Section 1 : Introduction

 		Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 About Certification Pdf
Lecture 3 About Proctor Testing Pdf
Lecture 4 All Course Resources + Notebooks Text
Lecture 5 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Deep Learning and TensorFlow Fundamentals

 		Lecture 6 What is deep learning 4:23
Lecture 7 Why use deep learning 9:39
Lecture 8 What are neural networks 10:26
Lecture 9 What is deep learning already being used for 8:37
Lecture 10 What is and why use TensorFlow 7:56
Lecture 11 What is a Tensor 3:38
Lecture 12 What we're going to cover throughout the course 4:29
Lecture 13 How to approach this course 5:34
Lecture 14 Need A Refresher Text
Lecture 15 Creating your first tensors with TensorFlow and tf 18:45
Lecture 16 Creating tensors with TensorFlow and tf 7:7
Lecture 17 Creating random tensors with TensorFlow 9:40
Lecture 18 Shuffling the order of tensors 9:40
Lecture 19 Creating tensors from NumPy arrays 11:55
Lecture 20 Getting information from your tensors (tensor attributes) 11:57
Lecture 21 Indexing and expanding tensors 12:33
Lecture 22 Manipulating tensors with basic operations 5:34
Lecture 23 Matrix multiplication with tensors part 1 11:53
Lecture 24 Matrix multiplication with tensors part 2 13:29
Lecture 25 Matrix multiplication with tensors part 3 10:3
Lecture 26 Changing the datatype of tensors 6:56
Lecture 27 Tensor aggregation (finding the min, max, mean & more) 9:49
Lecture 28 Tensor troubleshooting example (updating tensor datatypes) 6:14
Lecture 29 Finding the positional minimum and maximum of a tensor (argmin and argmax) 9:31
Lecture 30 Squeezing a tensor (removing all 1-dimension axes) 3:0
Lecture 31 One-hot encoding tensors 5:46
Lecture 32 Trying out more tensor math operations 4:48
Lecture 33 Exploring TensorFlow and NumPy's compatibility 5:43
Lecture 34 Making sure our tensor operations run really fast on GPUs 10:19
Lecture 35 TensorFlow Fundamentals challenge, exercises & extra-curriculum Text
Lecture 36 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 37 About Certification Pdf

Section 3 : Neural network regression with TensorFlow

 		Lecture 38 Introduction to Neural Network Regression with TensorFlow 7:33
Lecture 39 Inputs and outputs of a neural network regression model 8:59
Lecture 40 Anatomy and architecture of a neural network regression model 7:55
Lecture 41 Creating sample regression data (so we can model it) 12:47
Lecture 42 Note Code update for upcoming lecture(s) for TensorFlow 2 Text
Lecture 43 The major steps in modelling with TensorFlow 20:15
Lecture 44 Steps in improving a model with TensorFlow part 1 6:3
Lecture 45 Steps in improving a model with TensorFlow part 2 9:26
Lecture 46 Steps in improving a model with TensorFlow part 3 12:33
Lecture 47 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise) 7:24
Lecture 48 Evaluating a TensorFlow model part 2 (the three datasets) 11:2
Lecture 49 Evaluating a TensorFlow model part 3 (getting a model summary) 17:18
Lecture 50 Evaluating a TensorFlow model part 4 (visualising a model's layers) 7:15
Lecture 51 Evaluating a TensorFlow model part 5 (visualising a model's predictions) 9:16
Lecture 52 Evaluating a TensorFlow model part 6 (common regression evaluation metrics) 8:6
Lecture 53 Evaluating a TensorFlow regression model part 7 (mean absolute error) 5:53
Lecture 54 Evaluating a TensorFlow regression model part 7 (mean square error) 3:19
Lecture 55 Setting up TensorFlow modelling experiments part 1 (start with a simple model) 13:50
Lecture 56 Setting up TensorFlow modelling experiments part 2 (increasing complexity) 11:30
Lecture 57 Comparing and tracking your TensorFlow modelling experiments 10:20
Lecture 58 How to save a TensorFlow model 8:20
Lecture 59 How to load and use a saved TensorFlow model 10:16
Lecture 60 (Optional) How to save and download files from Google Colab 6:19
Lecture 61 Putting together what we've learned part 1 (preparing a dataset) 13:31
Lecture 62 Putting together what we've learned part 2 (building a regression model) 13:21
Lecture 63 Putting together what we've learned part 3 (improving our regression model) 15:47
Lecture 64 Preprocessing data with feature scaling part 1 (what is feature scaling) 9:34
Lecture 65 Preprocessing data with feature scaling part 2 (normalising our data) 10:57
Lecture 66 Preprocessing data with feature scaling part 3 (fitting a model on scaled data) 7:41
Lecture 67 TensorFlow Regression challenge, exercises & extra-curriculum Text
Lecture 68 Learning Guideline Text

Section 4 : Neural network classification in TensorFlow

 		Lecture 69 Introduction to neural network classification in TensorFlow 8:25
Lecture 70 Example classification problems (and their inputs and outputs) 6:38
Lecture 71 Input and output tensors of classification problems 6:22
Lecture 72 Typical architecture of neural network classification models with TensorFlow 9:36
Lecture 73 Creating and viewing classification data to model 11:34
Lecture 74 Checking the input and output shapes of our classification data 4:38
Lecture 75 Building a not very good classification model with TensorFlow 12:11
Lecture 76 Trying to improve our not very good classification model 9:13
Lecture 77 Creating a function to view our model's not so good predictions 15:8
Lecture 78 Note Updates for TensorFlow 2 Text
Lecture 79 Make our poor classification model work for a regression dataset 12:19
Lecture 80 Non-linearity part 1 Straight lines and non-straight lines 9:39
Lecture 81 Non-linearity part 2 Building our first neural network with non-linearity 5:47
Lecture 82 Non-linearity part 3 Upgrading our non-linear model with more layers 10:19
Lecture 83 Non-linearity part 4 Modelling our non-linear data once and for all 8:38
Lecture 84 Non-linearity part 5 Replicating non-linear activation functions from scratch
Lecture 85 Getting great results in less time by tweaking the learning rate 14:47
Lecture 86 Using the TensorFlow History object to plot a model's loss curves 6:12
Lecture 87 Using callbacks to find a model's ideal learning rate 17:32
Lecture 88 Training and evaluating a model with an ideal learning rate 9:21
Lecture 89 Introducing more classification evaluation methods 6:5
Lecture 90 Finding the accuracy of our classification model 4:18
Lecture 91 Creating our first confusion matrix (to see where our model is getting confused) 8:28
Lecture 92 Making our confusion matrix prettier 14:1
Lecture 93 Putting things together with multi-class classification part 1 Getting the data 10:37
Lecture 94 Multi-class classification part 2 Becoming one with the data 7:8
Lecture 95 Multi-class classification part 3 Building a multi-class classification model 15:38
Lecture 96 Multi-class classification part 4 Improving performance with normalisation 12:43
Lecture 97 Multi-class classification part 5 Comparing normalised and non-normalised data 4:14
Lecture 98 Multi-class classification part 6 Finding the ideal learning rate 10:39
Lecture 99 Multi-class classification part 7 Evaluating our model 13:16
Lecture 100 Multi-class classification part 8 Creating a confusion matrix 4:26
Lecture 101 Multi-class classification part 9 Visualising random model predictions
Lecture 102 What patterns is our model learning 15:33
Lecture 103 TensorFlow classification challenge, exercises & extra-curriculum Text

Section 5 : Computer Vision and Convolutional Neural Networks in TensorFlow

 		Lecture 104 Introduction to Computer Vision with TensorFlow 9:36
Lecture 105 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow 8:0
Lecture 106 Downloading an image dataset for our first Food Vision model 8:27
Lecture 107 Becoming One With Data 5:5
Lecture 108 Becoming One With Data Part 2 12:26
Lecture 109 Becoming One With Data Part 3 4:23
Lecture 110 Building an end to end CNN Model 18:18
Lecture 111 Using a GPU to run our CNN model 5x faster 9:17
Lecture 112 Trying a non-CNN model on our image data 8:51
Lecture 113 Improving our non-CNN model by adding more layers 9:52
Lecture 114 Breaking our CNN model down part 1 Becoming one with the data 9:3
Lecture 115 Breaking our CNN model down part 2 Preparing to load our data 11:46
Lecture 116 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 117 Breaking our CNN model down part 4 Building a baseline CNN model 8:3
Lecture 118 Breaking our CNN model down part 5 Looking inside a Conv2D layer 15:21
Lecture 119 About Certification Pdf
Lecture 120 Breaking our CNN model down part 7 Evaluating our CNN's training curves 11:46
Lecture 121 About Proctor Testing Pdf
Lecture 122 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 123 Breaking our CNN model down part 10 Visualizing our augmented data 15:4
Lecture 124 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 125 About Certification Pdf
Lecture 126 About Proctor Testing Pdf
Lecture 127 Downloading a custom image to make predictions on 4:54
Lecture 128 Writing a helper function to load and preprocessing custom images 10:1
Lecture 129 Making a prediction on a custom image with our trained CNN 10:9
Lecture 130 Multi-class CNN's part 1 Becoming one with the data 14:59
Lecture 131 Multi-class CNN's part 2 Preparing our data (turning it into tensors) 6:38
Lecture 132 Multi-class CNN's part 3 Building a multi-class CNN model 7:25
Lecture 133 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data 6:3
Lecture 134 Multi-class CNN's part 5 Evaluating our multi-class CNN model 4:51
Lecture 135 Multi-class CNN's part 6 Trying to fix overfitting by removing layers 12:20
Lecture 136 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 137 Multi-class CNN's part 8 Things you could do to improve your CNN model 4:24
Lecture 138 About Certification Pdf
Lecture 139 Saving and loading our trained CNN model 6:21
Lecture 140 TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum Text

Section 6 : Transfer Learning in TensorFlow Part 1 Feature extraction

 		Lecture 141 What is and why use transfer learning 10:12
Lecture 142 Downloading and preparing data for our first transfer learning model 14:40
Lecture 143 Introducing Callbacks in TensorFlow and making a callback to track our models 10:1
Lecture 144 Exploring the TensorFlow Hub website for pretrained models 9:51
Lecture 145 Building and compiling a TensorFlow Hub feature extraction model
Lecture 146 Blowing our previous models out of the water with transfer learning 9:13
Lecture 147 Plotting the loss curves of our ResNet feature extraction model 7:36
Lecture 148 Building and training a pre-trained EfficientNet model on our data 9:42
Lecture 149 Different Types of Transfer Learning 11:40
Lecture 150 Comparing Our Model's Results 15:17
Lecture 151 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum Text
Lecture 152 Exercise Imposter Syndrome 2:56

Section 7 : Transfer Learning in TensorFlow Part 2 Fine tuning

 		Lecture 153 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning 6:16
Lecture 154 Importing a script full of helper functions (and saving lots of space) 7:35
Lecture 155 Downloading and turning our images into a TensorFlow BatchDataset 15:39
Lecture 156 Discussing the four (actually five) modelling experiments we're running 2:15
Lecture 157 Comparing the TensorFlow Keras Sequential API versus the Functional API 2:34
Lecture 158 Creating our first model with the TensorFlow Keras Functional API 11:39
Lecture 159 Compiling and fitting our first Functional API model 10:54
Lecture 160 Getting a feature vector from our trained model 13:39
Lecture 161 Drilling into the concept of a feature vector 3:43
Lecture 162 Downloading and preparing the data for Model 1 (1 percent of training data) 9:52
Lecture 163 Building a data augmentation layer to use inside our model 12:6
Lecture 164 Note Small fix for next video, for images not augmenting Text
Lecture 165 Visualizing what happens when images pass through our data augmentation layer 10:56
Lecture 166 Building Model 1 (with a data augmentation layer and 1% of training data) 15:55
Lecture 167 Building Model 2 (with a data augmentation layer and 10% of training data) 16:37
Lecture 168 Creating a ModelCheckpoint to save our model's weights during training 7:25
Lecture 169 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint) 7:14
Lecture 170 Loading and comparing saved weights to our existing trained Model 2 7:18
Lecture 171 Preparing Model 3 (our first fine-tuned model) 20:27
Lecture 172 Fitting and evaluating Model 3 (our first fine-tuned model) 7:46
Lecture 173 Comparing our model's results before and after fine-tuning 10:27
Lecture 174 Downloading and preparing data for our biggest experiment yet (Model 4) 6:25
Lecture 175 Preparing our final modelling experiment (Model 4) 12:0
Lecture 176 Fine-tuning Model 4 on 100% of the training data and evaluating its results 10:19
Lecture 177 Comparing our modelling experiment results in TensorBoard 10:46
Lecture 178 How to view and delete previous TensorBoard experiments 2:4
Lecture 179 Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum Text

Section 8 : Transfer Learning with TensorFlow Part 3 Scaling Up

 		Lecture 180 Introduction to Transfer Learning Part 3 Scaling Up 6:20
Lecture 181 Getting helper functions ready and downloading data to model 13:34
Lecture 182 Outlining the model we're going to build and building a ModelCheckpoint callback 5:39
Lecture 183 Creating a data augmentation layer to use with our model 4:40
Lecture 184 Creating a headless EfficientNetB0 model with data augmentation built in 8:59
Lecture 185 Fitting and evaluating our biggest transfer learning model yet 7:57
Lecture 186 Unfreezing some layers in our base model to prepare for fine-tuning 11:28
Lecture 187 Fine-tuning our feature extraction model and evaluating its performance 8:24
Lecture 188 Saving and loading our trained model 6:26
Lecture 189 Downloading a pretrained model to make and evaluate predictions with 6:34
Lecture 190 Making predictions with our trained model on 25,250 test samples 12:47
Lecture 191 Unravelling our test dataset for comparing ground truth labels to predictions 6:5
Lecture 192 Confirming our model's predictions are in the same order as the test labels 5:17
Lecture 193 Creating a confusion matrix for our model's 101 different classes 12:8
Lecture 194 Evaluating every individual class in our dataset 14:16
Lecture 195 Plotting our model's F1-scores for each separate class 7:36
Lecture 196 Creating a function to load and prepare images for making predictions 12:9
Lecture 197 Making predictions on our test images and evaluating them 16:6
Lecture 198 Discussing the benefits of finding your model's most wrong predictions 6:9
Lecture 199 Writing code to uncover our model's most wrong predictions 11:16
Lecture 200 Plotting and visualising the samples our model got most wrong 10:36
Lecture 201 Making predictions on and plotting our own custom images 9:50
Lecture 202 Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum Text

Section 9 : Milestone Project 1 Food Vision Big™

 		Lecture 203 Introduction to Milestone Project 1 Food Vision Big™ 5:44
Lecture 204 Making sure we have access to the right GPU for mixed precision training 10:18
Lecture 205 Getting helper functions ready 3:6
Lecture 206 Introduction to TensorFlow Datasets (TFDS) 12:3
Lecture 207 Exploring and becoming one with the data (Food101 from TensorFlow Datasets) 15:56
Lecture 208 Creating a preprocessing function to prepare our data for modelling 15:50
Lecture 209 Batching and preparing our datasets (to make them run fast) 13:48
Lecture 210 Exploring what happens when we batch and prefetch our data 6:49
Lecture 211 Creating modelling callbacks for our feature extraction model 7:14
Lecture 212 Note Mixed Precision producing errors for TensorFlow 2 Text
Lecture 213 Turning on mixed precision training with TensorFlow 10:5
Lecture 214 Creating a feature extraction model capable of using mixed precision training 12:42
Lecture 215 Checking to see if our model is using mixed precision training layer by layer 7:57
Lecture 216 Training and evaluating a feature extraction model (Food Vision Big™) 10:19
Lecture 217 Introducing your Milestone Project 1 challenge build a model to beat DeepFood 7:48
Lecture 218 Milestone Project 1 Food Vision Big™, exercises and extra-curriculum Text

Section 10 : NLP Fundamentals in TensorFlow

 		Lecture 219 Welcome to natural language processing with TensorFlow! Text
Lecture 220 Introduction to Natural Language Processing 12:52
Lecture 221 Example NLP inputs and outputs 7:22
Lecture 222 The typical architecture of a Recurrent Neural Network (RNN) 9:3
Lecture 223 Preparing a notebook for our first NLP with TensorFlow project 8:53
Lecture 224 Becoming one with the data and visualising a text dataset 16:41
Lecture 225 Splitting data into training and validation sets 6:27
Lecture 226 Converting text data to numbers using tokenisation and embeddings (overview) 9:23
Lecture 227 Setting up a TensorFlow TextVectorization layer to convert text to numbers 17:10
Lecture 228 Mapping the TextVectorization layer to text data and turning it into numbers 11:3
Lecture 229 Creating an Embedding layer to turn tokenised text into embedding vectors 12:27
Lecture 230 Discussing the various modelling experiments we're going to run 8:58
Lecture 231 Model 0 Building a baseline model to try and improve upon 9:25
Lecture 232 Creating a function to track and evaluate our model's results 12:14
Lecture 233 Model 1 Building, fitting and evaluating our first deep model on text data 20:52
Lecture 234 Visualising our model's learned word embeddings with TensorFlow's projector tool 20:44
Lecture 235 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more 9:34
Lecture 236 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM) 18:17
Lecture 237 Model 3 Building, fitting and evaluating a GRU-cell powered RNN 16:56
Lecture 238 Model 4 Building, fitting and evaluating a bidirectional RNN model 19:35
Lecture 239 Discussing the intuition behind Conv1D neural networks for text and sequences 19:32
Lecture 240 Model 5 Building, fitting and evaluating a 1D CNN for text 9:58
Lecture 241 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP) 13:45
Lecture 242 Model 6 Building, training and evaluating a transfer learning model for NLP 10:46
Lecture 243 Preparing subsets of data for model 7 (same as model 6 but 10% of data) 10:52
Lecture 244 Model 7 Building, training and evaluating a transfer learning model on 10% data 10:4
Lecture 245 Fixing our data leakage issue with model 7 and retraining it 13:43
Lecture 246 Comparing all our modelling experiments evaluation metrics 13:14
Lecture 247 Uploading our model's training logs to TensorBoard and comparing them 11:15
Lecture 248 Saving and loading in a trained NLP model with TensorFlow 10:25
Lecture 249 Downloading a pretrained model and preparing data to investigate predictions 13:25
Lecture 250 Visualising our model's most wrong predictions 8:29
Lecture 251 Making and visualising predictions on the test dataset 8:27
Lecture 252 Understanding the concept of the speedscore tradeoff 15:2
Lecture 253 NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum Text

Section 11 : Milestone Project 2 SkimLit

 		Lecture 254 Introduction to Milestone Project 2 SkimLit 14:20
Lecture 255 What we're going to cover in Milestone Project 2 (NLP for medical abstracts) 7:22
Lecture 256 SkimLit inputs and outputs 11:2
Lecture 257 Setting up our notebook for Milestone Project 2 (getting the data) 14:58
Lecture 258 Visualising examples from the dataset (becoming one with the data) 13:18
Lecture 259 Writing a preprocessing function to structure our data for modelling
Lecture 260 Performing visual data analysis on our preprocessed text 7:55
Lecture 261 Turning our target labels into numbers (ML models require numbers) 13:15
Lecture 262 Model 0 Creating, fitting and evaluating a baseline model for SkimLit 9:26
Lecture 263 Preparing our data for deep sequence models 9:56
Lecture 264 Creating a text vectoriser to map our tokens (text) to numbers 14:7
Lecture 265 Creating a custom token embedding layer with TensorFlow 9:14
Lecture 266 Creating fast loading dataset with the TensorFlow tf 9:50
Lecture 267 Model 1 Building, fitting and evaluating a Conv1D with token embeddings 17:21
Lecture 268 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2 10:53
Lecture 269 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings 11:31
Lecture 270 Creating a character-level tokeniser with TensorFlow's TextVectorization layer 23:24
Lecture 271 Creating a character-level embedding layer with tf 7:44
Lecture 272 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings 13:45
Lecture 273 Discussing how we're going to build Model 4 (character + token embeddings) 6:5
Lecture 274 Model 4 Building a multi-input model (hybrid token + character embeddings) 15:36
Lecture 275 Model 4 Plotting and visually exploring different data inputs 7:32
Lecture 276 Crafting multi-input fast loading tf 8:41
Lecture 277 Model 4 Building, fitting and evaluating a hybrid embedding model 13:18
Lecture 278 Model 5 Adding positional embeddings via feature engineering (overview) 7:18
Lecture 279 Encoding the line number feature to used with Model 5 12:26
Lecture 280 Encoding the total lines feature to be used with Model 5 7:56
Lecture 281 Model 5 Building the foundations of a tribrid embedding model 9:19
Lecture 282 Model 5 Completing the build of a tribrid embedding model for sequences 14:9
Lecture 283 Visually inspecting the architecture of our tribrid embedding model 10:25
Lecture 284 Creating multi-level data input pipelines for Model 5 with the tf 9:0
Lecture 285 Bringing SkimLit to life!!! (fitting and evaluating Model 5) 10:35
Lecture 286 Comparing the performance of all of our modelling experiments 9:36
Lecture 287 Saving, loading & testing our best performing model 7:49
Lecture 288 Congratulations and your challenge before heading to the next module 12:34
Lecture 289 Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum Text

Section 12 : Time Series fundamentals in TensorFlow + Milestone Project 3 BitPredict

 		Lecture 290 Welcome to time series fundamentals with TensorFlow + Milestone Project 3! Text
Lecture 291 Example forecasting problems in daily life 4:52
Lecture 292 What can be forecast 7:58
Lecture 293 What we're going to cover (broadly) 2:35
Lecture 294 Time series forecasting inputs and outputs 8:56
Lecture 295 Downloading and inspecting our Bitcoin historical dataset 14:58
Lecture 296 Visualizing our Bitcoin historical data with pandas 4:53
Lecture 297 Reading in our Bitcoin data with Python's CSV module 10:58
Lecture 298 Creating train and test splits for time series (the wrong way) 8:37
Lecture 299 Creating train and test splits for time series (the right way) 7:12
Lecture 300 Creating a plotting function to visualize our time series data 7:57
Lecture 301 Model 0 Making and visualizing a naive forecast model 12:16
Lecture 302 Implementing MASE with TensorFlow 9:38
Lecture 303 Discussing the use of windows and horizons in time series data 7:50
Lecture 304 Creating a function to make predictions with our trained models 14:3
Lecture 305 Model 3 Visualizing the results 8:44
Lecture 306 Comparing our modelling experiments so far and discussing autocorrelation 9:44
Lecture 307 Preparing data for building a Conv1D model 13:21
Lecture 308 Preparing our multivariate time series for a model 13:37
Lecture 309 Model 7 Setting up hyperparameters for the N-BEATS algorithm 8:51
Lecture 310 Model 7 Getting ready for residual connections 12:56
Lecture 311 Model 8 Ensemble model overview 4:44
Lecture 312 Model 8 Building, compiling and fitting an ensemble of models 20:4
Lecture 313 Getting the upper and lower bounds of our prediction intervals 7:57
Lecture 314 Plotting the prediction intervals of our ensemble model prediction 13:3
Lecture 315 Model 9 Creating a function to make forecasts into the future
Lecture 316 Model 9 Plotting our model's future forecasts 13:9
Lecture 317 Model 10 Introducing the turkey problem and making data for it 14:15
Lecture 318 TensorFlow Time Series Fundamentals Challenge and Extra Resources Text

Section 13 : Passing the TensorFlow Developer Certificate Exam

 		Lecture 319 Get ready to be TensorFlow Developer Certified! Text
Lecture 320 What is the TensorFlow Developer Certification 5:29
Lecture 321 Why the TensorFlow Developer Certification 6:57
Lecture 322 How to prepare (your brain) for the TensorFlow Developer Certification 8:14
Lecture 323 How to prepare (your computer) for the TensorFlow Developer Certification 12:43
Lecture 324 What to do after the TensorFlow Developer Certification exam 2:14

Section 14 : Where To Go From Here

 		Lecture 325 Become An Alumni Text
Lecture 326 About Proctor Testing Pdf
Lecture 327 About Certification Pdf

Section 15 : Appendix Machine Learning Primer

 		Lecture 328 Quick Note Upcoming Videos Text
Lecture 329 What is Machine Learning 6:52
Lecture 330 AIMachine LearningData Science 4:51
Lecture 331 Exercise Machine Learning Playground 6:16
Lecture 332 How Did We Get Here 6:3
Lecture 333 Exercise YouTube Recommendation Engine 4:25
Lecture 334 Types of Machine Learning 4:41
Lecture 335 Are You Getting It Yet Text
Lecture 336 What Is Machine Learning Round 2 4:45
Lecture 337 Section Review 1:48

Section 16 : Appendix Machine Learning and Data Science Framework

 		Lecture 338 Quick Note Upcoming Videos Text
Lecture 339 Section Overview 3:9
Lecture 340 Introducing Our Framework 2:38
Lecture 341 6 Step Machine Learning Framework 4:59
Lecture 342 Types of Machine Learning Problems 10:32
Lecture 343 Types of Data 4:51
Lecture 344 Types of Evaluation 3:31
Lecture 345 Features In Data 5:22
Lecture 346 Modelling - Splitting Data 5:58
Lecture 347 Modelling - Picking the Model 4:35
Lecture 348 Modelling - Tuning 3:17
Lecture 349 Modelling - Comparison 9:32
Lecture 350 Overfitting and Underfitting Definitions Text
Lecture 351 Experimentation 3:35
Lecture 352 Tools We Will Use 4:0
Lecture 353 Optional Elements of AI Text

Section 17 : Appendix Pandas for Data Analysis

 		Lecture 354 Quick Note Upcoming Videos Text
Lecture 355 Section Overview 2:27
Lecture 356 Downloading Workbooks and Assignments Text
Lecture 357 Pandas Introduction 4:29
Lecture 358 Series, Data Frames and CSVs 13:21
Lecture 359 Data from URLs Text
Lecture 360 Describing Data with Pandas 9:49
Lecture 361 Selecting and Viewing Data with Pandas 11:8
Lecture 362 Selecting and Viewing Data with Pandas Part 2 13:7
Lecture 363 Manipulating Data 13:57
Lecture 364 Manipulating Data 2 9:57
Lecture 365 Manipulating Data 3 10:12
Lecture 366 Assignment Pandas Practice Text
Lecture 367 How To Download The Course Assignments 7:43

Section 18 : Appendix NumPy

 		Lecture 368 Quick Note Upcoming Videos Text
Lecture 369 Section Overview 2:41
Lecture 370 NumPy Introduction 5:18
Lecture 371 Quick Note Correction In Next Video Text
Lecture 372 NumPy DataTypes and Attributes 14:6
Lecture 373 Creating NumPy Arrays 9:22
Lecture 374 NumPy Random Seed 7:17
Lecture 375 Viewing Arrays and Matrices 9:35
Lecture 376 Manipulating Arrays 11:32
Lecture 377 Manipulating Arrays 2 9:44
Lecture 378 Standard Deviation and Variance 7:10
Lecture 379 mp4 7:27
Lecture 380 Dot Product vs Element Wise 11:45
Lecture 381 Exercise Nut Butter Store Sales 13:4
Lecture 382 Comparison Operators 3:34
Lecture 383 Sorting Arrays 6:20
Lecture 384 Turn Images Into NumPy Arrays 7:37
Lecture 385 Assignment NumPy Practice Text
Lecture 386 Optional Extra NumPy resources Text