi-class CNN’s part 5 Evaluating our multi-class CNN model

131 Multi-class CNN’s part 6 Trying to fix overfitting by removing layers

132 Multi-class CNN’s part 7 Trying to fix overfitting with data augmentation

133 Multi-class CNN’s part 8 Things you could do to improve your CNN model

134 Multi-class CNN’s part 9 Making predictions with our model on custom images

135 Saving and loading our trained CNN model

136 TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum

**Transfer Learning in TensorFlow Part 1 Feature extraction**

137 What is and why use transfer learning

138 Downloading and preparing data for our first transfer learning model

139 Introducing Callbacks in TensorFlow and making a callback to track our models

140 Exploring the TensorFlow Hub website for pretrained models

141 Building and compiling a TensorFlow Hub feature extraction model

142 Blowing our previous models out of the water with transfer learning

143 Plotting the loss curves of our ResNet feature extraction model

144 Building and training a pre-trained EfficientNet model on our data

145 Different Types of Transfer Learning

146 Comparing Our Model’s Results

147 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum

**Transfer Learning in TensorFlow Part 2 Fine tuning**

148 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning

149 Importing a script full of helper functions (and saving lots of space)

150 Downloading and turning our images into a TensorFlow BatchDataset

151 Discussing the four (actually five) modelling experiments we’re running

152 Comparing the TensorFlow Keras Sequential API versus the Functional API

153 Creating our first model with the TensorFlow Keras Functional API

154 Compiling and fitting our first Functional API model

155 Getting a feature vector from our trained model

156 Drilling into the concept of a feature vector (a learned representation)

157 Downloading and preparing the data for Model 1 (1 percent of training data)

158 Building a data augmentation layer to use inside our model

159 Visualising what happens when images pass through our data augmentation layer

160 Building Model 1 (with a data augmentation layer and 1% of training data)

161 Building Model 2 (with a data augmentation layer and 10% of training data)

162 Creating a ModelCheckpoint to save our model’s weights during training

163 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint)

164 Loading and comparing saved weights to our existing trained Model 2

165 Preparing Model 3 (our first fine-tuned model)

166 Fitting and evaluating Model 3 (our first fine-tuned model)

167 Comparing our model’s results before and after fine-tuning

168 Downloading and preparing data for our biggest experiment yet (Model 4)

169 Preparing our final modelling experiment (Model 4)

170 Fine-tuning Model 4 on 100% of the training data and evaluating its results

171 Comparing our modelling experiment results in TensorBoard

172 How to view and delete previous TensorBoard experiments

173 Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum

**Transfer Learning with TensorFlow Part 3 Scaling Up**

174 Introduction to Transfer Learning Part 3 Scaling Up

175 Getting helper functions ready and downloading data to model

176 Outlining the model we’re going to build and building a ModelCheckpoint callback

177 Creating a data augmentation layer to use with our model

178 Creating a headless EfficientNetB0 model with data augmentation built in

179 Fitting and evaluating our biggest transfer learning model yet

180 Unfreezing some layers in our base model to prepare for fine-tuning

181 Fine-tuning our feature extraction model and evaluating its performance

182 Saving and loading our trained model

183 Downloading a pretrained model to make and evaluate predictions with

184 Making predictions with our trained model on 25,250 test samples

185 Unravelling our test dataset for comparing ground truth labels to predictions

186 Confirming our model’s predictions are in the same order as the test labels

187 Creating a confusion matrix for our model’s 101 different classes

188 Evaluating every individual class in our dataset

189 Plotting our model’s F1-scores for each separate class

190 Creating a function to load and prepare images for making predictions

191 Making predictions on our test images and evaluating them

192 Discussing the benefits of finding your model’s most wrong predictions

193 Writing code to uncover our model’s most wrong predictions

194 Plotting and visualising the samples our model got most wrong

195 Making predictions on and plotting our own custom images

196 Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum

**Milestone Project 1 Food Vision Big™**

197 Introduction to Milestone Project 1 Food Vision Big™

198 Making sure we have access to the right GPU for mixed precision training

199 Getting helper functions ready

200 Introduction to TensorFlow Datasets (TFDS)

201 Exploring and becoming one with the data (Food101 from TensorFlow Datasets)

202 Creating a preprocessing function to prepare our data for modelling

203 Batching and preparing our datasets (to make them run fast)

204 Exploring what happens when we batch and prefetch our data

205 Creating modelling callbacks for our feature extraction model

206 Turning on mixed precision training with TensorFlow

207 Creating a feature extraction model capable of using mixed precision training

208 Checking to see if our model is using mixed precision training layer by layer

209 Training and evaluating a feature extraction model (Food Vision Big™)

210 Introducing your Milestone Project 1 challenge build a model to beat DeepFood

211 Milestone Project 1 Food Vision Big™, exercises and extra-curriculum

**NLP Fundamentals in TensorFlow**

212 More Videos Coming Soon!

**Milestone Project 2 SkimLit**

213 More Videos Coming Soon!

**Time Series fundamentals in TensorFlow**

214 More Videos Coming Soon!

**Milestone Project 3 BitPredict**

215 More Videos Coming Soon!

**Passing the TensorFlow Developer Certificate Exam**

216 More Videos Coming Soon!

**Where To Go From Here**

217 Become An Alumni

218 LinkedIn Endorsements

219 TensorFlow Certificate

**Appendix Machine Learning Primer**

220 Quick Note Upcoming Videos

221 What is Machine Learning

222 AIMachine LearningData Science

223 Exercise Machine Learning Playground

224 How Did We Get Here

225 Exercise YouTube Recommendation Engine

226 Types of Machine Learning

227 Are You Getting It Yet

228 What Is Machine Learning Round 2

229 Section Review

**Appendix Machine Learning and Data Science Framework**

230 Quick Note Upcoming Videos

231 Section Overview

232 Introducing Our Framework

233 Step Machine Learning Framework

234 Types of Machine Learning Problems

235 Types of Data

236 Types of Evaluation

237 Features In Data

238 Modelling – Splitting Data

239 Modelling – Picking the Model

240 Modelling – Tuning

241 Modelling – Comparison

242 Overfitting and Underfitting Definitions

243 Experimentation

244 Tools We Will Use

245 Optional Elements of AI

**Appendix Pandas for Data Analysis**

246 Quick Note Upcoming Videos

247 Section Overview

248 Downloading Workbooks and Assignments

249 Pandas Introduction

250 Series, Data Frames and CSVs

251 Data from URLs

252 Describing Data with Pandas

253 Selecting and Viewing Data with Pandas

254 Selecting and Viewing Data with Pandas Part 2

255 Manipulating Data

256 Manipulating Data 2

257 Manipulating Data 3

258 Assignment Pandas Practice

259 How To Download The Course Assignments

**Appendix NumPy**

260 Quick Note Upcoming Videos

261 Section Overview

262 NumPy Introduction

263 Quick Note Correction In Next Video

264 NumPy DataTypes and Attributes

265 Creating NumPy Arrays

266 NumPy Random Seed

267 Viewing Arrays and Matrices

268 Manipulating Arrays

269 Manipulating Arrays 2

270 Standard Deviation and Variance

271 Reshape and Transpose

272 Dot Product vs Element Wise

273 Exercise Nut Butter Store Sales

274 Comparison Operators

275 Sorting Arrays

276 Turn Images Into NumPy Arrays

277 Assignment NumPy Practice

278 Optional Extra NumPy resources

**BONUS SECTION**

279 Special Bonus Lecture

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