Autoplay
Autocomplete
Previous Lesson
Complete and Continue
TensorFlow for Deep Learning Bootcamp: Zero to Mastery
Introduction
TensorFlow for Deep Learning: Zero to Mastery (1:38)
Course Outline (4:16)
Exercise: Meet Your Classmates and Instructor
All Course Resources + Notebooks
Python + Machine Learning Monthly
ZTM Plugin + Understanding Your Video Player
Set Your Learning Streak Goal
Deep Learning and TensorFlow Fundamentals
What is deep learning? (4:38)
Why use deep learning? (9:38)
What are neural networks? (10:26)
What is deep learning already being used for? (8:36)
What is and why use TensorFlow? (7:56)
What is a Tensor? (3:37)
What we're going to cover throughout the course (4:29)
How to approach this course (5:34)
Need A Refresher?
Creating your first tensors with TensorFlow and tf.constant() (18:45)
Creating tensors with TensorFlow and tf.Variable() (7:07)
Creating random tensors with TensorFlow (9:40)
Shuffling the order of tensors (9:40)
Creating tensors from NumPy arrays (11:55)
Getting information from your tensors (tensor attributes) (11:57)
Indexing and expanding tensors (12:33)
Manipulating tensors with basic operations (5:34)
Matrix multiplication with tensors part 1 (11:53)
Matrix multiplication with tensors part 2 (13:29)
Matrix multiplication with tensors part 3 (10:03)
Changing the datatype of tensors (6:55)
Tensor aggregation (finding the min, max, mean & more) (9:49)
Tensor troubleshooting example (updating tensor datatypes) (6:13)
Finding the positional minimum and maximum of a tensor (argmin and argmax) (9:31)
Squeezing a tensor (removing all 1-dimension axes) (2:59)
One-hot encoding tensors (5:46)
Trying out more tensor math operations (4:47)
Exploring TensorFlow and NumPy's compatibility (5:43)
Making sure our tensor operations run really fast on GPUs (10:19)
TensorFlow Fundamentals challenge, exercises & extra-curriculum
Let's Have Some Fun (+ Free Resources)
Neural network regression with TensorFlow
Introduction to Neural Network Regression with TensorFlow (7:33)
Inputs and outputs of a neural network regression model (8:59)
Anatomy and architecture of a neural network regression model (7:55)
Creating sample regression data (so we can model it) (12:46)
Note: Code update for upcoming lecture(s) for TensorFlow 2.7.0+ fix
Endorsements On LinkedIn
The major steps in modelling with TensorFlow (20:15)
Steps in improving a model with TensorFlow part 1 (6:02)
Steps in improving a model with TensorFlow part 2 (9:25)
Steps in improving a model with TensorFlow part 3 (12:33)
Evaluating a TensorFlow model part 1 ("visualise, visualise, visualise") (7:24)
Evaluating a TensorFlow model part 2 (the three datasets) (11:01)
Evaluating a TensorFlow model part 3 (getting a model summary) (17:18)
Evaluating a TensorFlow model part 4 (visualising a model's layers) (7:14)
Evaluating a TensorFlow model part 5 (visualising a model's predictions) (9:16)
Evaluating a TensorFlow model part 6 (common regression evaluation metrics) (8:05)
Evaluating a TensorFlow regression model part 7 (mean absolute error) (5:52)
Evaluating a TensorFlow regression model part 7 (mean square error) (3:18)
Setting up TensorFlow modelling experiments part 1 (start with a simple model) (13:50)
Setting up TensorFlow modelling experiments part 2 (increasing complexity) (11:29)
Comparing and tracking your TensorFlow modelling experiments (10:20)
How to save a TensorFlow model (8:19)
How to load and use a saved TensorFlow model (10:15)
(Optional) How to save and download files from Google Colab (6:18)
Putting together what we've learned part 1 (preparing a dataset) (13:31)
Putting together what we've learned part 2 (building a regression model) (13:20)
Putting together what we've learned part 3 (improving our regression model) (15:47)
Preprocessing data with feature scaling part 1 (what is feature scaling?) (9:34)
Preprocessing data with feature scaling part 2 (normalising our data) (10:57)
Preprocessing data with feature scaling part 3 (fitting a model on scaled data) (7:40)
TensorFlow Regression challenge, exercises & extra-curriculum
Unlimited Updates
Neural network classification in TensorFlow
Introduction to neural network classification in TensorFlow (8:25)
Example classification problems (and their inputs and outputs) (6:38)
Input and output tensors of classification problems (6:21)
Typical architecture of neural network classification models with TensorFlow (9:36)
Creating and viewing classification data to model (11:34)
Checking the input and output shapes of our classification data (4:38)
Building a not very good classification model with TensorFlow (12:10)
Trying to improve our not very good classification model (9:13)
Creating a function to view our model's not so good predictions (15:08)
Note: Updates for TensorFlow 2.7.0
Make our poor classification model work for a regression dataset (12:18)
Non-linearity part 1: Straight lines and non-straight lines (9:38)
Non-linearity part 2: Building our first neural network with non-linearity (5:47)
Non-linearity part 3: Upgrading our non-linear model with more layers (10:18)
Non-linearity part 4: Modelling our non-linear data once and for all (8:37)
Non-linearity part 5: Replicating non-linear activation functions from scratch (14:26)
Getting great results in less time by tweaking the learning rate (14:47)
Using the TensorFlow History object to plot a model's loss curves (6:11)
Using callbacks to find a model's ideal learning rate (17:32)
Training and evaluating a model with an ideal learning rate (9:20)
Introducing more classification evaluation methods (6:04)
Finding the accuracy of our classification model (4:17)
Creating our first confusion matrix (to see where our model is getting confused) (8:27)
Making our confusion matrix prettier (14:00)
Putting things together with multi-class classification part 1: Getting the data (10:37)
Multi-class classification part 2: Becoming one with the data (7:07)
Multi-class classification part 3: Building a multi-class classification model (15:38)
Multi-class classification part 4: Improving performance with normalisation (12:43)
Multi-class classification part 5: Comparing normalised and non-normalised data (4:13)
Multi-class classification part 6: Finding the ideal learning rate (10:38)
Multi-class classification part 7: Evaluating our model (13:16)
Multi-class classification part 8: Creating a confusion matrix (4:26)
Multi-class classification part 9: Visualising random model predictions (10:42)
What "patterns" is our model learning? (15:33)
TensorFlow classification challenge, exercises & extra-curriculum
Course Check-In
Computer Vision and Convolutional Neural Networks in TensorFlow
Introduction to Computer Vision with TensorFlow (9:36)
Introduction to Convolutional Neural Networks (CNNs) with TensorFlow (7:59)
Downloading an image dataset for our first Food Vision model (8:27)
Becoming One With Data (5:05)
Becoming One With Data Part 2 (12:26)
Becoming One With Data Part 3 (4:22)
Building an end to end CNN Model (18:17)
Using a GPU to run our CNN model 5x faster (9:17)
Trying a non-CNN model on our image data (8:51)
Improving our non-CNN model by adding more layers (9:52)
Breaking our CNN model down part 1: Becoming one with the data (9:03)
Breaking our CNN model down part 2: Preparing to load our data (11:46)
Breaking our CNN model down part 3: Loading our data with ImageDataGenerator (9:54)
Breaking our CNN model down part 4: Building a baseline CNN model (8:02)
Breaking our CNN model down part 5: Looking inside a Conv2D layer (15:20)
Breaking our CNN model down part 6: Compiling and fitting our baseline CNN (7:14)
Breaking our CNN model down part 7: Evaluating our CNN's training curves (11:45)
Breaking our CNN model down part 8: Reducing overfitting with Max Pooling (13:40)
Breaking our CNN model down part 9: Reducing overfitting with data augmentation (6:52)
Breaking our CNN model down part 10: Visualizing our augmented data (15:04)
Breaking our CNN model down part 11: Training a CNN model on augmented data (8:49)
Breaking our CNN model down part 12: Discovering the power of shuffling data (10:01)
Breaking our CNN model down part 13: Exploring options to improve our model (5:21)
Downloading a custom image to make predictions on (4:54)
Writing a helper function to load and preprocessing custom images (10:00)
Making a prediction on a custom image with our trained CNN (10:08)
Multi-class CNN's part 1: Becoming one with the data (14:59)
Multi-class CNN's part 2: Preparing our data (turning it into tensors) (6:38)
Multi-class CNN's part 3: Building a multi-class CNN model (7:24)
Multi-class CNN's part 4: Fitting a multi-class CNN model to the data (6:02)
Multi-class CNN's part 5: Evaluating our multi-class CNN model (4:51)
Multi-class CNN's part 6: Trying to fix overfitting by removing layers (12:19)
Multi-class CNN's part 7: Trying to fix overfitting with data augmentation (11:45)
Multi-class CNN's part 8: Things you could do to improve your CNN model (4:23)
Multi-class CNN's part 9: Making predictions with our model on custom images (9:22)
Saving and loading our trained CNN model (6:21)
TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum
Implement a New Life System
Transfer Learning in TensorFlow Part 1: Feature extraction
What is and why use transfer learning? (10:12)
Downloading and preparing data for our first transfer learning model (14:39)
Introducing Callbacks in TensorFlow and making a callback to track our models (10:01)
Exploring the TensorFlow Hub website for pretrained models (9:51)
Building and compiling a TensorFlow Hub feature extraction model (14:00)
Blowing our previous models out of the water with transfer learning (9:13)
Plotting the loss curves of our ResNet feature extraction model (7:35)
Building and training a pre-trained EfficientNet model on our data (9:42)
Different Types of Transfer Learning (11:40)
Comparing Our Model's Results (15:16)
TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum
Transfer Learning in TensorFlow Part 2: Fine tuning
Introduction to Transfer Learning in TensorFlow Part 2: Fine-tuning (6:16)
Importing a script full of helper functions (and saving lots of space) (7:35)
Exercise: Imposter Syndrome (2:55)
Downloading and turning our images into a TensorFlow BatchDataset (15:38)
Discussing the four (actually five) modelling experiments we're running (2:15)
Comparing the TensorFlow Keras Sequential API versus the Functional API (2:34)
Note: Fixes for EfficientNetB0 model creation + weight loading
Creating our first model with the TensorFlow Keras Functional API (11:38)
Compiling and fitting our first Functional API model (10:53)
Getting a feature vector from our trained model (13:39)
Drilling into the concept of a feature vector (a learned representation) (3:43)
Downloading and preparing the data for Model 1 (1 percent of training data) (9:51)
Building a data augmentation layer to use inside our model (12:06)
Note: Small fix for next video, for images not augmenting
Visualizing what happens when images pass through our data augmentation layer (10:55)
Building Model 1 (with a data augmentation layer and 1% of training data) (15:55)
Building Model 2 (with a data augmentation layer and 10% of training data) (16:37)
Creating a ModelCheckpoint to save our model's weights during training (7:25)
Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint) (7:14)
Loading and comparing saved weights to our existing trained Model 2 (7:17)
Preparing Model 3 (our first fine-tuned model) (20:26)
Fitting and evaluating Model 3 (our first fine-tuned model) (7:45)
Comparing our model's results before and after fine-tuning (10:26)
Downloading and preparing data for our biggest experiment yet (Model 4) (6:24)
Preparing our final modelling experiment (Model 4) (12:00)
Fine-tuning Model 4 on 100% of the training data and evaluating its results (10:19)
Comparing our modelling experiment results in TensorBoard (10:46)
How to view and delete previous TensorBoard experiments (2:04)
Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum
Transfer Learning with TensorFlow Part 3: Scaling Up
Introduction to Transfer Learning Part 3: Scaling Up (6:19)
Getting helper functions ready and downloading data to model (13:34)
Outlining the model we're going to build and building a ModelCheckpoint callback (5:38)
Creating a data augmentation layer to use with our model (4:39)
Creating a headless EfficientNetB0 model with data augmentation built in (8:58)
Fitting and evaluating our biggest transfer learning model yet (7:56)
Unfreezing some layers in our base model to prepare for fine-tuning (11:28)
Fine-tuning our feature extraction model and evaluating its performance (8:23)
Saving and loading our trained model (6:25)
Downloading a pretrained model to make and evaluate predictions with (6:34)
Making predictions with our trained model on 25,250 test samples (12:46)
Unravelling our test dataset for comparing ground truth labels to predictions (6:05)
Confirming our model's predictions are in the same order as the test labels (5:17)
Creating a confusion matrix for our model's 101 different classes (12:07)
Evaluating every individual class in our dataset (14:16)
Plotting our model's F1-scores for each separate class (7:36)
Creating a function to load and prepare images for making predictions (12:08)
Making predictions on our test images and evaluating them (16:06)
Discussing the benefits of finding your model's most wrong predictions (6:09)
Writing code to uncover our model's most wrong predictions (11:16)
Plotting and visualizing the samples our model got most wrong (10:36)
Making predictions on and plotting our own custom images (9:49)
Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum
Milestone Project 1: Food Vision Big™
Introduction to Milestone Project 1: Food Vision Big™ (5:44)
Making sure we have access to the right GPU for mixed precision training (10:17)
Getting helper functions ready (3:06)
Introduction to TensorFlow Datasets (TFDS) (12:03)
Exploring and becoming one with the data (Food101 from TensorFlow Datasets) (15:56)
Creating a preprocessing function to prepare our data for modelling (15:50)
Batching and preparing our datasets (to make them run fast) (13:47)
Exploring what happens when we batch and prefetch our data (6:49)
Creating modelling callbacks for our feature extraction model (7:14)
Note: Mixed Precision producing errors for TensorFlow 2.5+
Turning on mixed precision training with TensorFlow (10:05)
Creating a feature extraction model capable of using mixed precision training (12:42)
Checking to see if our model is using mixed precision training layer by layer (7:56)
Training and evaluating a feature extraction model (Food Vision Big™) (10:19)
Introducing your Milestone Project 1 challenge: build a model to beat DeepFood (7:47)
Milestone Project 1: Food Vision Big™, exercises and extra-curriculum
NLP Fundamentals in TensorFlow
Welcome to natural language processing with TensorFlow!
Introduction to Natural Language Processing (NLP) and Sequence Problems (12:51)
Example NLP inputs and outputs (7:22)
The typical architecture of a Recurrent Neural Network (RNN) (9:03)
Preparing a notebook for our first NLP with TensorFlow project (8:52)
Becoming one with the data and visualizing a text dataset (16:41)
Splitting data into training and validation sets (6:26)
Converting text data to numbers using tokenisation and embeddings (overview) (9:22)
Setting up a TensorFlow TextVectorization layer to convert text to numbers (17:10)
Mapping the TextVectorization layer to text data and turning it into numbers (11:02)
Creating an Embedding layer to turn tokenised text into embedding vectors (12:27)
Discussing the various modelling experiments we're going to run (8:57)
Model 0: Building a baseline model to try and improve upon (9:25)
Creating a function to track and evaluate our model's results (12:14)
Model 1: Building, fitting and evaluating our first deep model on text data (20:51)
Visualizing our model's learned word embeddings with TensorFlow's projector tool (20:43)
High-level overview of Recurrent Neural Networks (RNNs) + where to learn more (9:34)
Model 2: Building, fitting and evaluating our first TensorFlow RNN model (LSTM) (18:16)
Model 3: Building, fitting and evaluating a GRU-cell powered RNN (16:56)
Model 4: Building, fitting and evaluating a bidirectional RNN model (19:34)
Discussing the intuition behind Conv1D neural networks for text and sequences (19:31)
Model 5: Building, fitting and evaluating a 1D CNN for text (9:57)
Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP) (13:45)
Model 6: Building, training and evaluating a transfer learning model for NLP (10:45)
Preparing subsets of data for model 7 (same as model 6 but 10% of data) (10:52)
Model 7: Building, training and evaluating a transfer learning model on 10% data (10:04)
Fixing our data leakage issue with model 7 and retraining it (13:42)
Comparing all our modelling experiments evaluation metrics (13:14)
Uploading our model's training logs to TensorBoard and comparing them (11:14)
Saving and loading in a trained NLP model with TensorFlow (10:25)
Downloading a pretrained model and preparing data to investigate predictions (13:24)
Visualizing our model's most wrong predictions (8:28)
Making and visualizing predictions on the test dataset (8:27)
Understanding the concept of the speed/score tradeoff (15:01)
NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum
Milestone Project 2: SkimLit
Introduction to Milestone Project 2: SkimLit (14:20)
What we're going to cover in Milestone Project 2 (NLP for medical abstracts) (7:22)
SkimLit inputs and outputs (11:02)
Setting up our notebook for Milestone Project 2 (getting the data) (14:58)
Visualizing examples from the dataset (becoming one with the data) (13:18)
Writing a preprocessing function to structure our data for modelling (19:50)
Performing visual data analysis on our preprocessed text (7:55)
Turning our target labels into numbers (ML models require numbers) (13:15)
Model 0: Creating, fitting and evaluating a baseline model for SkimLit (9:25)
Preparing our data for deep sequence models (9:55)
Creating a text vectoriser to map our tokens (text) to numbers (14:07)
Creating a custom token embedding layer with TensorFlow (9:14)
Creating fast loading dataset with the TensorFlow tf.data API (9:49)
Model 1: Building, fitting and evaluating a Conv1D with token embeddings (17:21)
Preparing a pretrained embedding layer from TensorFlow Hub for Model 2 (10:53)
Model 2: Building, fitting and evaluating a Conv1D model with token embeddings (11:30)
Creating a character-level tokeniser with TensorFlow's TextVectorization layer (23:24)
Creating a character-level embedding layer with tf.keras.layers.Embedding (7:44)
Model 3: Building, fitting and evaluating a Conv1D model on character embeddings (13:45)
Discussing how we're going to build Model 4 (character + token embeddings) (6:04)
Model 4: Building a multi-input model (hybrid token + character embeddings) (15:36)
Model 4: Plotting and visually exploring different data inputs (7:32)
Crafting multi-input fast loading tf.data datasets for Model 4 (8:41)
Model 4: Building, fitting and evaluating a hybrid embedding model (13:18)
Model 5: Adding positional embeddings via feature engineering (overview) (7:18)
Encoding the line number feature to used with Model 5 (12:25)
Encoding the total lines feature to be used with Model 5 (7:56)
Model 5: Building the foundations of a tribrid embedding model (9:19)
Model 5: Completing the build of a tribrid embedding model for sequences (14:08)
Visually inspecting the architecture of our tribrid embedding model (10:25)
Creating multi-level data input pipelines for Model 5 with the tf.data API (9:00)
Bringing SkimLit to life!!! (fitting and evaluating Model 5) (10:35)
Comparing the performance of all of our modelling experiments (9:36)
Saving, loading & testing our best performing model (7:48)
Congratulations and your challenge before heading to the next module (12:33)
Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum
Time Series fundamentals in TensorFlow + Milestone Project 3: BitPredict
Welcome to time series fundamentals with TensorFlow + Milestone Project 3!
Introduction to Milestone Project 3 (BitPredict) & where you can get help (3:53)
What is a time series problem and example forecasting problems at Uber (7:45)
Example forecasting problems in daily life (4:52)
What can be forecast? (7:57)
What we're going to cover (broadly) (2:35)
Time series forecasting inputs and outputs (8:55)
Downloading and inspecting our Bitcoin historical dataset (14:58)
Different kinds of time series patterns & different amounts of feature variables (7:39)
Visualizing our Bitcoin historical data with pandas (4:52)
Reading in our Bitcoin data with Python's CSV module (10:58)
Creating train and test splits for time series (the wrong way) (8:37)
Creating train and test splits for time series (the right way) (7:12)
Creating a plotting function to visualize our time series data (7:57)
Discussing the various modelling experiments were going to be running (9:11)
Model 0: Making and visualizing a naive forecast model (12:16)
Discussing some of the most common time series evaluation metrics (11:11)
Implementing MASE with TensorFlow (9:38)
Creating a function to evaluate our model's forecasts with various metrics (10:11)
Discussing other non-TensorFlow kinds of time series forecasting models (5:06)
Formatting data Part 2: Creating a function to label our windowed time series (13:01)
Discussing the use of windows and horizons in time series data (7:50)
Writing a preprocessing function to turn time series data into windows & labels (23:35)
Turning our windowed time series data into training and test sets (10:01)
Creating a modelling checkpoint callback to save our best performing model (7:25)
Model 1: Building, compiling and fitting a deep learning model on Bitcoin data (16:58)
Creating a function to make predictions with our trained models (14:02)
Model 2: Building, fitting and evaluating a deep model with a larger window size-27 (17:43)
Model 3: Building, fitting and evaluating a model with a larger horizon size (13:15)
Adjusting the evaluation function to work for predictions with larger horizons (8:34)
Model 3: Visualizing the results (8:44)
Comparing our modelling experiments so far and discussing autocorrelation (9:44)
Preparing data for building a Conv1D model (13:21)
Model 4: Building, fitting and evaluating a Conv1D model on our Bitcoin data (14:51)
Model 5: Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data (16:05)
Investigating how to turn our univariate time series into multivariate (13:52)
Creating and plotting a multivariate time series with BTC price and block reward (12:12)
Preparing our multivariate time series for a model (13:37)
Model 6: Building, fitting and evaluating a multivariate time series model (9:25)
Model 7: Discussing what we're going to be doing with the N-BEATS algorithm (9:39)
Model 7: Replicating the N-BEATS basic block with TensorFlow layer subclassing (18:38)
Model 7: Testing our N-BEATS block implementation with dummy data inputs (15:02)
Model 7: Creating a performant data pipeline for the N-BEATS model with tf.data (14:09)
Model 7: Setting up hyperparameters for the N-BEATS algorithm (8:50)
Model 7: Getting ready for residual connections (12:55)
Model 7: Outlining the steps we're going to take to build the N-BEATS model (10:05)
Model 7: Putting together the pieces of the puzzle of the N-BEATS model (22:22)
Model 7: Plotting the N-BEATS algorithm we've created and admiring its beauty (6:46)
Model 8: Ensemble model overview (4:43)
Model 8: Building, compiling and fitting an ensemble of models (20:04)
Model 8: Making and evaluating predictions with our ensemble model (16:09)
Discussing the importance of prediction intervals in forecasting (12:56)
Getting the upper and lower bounds of our prediction intervals (7:57)
Plotting the prediction intervals of our ensemble model predictions (13:02)
(Optional) Discussing the types of uncertainty in machine learning (13:41)
Model 9: Preparing data to create a model capable of predicting into the future (8:24)
Model 9: Building, compiling and fitting a future predictions model (5:01)
Model 9: Discussing what's required for our model to make future predictions (8:30)
Model 9: Creating a function to make forecasts into the future (12:08)
Model 9: Plotting our model's future forecasts (13:09)
Model 10: Introducing the turkey problem and making data for it (14:15)
Model 10: Building a model to predict on turkey data (why forecasting is BS) (13:38)
Comparing the results of all of our models and discussing where to go next (12:59)
TensorFlow Time Series Fundamentals Challenge and Extra Resources
Where To Go From Here?
Thank You! (1:17)
Review This Course!
Become An Alumni
Learning Guideline
ZTM Events Every Month
LinkedIn Endorsements
Appendix: Machine Learning Primer
Quick Note: Upcoming Videos
What is Machine Learning? (6:52)
AI/Machine Learning/Data Science (4:51)
Exercise: Machine Learning Playground (6:16)
How Did We Get Here? (6:03)
Exercise: YouTube Recommendation Engine (4:24)
Types of Machine Learning (4:41)
Are You Getting It Yet?
What Is Machine Learning? Round 2 (4:44)
Section Review (1:48)
Appendix: Machine Learning and Data Science Framework
Quick Note: Upcoming Videos
Section Overview (3:08)
Introducing Our Framework (2:38)
6 Step Machine Learning Framework (4:59)
Types of Machine Learning Problems (10:32)
Types of Data (4:50)
Types of Evaluation (3:31)
Features In Data (5:22)
Modelling - Splitting Data (5:58)
Modelling - Picking the Model (4:35)
Modelling - Tuning (3:17)
Modelling - Comparison (9:32)
Overfitting and Underfitting Definitions
Experimentation (3:35)
Tools We Will Use (3:59)
Optional: Elements of AI(document)
Appendix: Pandas for Data Analysis
Quick Note: Upcoming Videos
Section Overview (2:27)
Downloading Workbooks and Assignments
Pandas Introduction (4:29)
Series, Data Frames and CSVs (13:21)
Data from URLs
Describing Data with Pandas (9:48)
Selecting and Viewing Data with Pandas (11:08)
Selecting and Viewing Data with Pandas Part 2 (13:06)
Manipulating Data (13:56)
Manipulating Data 2 (9:56)
Manipulating Data 3 (10:12)
Assignment: Pandas Practice
How To Download The Course Assignments (7:43)
Appendix: NumPy
Quick Note: Upcoming Videos
Section Overview (2:40)
NumPy Introduction (5:17)
Quick Note: Correction In Next Video
NumPy DataTypes and Attributes (14:05)
Creating NumPy Arrays (9:22)
NumPy Random Seed (7:17)
Viewing Arrays and Matrices (9:35)
Manipulating Arrays (11:31)
Manipulating Arrays 2 (9:44)
Standard Deviation and Variance (7:10)
Reshape and Transpose (7:26)
Dot Product vs Element Wise (11:45)
Exercise: Nut Butter Store Sales (13:04)
Comparison Operators (3:33)
Sorting Arrays (6:19)
Turn Images Into NumPy Arrays (7:37)
Assignment: NumPy Practice
Optional: Extra NumPy resources
Using callbacks to find a model's ideal learning rate
This lecture is available exclusively for ZTM Academy members.
If you're already a member,
you'll need to login
.
Join ZTM To Unlock All Lectures