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[GigaCourse.Com] Udemy - TensorFlow Developer Certificate in 2021 Zero to Mastery

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种子名称： [GigaCourse.Com] Udemy - TensorFlow Developer Certificate in 2021 Zero to Mastery
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收录时间： 2021-11-4 03:24
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GigaCourse Com Udemy - TensorFlow Developer Certificate in 2021 Zero to Mastery

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[GigaCourse.Com] Udemy - TensorFlow Developer Certificate in 2021 Zero to Mastery.torrent
  • 01 Introduction/001 Course Outline.mp458.04MB
  • 02 Deep Learning and TensorFlow Fundamentals/001 What is deep learning_.mp434.17MB
  • 02 Deep Learning and TensorFlow Fundamentals/002 Why use deep learning_.mp462.32MB
  • 02 Deep Learning and TensorFlow Fundamentals/003 What are neural networks_.mp463.38MB
  • 02 Deep Learning and TensorFlow Fundamentals/004 What is deep learning already being used for_.mp476.29MB
  • 02 Deep Learning and TensorFlow Fundamentals/005 What is and why use TensorFlow_.mp469.18MB
  • 02 Deep Learning and TensorFlow Fundamentals/006 What is a Tensor_.mp427.55MB
  • 02 Deep Learning and TensorFlow Fundamentals/007 What we're going to cover throughout the course.mp429.38MB
  • 02 Deep Learning and TensorFlow Fundamentals/008 How to approach this course.mp426.12MB
  • 02 Deep Learning and TensorFlow Fundamentals/010 Creating your first tensors with TensorFlow and tf.constant().mp4134.73MB
  • 02 Deep Learning and TensorFlow Fundamentals/011 Creating tensors with TensorFlow and tf.Variable().mp470.89MB
  • 02 Deep Learning and TensorFlow Fundamentals/012 Creating random tensors with TensorFlow.mp488.38MB
  • 02 Deep Learning and TensorFlow Fundamentals/013 Shuffling the order of tensors.mp490.01MB
  • 02 Deep Learning and TensorFlow Fundamentals/014 Creating tensors from NumPy arrays.mp4101.35MB
  • 02 Deep Learning and TensorFlow Fundamentals/015 Getting information from your tensors (tensor attributes).mp487.48MB
  • 02 Deep Learning and TensorFlow Fundamentals/016 Indexing and expanding tensors.mp486.42MB
  • 02 Deep Learning and TensorFlow Fundamentals/017 Manipulating tensors with basic operations.mp445.03MB
  • 02 Deep Learning and TensorFlow Fundamentals/018 Matrix multiplication with tensors part 1.mp4100.89MB
  • 02 Deep Learning and TensorFlow Fundamentals/019 Matrix multiplication with tensors part 2.mp4107.78MB
  • 02 Deep Learning and TensorFlow Fundamentals/020 Matrix multiplication with tensors part 3.mp480.63MB
  • 02 Deep Learning and TensorFlow Fundamentals/021 Changing the datatype of tensors.mp471.32MB
  • 02 Deep Learning and TensorFlow Fundamentals/022 Tensor aggregation (finding the min, max, mean & more).mp489.69MB
  • 02 Deep Learning and TensorFlow Fundamentals/023 Tensor troubleshooting example (updating tensor datatypes).mp469.39MB
  • 02 Deep Learning and TensorFlow Fundamentals/024 Finding the positional minimum and maximum of a tensor (argmin and argmax).mp496.41MB
  • 02 Deep Learning and TensorFlow Fundamentals/025 Squeezing a tensor (removing all 1-dimension axes).mp430.14MB
  • 02 Deep Learning and TensorFlow Fundamentals/026 One-hot encoding tensors.mp459.72MB
  • 02 Deep Learning and TensorFlow Fundamentals/027 Trying out more tensor math operations.mp455.77MB
  • 02 Deep Learning and TensorFlow Fundamentals/028 Exploring TensorFlow and NumPy's compatibility.mp443.79MB
  • 02 Deep Learning and TensorFlow Fundamentals/029 Making sure our tensor operations run really fast on GPUs.mp4110.92MB
  • 03 Neural network regression with TensorFlow/001 Introduction to Neural Network Regression with TensorFlow.mp460.06MB
  • 03 Neural network regression with TensorFlow/002 Inputs and outputs of a neural network regression model.mp457.54MB
  • 03 Neural network regression with TensorFlow/003 Anatomy and architecture of a neural network regression model.mp458.96MB
  • 03 Neural network regression with TensorFlow/004 Creating sample regression data (so we can model it).mp490MB
  • 03 Neural network regression with TensorFlow/005 The major steps in modelling with TensorFlow.mp4181.9MB
  • 03 Neural network regression with TensorFlow/006 Steps in improving a model with TensorFlow part 1.mp445.89MB
  • 03 Neural network regression with TensorFlow/007 Steps in improving a model with TensorFlow part 2.mp490.26MB
  • 03 Neural network regression with TensorFlow/008 Steps in improving a model with TensorFlow part 3.mp4132.91MB
  • 03 Neural network regression with TensorFlow/009 Evaluating a TensorFlow model part 1 (_visualise, visualise, visualise_).mp466.96MB
  • 03 Neural network regression with TensorFlow/010 Evaluating a TensorFlow model part 2 (the three datasets).mp481.66MB
  • 03 Neural network regression with TensorFlow/011 Evaluating a TensorFlow model part 3 (getting a model summary).mp4192.99MB
  • 03 Neural network regression with TensorFlow/012 Evaluating a TensorFlow model part 4 (visualising a model's layers).mp470.2MB
  • 03 Neural network regression with TensorFlow/013 Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp478.81MB
  • 03 Neural network regression with TensorFlow/014 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp470.36MB
  • 03 Neural network regression with TensorFlow/015 Evaluating a TensorFlow regression model part 7 (mean absolute error).mp456.04MB
  • 03 Neural network regression with TensorFlow/016 Evaluating a TensorFlow regression model part 7 (mean square error).mp432.31MB
  • 03 Neural network regression with TensorFlow/017 Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4127.3MB
  • 03 Neural network regression with TensorFlow/018 Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp495.51MB
  • 03 Neural network regression with TensorFlow/019 Comparing and tracking your TensorFlow modelling experiments.mp492.23MB
  • 03 Neural network regression with TensorFlow/020 How to save a TensorFlow model.mp492.18MB
  • 03 Neural network regression with TensorFlow/021 How to load and use a saved TensorFlow model.mp4104.35MB
  • 03 Neural network regression with TensorFlow/022 (Optional) How to save and download files from Google Colab.mp467.68MB
  • 03 Neural network regression with TensorFlow/023 Putting together what we've learned part 1 (preparing a dataset).mp4143.6MB
  • 03 Neural network regression with TensorFlow/024 Putting together what we've learned part 2 (building a regression model).mp4121.1MB
  • 03 Neural network regression with TensorFlow/025 Putting together what we've learned part 3 (improving our regression model).mp4154.87MB
  • 03 Neural network regression with TensorFlow/026 Preprocessing data with feature scaling part 1 (what is feature scaling_).mp492.51MB
  • 03 Neural network regression with TensorFlow/027 Preprocessing data with feature scaling part 2 (normalising our data).mp497.1MB
  • 03 Neural network regression with TensorFlow/028 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp475.57MB
  • 04 Neural network classification in TensorFlow/001 Introduction to neural network classification in TensorFlow.mp472.76MB
  • 04 Neural network classification in TensorFlow/002 Example classification problems (and their inputs and outputs).mp450.64MB
  • 04 Neural network classification in TensorFlow/003 Input and output tensors of classification problems.mp451.01MB
  • 04 Neural network classification in TensorFlow/004 Typical architecture of neural network classification models with TensorFlow.mp4112.64MB
  • 04 Neural network classification in TensorFlow/005 Creating and viewing classification data to model.mp4106.12MB
  • 04 Neural network classification in TensorFlow/006 Checking the input and output shapes of our classification data.mp438.07MB
  • 04 Neural network classification in TensorFlow/007 Building a not very good classification model with TensorFlow.mp4125.48MB
  • 04 Neural network classification in TensorFlow/008 Trying to improve our not very good classification model.mp484.44MB
  • 04 Neural network classification in TensorFlow/009 Creating a function to view our model's not so good predictions.mp4160.62MB
  • 04 Neural network classification in TensorFlow/010 Make our poor classification model work for a regression dataset.mp4122.9MB
  • 04 Neural network classification in TensorFlow/011 Non-linearity part 1_ Straight lines and non-straight lines.mp495.72MB
  • 04 Neural network classification in TensorFlow/012 Non-linearity part 2_ Building our first neural network with non-linearity.mp458.96MB
  • 04 Neural network classification in TensorFlow/013 Non-linearity part 3_ Upgrading our non-linear model with more layers.mp4123.07MB
  • 04 Neural network classification in TensorFlow/014 Non-linearity part 4_ Modelling our non-linear data once and for all.mp496.56MB
  • 04 Neural network classification in TensorFlow/015 Non-linearity part 5_ Replicating non-linear activation functions from scratch.mp4146.29MB
  • 04 Neural network classification in TensorFlow/016 Getting great results in less time by tweaking the learning rate.mp4136.85MB
  • 04 Neural network classification in TensorFlow/017 Using the TensorFlow History object to plot a model's loss curves.mp462.06MB
  • 04 Neural network classification in TensorFlow/018 Using callbacks to find a model's ideal learning rate.mp4155.68MB
  • 04 Neural network classification in TensorFlow/019 Training and evaluating a model with an ideal learning rate.mp488.86MB
  • 04 Neural network classification in TensorFlow/020 Introducing more classification evaluation methods.mp442.2MB
  • 04 Neural network classification in TensorFlow/021 Finding the accuracy of our classification model.mp434MB
  • 04 Neural network classification in TensorFlow/022 Creating our first confusion matrix (to see where our model is getting confused).mp465.7MB
  • 04 Neural network classification in TensorFlow/023 Making our confusion matrix prettier.mp4114.07MB
  • 04 Neural network classification in TensorFlow/024 Putting things together with multi-class classification part 1_ Getting the data.mp487.19MB
  • 04 Neural network classification in TensorFlow/025 Multi-class classification part 2_ Becoming one with the data.mp448.52MB
  • 04 Neural network classification in TensorFlow/026 Multi-class classification part 3_ Building a multi-class classification model.mp4142.39MB
  • 04 Neural network classification in TensorFlow/027 Multi-class classification part 4_ Improving performance with normalisation.mp4113.34MB
  • 04 Neural network classification in TensorFlow/028 Multi-class classification part 5_ Comparing normalised and non-normalised data.mp426.77MB
  • 04 Neural network classification in TensorFlow/029 Multi-class classification part 6_ Finding the ideal learning rate.mp473.34MB
  • 04 Neural network classification in TensorFlow/030 Multi-class classification part 7_ Evaluating our model.mp4119.04MB
  • 04 Neural network classification in TensorFlow/031 Multi-class classification part 8_ Creating a confusion matrix.mp440.49MB
  • 04 Neural network classification in TensorFlow/032 Multi-class classification part 9_ Visualising random model predictions.mp465.58MB
  • 04 Neural network classification in TensorFlow/033 What _patterns_ is our model learning_.mp4127.85MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/001 Introduction to Computer Vision with TensorFlow.mp475.13MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp476.63MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/003 Downloading an image dataset for our first Food Vision model.mp472.98MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/004 Becoming One With Data.mp445.61MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/005 Becoming One With Data Part 2.mp4104.59MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/006 Becoming One With Data Part 3.mp439.77MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/007 Building an end to end CNN Model.mp4155.25MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/008 Using a GPU to run our CNN model 5x faster.mp4114.96MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/009 Trying a non-CNN model on our image data.mp4100.57MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/010 Improving our non-CNN model by adding more layers.mp4106.43MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/011 Breaking our CNN model down part 1_ Becoming one with the data.mp490.98MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/012 Breaking our CNN model down part 2_ Preparing to load our data.mp4109.5MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/013 Breaking our CNN model down part 3_ Loading our data with ImageDataGenerator.mp4103.57MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/014 Breaking our CNN model down part 4_ Building a baseline CNN model.mp485.13MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/015 Breaking our CNN model down part 5_ Looking inside a Conv2D layer.mp4185.98MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/016 Breaking our CNN model down part 6_ Compiling and fitting our baseline CNN.mp477.09MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/017 Breaking our CNN model down part 7_ Evaluating our CNN's training curves.mp4106.2MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/018 Breaking our CNN model down part 8_ Reducing overfitting with Max Pooling.mp4130.5MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/019 Breaking our CNN model down part 9_ Reducing overfitting with data augmentation.mp466.17MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/020 Breaking our CNN model down part 10_ Visualizing our augmented data.mp4157.57MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/021 Breaking our CNN model down part 11_ Training a CNN model on augmented data.mp494.24MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/022 Breaking our CNN model down part 12_ Discovering the power of shuffling data.mp4103.71MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/023 Breaking our CNN model down part 13_ Exploring options to improve our model.mp450.31MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/024 Downloading a custom image to make predictions on.mp453.1MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/025 Writing a helper function to load and preprocessing custom images.mp4105.11MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/026 Making a prediction on a custom image with our trained CNN.mp499.8MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/027 Multi-class CNN's part 1_ Becoming one with the data.mp4140.18MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/028 Multi-class CNN's part 2_ Preparing our data (turning it into tensors).mp472.82MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/029 Multi-class CNN's part 3_ Building a multi-class CNN model.mp489.33MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/030 Multi-class CNN's part 4_ Fitting a multi-class CNN model to the data.mp459.68MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/031 Multi-class CNN's part 5_ Evaluating our multi-class CNN model.mp441.1MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/032 Multi-class CNN's part 6_ Trying to fix overfitting by removing layers.mp4129.83MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/033 Multi-class CNN's part 7_ Trying to fix overfitting with data augmentation.mp4121.02MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/034 Multi-class CNN's part 8_ Things you could do to improve your CNN model.mp443.31MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/035 Multi-class CNN's part 9_ Making predictions with our model on custom images.mp4119.07MB
  • 05 Computer Vision and Convolutional Neural Networks in TensorFlow/036 Saving and loading our trained CNN model.mp469.36MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/001 What is and why use transfer learning_.mp465.82MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/002 Downloading and preparing data for our first transfer learning model.mp4132.67MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/003 Introducing Callbacks in TensorFlow and making a callback to track our models.mp494.64MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/004 Exploring the TensorFlow Hub website for pretrained models.mp4102.9MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/005 Building and compiling a TensorFlow Hub feature extraction model.mp4135.64MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/006 Blowing our previous models out of the water with transfer learning.mp499.68MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/007 Plotting the loss curves of our ResNet feature extraction model.mp462.03MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/008 Building and training a pre-trained EfficientNet model on our data.mp4105.94MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/009 Different Types of Transfer Learning.mp4110.65MB
  • 06 Transfer Learning in TensorFlow Part 1_ Feature extraction/010 Comparing Our Model's Results.mp4144.01MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/001 Introduction to Transfer Learning in TensorFlow Part 2_ Fine-tuning.mp461.44MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/002 Importing a script full of helper functions (and saving lots of space).mp489.3MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/003 Downloading and turning our images into a TensorFlow BatchDataset.mp4173.3MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/004 Discussing the four (actually five) modelling experiments we're running.mp415.85MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/005 Comparing the TensorFlow Keras Sequential API versus the Functional API.mp426.45MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/006 Creating our first model with the TensorFlow Keras Functional API.mp4132.17MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/007 Compiling and fitting our first Functional API model.mp4132.78MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/008 Getting a feature vector from our trained model.mp4147.57MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/009 Drilling into the concept of a feature vector (a learned representation).mp451.49MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/010 Downloading and preparing the data for Model 1 (1 percent of training data).mp497.66MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/011 Building a data augmentation layer to use inside our model.mp4117.66MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/012 Visualising what happens when images pass through our data augmentation layer.mp4119.23MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/013 Building Model 1 (with a data augmentation layer and 1% of training data).mp4153.03MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/014 Building Model 2 (with a data augmentation layer and 10% of training data).mp4159.74MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/015 Creating a ModelCheckpoint to save our model's weights during training.mp468.9MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/016 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp468.3MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/017 Loading and comparing saved weights to our existing trained Model 2.mp462.53MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/018 Preparing Model 3 (our first fine-tuned model).mp4198.29MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/019 Fitting and evaluating Model 3 (our first fine-tuned model).mp469.17MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/020 Comparing our model's results before and after fine-tuning.mp484.12MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/021 Downloading and preparing data for our biggest experiment yet (Model 4).mp456.72MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/022 Preparing our final modelling experiment (Model 4).mp496.42MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/023 Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp496.94MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/024 Comparing our modelling experiment results in TensorBoard.mp495.65MB
  • 07 Transfer Learning in TensorFlow Part 2_ Fine tuning/025 How to view and delete previous TensorBoard experiments.mp421.89MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/001 Introduction to Transfer Learning Part 3_ Scaling Up.mp441.49MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/002 Getting helper functions ready and downloading data to model.mp4131.37MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/003 Outlining the model we're going to build and building a ModelCheckpoint callback.mp440.63MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/004 Creating a data augmentation layer to use with our model.mp440.54MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/005 Creating a headless EfficientNetB0 model with data augmentation built in.mp480.37MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/006 Fitting and evaluating our biggest transfer learning model yet.mp470.12MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/007 Unfreezing some layers in our base model to prepare for fine-tuning.mp4100MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/008 Fine-tuning our feature extraction model and evaluating its performance.mp466.21MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/009 Saving and loading our trained model.mp457.38MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/010 Downloading a pretrained model to make and evaluate predictions with.mp478.68MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/011 Making predictions with our trained model on 25,250 test samples.mp4115.41MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/012 Unravelling our test dataset for comparing ground truth labels to predictions.mp443.75MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/013 Confirming our model's predictions are in the same order as the test labels.mp450.58MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/014 Creating a confusion matrix for our model's 101 different classes.mp4156.96MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/015 Evaluating every individual class in our dataset.mp4131.91MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/016 Plotting our model's F1-scores for each separate class.mp477.88MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/017 Creating a function to load and prepare images for making predictions.mp4109.47MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/018 Making predictions on our test images and evaluating them.mp4171.65MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/019 Discussing the benefits of finding your model's most wrong predictions.mp459.21MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/020 Writing code to uncover our model's most wrong predictions.mp4109.74MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/021 Plotting and visualising the samples our model got most wrong.mp4125.49MB
  • 08 Transfer Learning with TensorFlow Part 3_ Scaling Up/022 Making predictions on and plotting our own custom images.mp4108.11MB
  • 09 Milestone Project 1_ Food Vision Big™/001 Introduction to Milestone Project 1_ Food Vision Big™.mp442.27MB
  • 09 Milestone Project 1_ Food Vision Big™/002 Making sure we have access to the right GPU for mixed precision training.mp488.15MB
  • 09 Milestone Project 1_ Food Vision Big™/003 Getting helper functions ready.mp431.2MB
  • 09 Milestone Project 1_ Food Vision Big™/004 Introduction to TensorFlow Datasets (TFDS).mp4116.85MB
  • 09 Milestone Project 1_ Food Vision Big™/005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4116.67MB
  • 09 Milestone Project 1_ Food Vision Big™/006 Creating a preprocessing function to prepare our data for modelling.mp4132.12MB
  • 09 Milestone Project 1_ Food Vision Big™/007 Batching and preparing our datasets (to make them run fast).mp4132.16MB
  • 09 Milestone Project 1_ Food Vision Big™/008 Exploring what happens when we batch and prefetch our data.mp463.87MB
  • 09 Milestone Project 1_ Food Vision Big™/009 Creating modelling callbacks for our feature extraction model.mp460.56MB
  • 09 Milestone Project 1_ Food Vision Big™/011 Turning on mixed precision training with TensorFlow.mp4107.63MB
  • 09 Milestone Project 1_ Food Vision Big™/012 Creating a feature extraction model capable of using mixed precision training.mp4107.93MB
  • 09 Milestone Project 1_ Food Vision Big™/013 Checking to see if our model is using mixed precision training layer by layer.mp487.69MB
  • 09 Milestone Project 1_ Food Vision Big™/014 Training and evaluating a feature extraction model (Food Vision Big™).mp489.16MB
  • 09 Milestone Project 1_ Food Vision Big™/015 Introducing your Milestone Project 1 challenge_ build a model to beat DeepFood.mp489.17MB
  • 10 NLP Fundamentals in TensorFlow/002 Introduction to Natural Language Processing (NLP) and Sequence Problems.mp4124.04MB
  • 10 NLP Fundamentals in TensorFlow/003 Example NLP inputs and outputs.mp464.29MB
  • 10 NLP Fundamentals in TensorFlow/004 The typical architecture of a Recurrent Neural Network (RNN).mp4107.22MB
  • 10 NLP Fundamentals in TensorFlow/005 Preparing a notebook for our first NLP with TensorFlow project.mp482.45MB
  • 10 NLP Fundamentals in TensorFlow/006 Becoming one with the data and visualising a text dataset.mp4160.37MB
  • 10 NLP Fundamentals in TensorFlow/007 Splitting data into training and validation sets.mp459.92MB
  • 10 NLP Fundamentals in TensorFlow/008 Converting text data to numbers using tokenisation and embeddings (overview).mp482.08MB
  • 10 NLP Fundamentals in TensorFlow/009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.mp4200.08MB
  • 10 NLP Fundamentals in TensorFlow/010 Mapping the TextVectorization layer to text data and turning it into numbers.mp497.91MB
  • 10 NLP Fundamentals in TensorFlow/011 Creating an Embedding layer to turn tokenised text into embedding vectors.mp4135.51MB
  • 10 NLP Fundamentals in TensorFlow/012 Discussing the various modelling experiments we're going to run.mp487.68MB
  • 10 NLP Fundamentals in TensorFlow/013 Model 0_ Building a baseline model to try and improve upon.mp493.22MB
  • 10 NLP Fundamentals in TensorFlow/014 Creating a function to track and evaluate our model's results.mp4148.75MB
  • 10 NLP Fundamentals in TensorFlow/015 Model 1_ Building, fitting and evaluating our first deep model on text data.mp4207.79MB
  • 10 NLP Fundamentals in TensorFlow/016 Visualising our model's learned word embeddings with TensorFlow's projector tool.mp4283.12MB
  • 10 NLP Fundamentals in TensorFlow/017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.mp496.53MB
  • 10 NLP Fundamentals in TensorFlow/018 Model 2_ Building, fitting and evaluating our first TensorFlow RNN model (LSTM).mp4165.86MB
  • 10 NLP Fundamentals in TensorFlow/019 Model 3_ Building, fitting and evaluating a GRU-cell powered RNN.mp4168.14MB
  • 10 NLP Fundamentals in TensorFlow/020 Model 4_ Building, fitting and evaluating a bidirectional RNN model.mp4167.17MB
  • 10 NLP Fundamentals in TensorFlow/021 Discussing the intuition behind Conv1D neural networks for text and sequences.mp4184.34MB
  • 10 NLP Fundamentals in TensorFlow/022 Model 5_ Building, fitting and evaluating a 1D CNN for text.mp477.77MB
  • 10 NLP Fundamentals in TensorFlow/023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).mp4138.12MB
  • 10 NLP Fundamentals in TensorFlow/024 Model 6_ Building, training and evaluating a transfer learning model for NLP.mp499.2MB
  • 10 NLP Fundamentals in TensorFlow/025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).mp491.56MB
  • 10 NLP Fundamentals in TensorFlow/026 Model 7_ Building, training and evaluating a transfer learning model on 10% data.mp4100.92MB
  • 10 NLP Fundamentals in TensorFlow/027 Fixing our data leakage issue with model 7 and retraining it.mp4165.89MB
  • 10 NLP Fundamentals in TensorFlow/028 Comparing all our modelling experiments evaluation metrics.mp4115.91MB
  • 10 NLP Fundamentals in TensorFlow/029 Uploading our model's training logs to TensorBoard and comparing them.mp4109.2MB
  • 10 NLP Fundamentals in TensorFlow/030 Saving and loading in a trained NLP model with TensorFlow.mp4104.82MB
  • 10 NLP Fundamentals in TensorFlow/031 Downloading a pretrained model and preparing data to investigate predictions.mp4131.09MB
  • 10 NLP Fundamentals in TensorFlow/032 Visualising our model's most wrong predictions.mp477.04MB
  • 10 NLP Fundamentals in TensorFlow/033 Making and visualising predictions on the test dataset.mp476.66MB
  • 10 NLP Fundamentals in TensorFlow/034 Understanding the concept of the speed_score tradeoff.mp4130.52MB
  • 11 Milestone Project 2_ SkimLit/001 Introduction to Milestone Project 2_ SkimLit.mp4148.47MB
  • 11 Milestone Project 2_ SkimLit/002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).mp471.04MB
  • 11 Milestone Project 2_ SkimLit/003 SkimLit inputs and outputs.mp476.76MB
  • 11 Milestone Project 2_ SkimLit/004 Setting up our notebook for Milestone Project 2 (getting the data).mp4146.21MB
  • 11 Milestone Project 2_ SkimLit/005 Visualising examples from the dataset (becoming one with the data).mp4132.57MB
  • 11 Milestone Project 2_ SkimLit/006 Writing a preprocessing function to structure our data for modelling.mp4217.99MB
  • 11 Milestone Project 2_ SkimLit/007 Performing visual data analysis on our preprocessed text.mp474.35MB
  • 11 Milestone Project 2_ SkimLit/008 Turning our target labels into numbers (ML models require numbers).mp4117.35MB
  • 11 Milestone Project 2_ SkimLit/009 Model 0_ Creating, fitting and evaluating a baseline model for SkimLit.mp481.5MB
  • 11 Milestone Project 2_ SkimLit/010 Preparing our data for deep sequence models.mp485.2MB
  • 11 Milestone Project 2_ SkimLit/011 Creating a text vectoriser to map our tokens (text) to numbers.mp4129.9MB
  • 11 Milestone Project 2_ SkimLit/012 Creating a custom token embedding layer with TensorFlow.mp499.65MB
  • 11 Milestone Project 2_ SkimLit/013 Creating fast loading dataset with the TensorFlow tf.data API.mp490.71MB
  • 11 Milestone Project 2_ SkimLit/014 Model 1_ Building, fitting and evaluating a Conv1D with token embeddings.mp4168.35MB
  • 11 Milestone Project 2_ SkimLit/015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.mp4124.66MB
  • 11 Milestone Project 2_ SkimLit/016 Model 2_ Building, fitting and evaluating a Conv1D model with token embeddings.mp4106.75MB
  • 11 Milestone Project 2_ SkimLit/017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.mp4197.33MB
  • 11 Milestone Project 2_ SkimLit/018 Creating a character-level embedding layer with tf.keras.layers.Embedding.mp477.56MB
  • 11 Milestone Project 2_ SkimLit/019 Model 3_ Building, fitting and evaluating a Conv1D model on character embeddings.mp4131.1MB
  • 11 Milestone Project 2_ SkimLit/020 Discussing how we're going to build Model 4 (character + token embeddings).mp460.3MB
  • 11 Milestone Project 2_ SkimLit/021 Model 4_ Building a multi-input model (hybrid token + character embeddings).mp4181.61MB
  • 11 Milestone Project 2_ SkimLit/022 Model 4_ Plotting and visually exploring different data inputs.mp486.67MB
  • 11 Milestone Project 2_ SkimLit/023 Crafting multi-input fast loading tf.data datasets for Model 4.mp483.82MB
  • 11 Milestone Project 2_ SkimLit/024 Model 4_ Building, fitting and evaluating a hybrid embedding model.mp4139MB
  • 11 Milestone Project 2_ SkimLit/025 Model 5_ Adding positional embeddings via feature engineering (overview).mp466.18MB
  • 11 Milestone Project 2_ SkimLit/026 Encoding the line number feature to used with Model 5.mp4112.93MB
  • 11 Milestone Project 2_ SkimLit/027 Encoding the total lines feature to be used with Model 5.mp464.34MB
  • 11 Milestone Project 2_ SkimLit/028 Model 5_ Building the foundations of a tribrid embedding model.mp481.79MB
  • 11 Milestone Project 2_ SkimLit/029 Model 5_ Completing the build of a tribrid embedding model for sequences.mp4152.92MB
  • 11 Milestone Project 2_ SkimLit/030 Visually inspecting the architecture of our tribrid embedding model.mp4107.86MB
  • 11 Milestone Project 2_ SkimLit/031 Creating multi-level data input pipelines for Model 5 with the tf.data API.mp499.34MB
  • 11 Milestone Project 2_ SkimLit/032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).mp4115.62MB
  • 11 Milestone Project 2_ SkimLit/033 Comparing the performance of all of our modelling experiments.mp477.89MB
  • 11 Milestone Project 2_ SkimLit/034 Saving, loading & testing our best performing model.mp483.51MB
  • 11 Milestone Project 2_ SkimLit/035 Congratulations and your challenge before heading to the next module.mp4135.78MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.mp436.36MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/003 What is a time series problem and example forecasting problems at Uber.mp465.11MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/004 Example forecasting problems in daily life.mp427.8MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/005 What can be forecast_.mp477.87MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/006 What we're going to cover (broadly).mp425.61MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/007 Time series forecasting inputs and outputs.mp461.95MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/008 Downloading and inspecting our Bitcoin historical dataset.mp4148.81MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/009 Different kinds of time series patterns & different amounts of feature variables.mp468.12MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/010 Visualizing our Bitcoin historical data with pandas.mp441.84MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/011 Reading in our Bitcoin data with Python's CSV module.mp4102.67MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/012 Creating train and test splits for time series (the wrong way).mp463.59MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/013 Creating train and test splits for time series (the right way).mp448.66MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/014 Creating a plotting function to visualize our time series data.mp468.17MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/015 Discussing the various modelling experiments were going to be running.mp478.21MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/016 Model 0_ Making and visualizing a naive forecast model.mp4114.28MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/017 Discussing some of the most common time series evaluation metrics.mp498.85MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/018 Implementing MASE with TensorFlow.mp4102.03MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/019 Creating a function to evaluate our model's forecasts with various metrics.mp492.24MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/020 Discussing other non-TensorFlow kinds of time series forecasting models.mp459.39MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/021 Formatting data Part 2_ Creating a function to label our windowed time series.mp4109.23MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/022 Discussing the use of windows and horizons in time series data.mp470.16MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/023 Writing a preprocessing function to turn time series data into windows & labels.mp4250.43MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/024 Turning our windowed time series data into training and test sets.mp490.56MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/025 Creating a modelling checkpoint callback to save our best performing model.mp464.88MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/026 Model 1_ Building, compiling and fitting a deep learning model on Bitcoin data.mp4167.32MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/027 Creating a function to make predictions with our trained models.mp4122.06MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/028 Model 2_ Building, fitting and evaluating a deep model with a larger window size.mp4153.87MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/029 Model 3_ Building, fitting and evaluating a model with a larger horizon size.mp4122.66MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/030 Adjusting the evaluation function to work for predictions with larger horizons.mp489.25MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/031 Model 3_ Visualizing the results.mp486.71MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/032 Comparing our modelling experiments so far and discussing autocorrelation.mp492.91MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/033 Preparing data for building a Conv1D model.mp4112.61MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/034 Model 4_ Building, fitting and evaluating a Conv1D model on our Bitcoin data.mp4144.63MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/035 Model 5_ Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.mp4165.99MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/036 Investigating how to turn our univariate time series into multivariate.mp4120.69MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/037 Creating and plotting a multivariate time series with BTC price and block reward.mp4111.58MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/038 Preparing our multivariate time series for a model.mp4117.31MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/039 Model 6_ Building, fitting and evaluating a multivariate time series model.mp481.62MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/040 Model 7_ Discussing what we're going to be doing with the N-BEATS algorithm.mp4104.17MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/041 Model 7_ Replicating the N-BEATS basic block with TensorFlow layer subclassing.mp4216.3MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/042 Model 7_ Testing our N-BEATS block implementation with dummy data inputs.mp4182.99MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/043 Model 7_ Creating a performant data pipeline for the N-BEATS model with tf.data.mp4122.49MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/044 Model 7_ Setting up hyperparameters for the N-BEATS algorithm.mp4100.21MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/045 Model 7_ Getting ready for residual connections.mp4148.25MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/046 Model 7_ Outlining the steps we're going to take to build the N-BEATS model.mp4107.52MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/047 Model 7_ Putting together the pieces of the puzzle of the N-BEATS model.mp4237.41MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/048 Model 7_ Plotting the N-BEATS algorithm we've created and admiring its beauty.mp474.06MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/049 Model 8_ Ensemble model overview.mp443.5MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/050 Model 8_ Building, compiling and fitting an ensemble of models.mp4180.06MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/051 Model 8_ Making and evaluating predictions with our ensemble model.mp4182.18MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/052 Discussing the importance of prediction intervals in forecasting.mp4114.95MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/053 Getting the upper and lower bounds of our prediction intervals.mp481.54MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/054 Plotting the prediction intervals of our ensemble model predictions.mp4117.45MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/055 (Optional) Discussing the types of uncertainty in machine learning.mp4116.05MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/056 Model 9_ Preparing data to create a model capable of predicting into the future.mp475.29MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/057 Model 9_ Building, compiling and fitting a future predictions model.mp447.66MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/058 Model 9_ Discussing what's required for our model to make future predictions.mp464.15MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/059 Model 9_ Creating a function to make forecasts into the future.mp4119.52MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/060 Model 9_ Plotting our model's future forecasts.mp4106.21MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/061 Model 10_ Introducing the turkey problem and making data for it.mp4108.19MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/062 Model 10_ Building a model to predict on turkey data (why forecasting is BS).mp4111.56MB
  • 12 Time Series fundamentals in TensorFlow + Milestone Project 3_ BitPredict/063 Comparing the results of all of our models and discussing where to go next.mp4109.49MB
  • 13 Passing the TensorFlow Developer Certificate Exam/002 What is the TensorFlow Developer Certification_.mp454.85MB
  • 13 Passing the TensorFlow Developer Certificate Exam/003 Why the TensorFlow Developer Certification_.mp451.54MB
  • 13 Passing the TensorFlow Developer Certificate Exam/004 How to prepare (your brain) for the TensorFlow Developer Certification.mp498.2MB
  • 13 Passing the TensorFlow Developer Certificate Exam/005 How to prepare (your computer) for the TensorFlow Developer Certification.mp4140.42MB
  • 13 Passing the TensorFlow Developer Certificate Exam/006 What to do after the TensorFlow Developer Certification exam.mp417.03MB
  • 15 Appendix_ Machine Learning Primer/002 What is Machine Learning_.mp428.3MB
  • 15 Appendix_ Machine Learning Primer/003 AI_Machine Learning_Data Science.mp419.65MB
  • 15 Appendix_ Machine Learning Primer/004 Exercise_ Machine Learning Playground.mp442.55MB
  • 15 Appendix_ Machine Learning Primer/005 How Did We Get Here_.mp430.5MB
  • 15 Appendix_ Machine Learning Primer/006 Exercise_ YouTube Recommendation Engine.mp419.44MB
  • 15 Appendix_ Machine Learning Primer/007 Types of Machine Learning.mp422.78MB
  • 15 Appendix_ Machine Learning Primer/009 What Is Machine Learning_ Round 2.mp425.47MB
  • 15 Appendix_ Machine Learning Primer/010 Section Review.mp45.56MB
  • 16 Appendix_ Machine Learning and Data Science Framework/002 Section Overview.mp413.29MB
  • 16 Appendix_ Machine Learning and Data Science Framework/003 Introducing Our Framework.mp411.37MB
  • 16 Appendix_ Machine Learning and Data Science Framework/004 6 Step Machine Learning Framework.mp423.44MB
  • 16 Appendix_ Machine Learning and Data Science Framework/005 Types of Machine Learning Problems.mp460.45MB
  • 16 Appendix_ Machine Learning and Data Science Framework/006 Types of Data.mp429.28MB
  • 16 Appendix_ Machine Learning and Data Science Framework/007 Types of Evaluation.mp417.73MB
  • 16 Appendix_ Machine Learning and Data Science Framework/008 Features In Data.mp436.75MB
  • 16 Appendix_ Machine Learning and Data Science Framework/009 Modelling - Splitting Data.mp427.51MB
  • 16 Appendix_ Machine Learning and Data Science Framework/010 Modelling - Picking the Model.mp423.25MB
  • 16 Appendix_ Machine Learning and Data Science Framework/011 Modelling - Tuning.mp415.98MB
  • 16 Appendix_ Machine Learning and Data Science Framework/012 Modelling - Comparison.mp444.83MB
  • 16 Appendix_ Machine Learning and Data Science Framework/014 Experimentation.mp421.33MB
  • 16 Appendix_ Machine Learning and Data Science Framework/015 Tools We Will Use.mp427.33MB
  • 17 Appendix_ Pandas for Data Analysis/002 Section Overview.mp410.87MB
  • 17 Appendix_ Pandas for Data Analysis/004 Pandas Introduction.mp427.43MB
  • 17 Appendix_ Pandas for Data Analysis/005 Series, Data Frames and CSVs.mp495.35MB
  • 17 Appendix_ Pandas for Data Analysis/007 Describing Data with Pandas.mp475.61MB
  • 17 Appendix_ Pandas for Data Analysis/008 Selecting and Viewing Data with Pandas.mp472.28MB
  • 17 Appendix_ Pandas for Data Analysis/009 Selecting and Viewing Data with Pandas Part 2.mp4106.42MB
  • 17 Appendix_ Pandas for Data Analysis/010 Manipulating Data.mp4104.94MB
  • 17 Appendix_ Pandas for Data Analysis/011 Manipulating Data 2.mp486.29MB
  • 17 Appendix_ Pandas for Data Analysis/012 Manipulating Data 3.mp491.12MB
  • 17 Appendix_ Pandas for Data Analysis/014 How To Download The Course Assignments.mp466.68MB
  • 18 Appendix_ NumPy/002 Section Overview.mp413.34MB
  • 18 Appendix_ NumPy/003 NumPy Introduction.mp426.86MB
  • 18 Appendix_ NumPy/005 NumPy DataTypes and Attributes.mp478.83MB
  • 18 Appendix_ NumPy/006 Creating NumPy Arrays.mp466.82MB
  • 18 Appendix_ NumPy/007 NumPy Random Seed.mp451.97MB
  • 18 Appendix_ NumPy/008 Viewing Arrays and Matrices.mp470.59MB
  • 18 Appendix_ NumPy/009 Manipulating Arrays.mp480.54MB
  • 18 Appendix_ NumPy/010 Manipulating Arrays 2.mp467.85MB
  • 18 Appendix_ NumPy/011 Standard Deviation and Variance.mp451.16MB
  • 18 Appendix_ NumPy/012 Reshape and Transpose.mp453.52MB
  • 18 Appendix_ NumPy/013 Dot Product vs Element Wise.mp483.78MB
  • 18 Appendix_ NumPy/014 Exercise_ Nut Butter Store Sales.mp491.26MB
  • 18 Appendix_ NumPy/015 Comparison Operators.mp426.36MB
  • 18 Appendix_ NumPy/016 Sorting Arrays.mp432.8MB
  • 18 Appendix_ NumPy/017 Turn Images Into NumPy Arrays.mp485.89MB



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