Section 1 : Introduction to the Course
Lecture 1 What is Data Science 00:03:56 Duration

Section 2 : Predict Movie Box Office Revenue with Linear Regression
Lecture 1 Introduction to Linear Regression & Specifying the Problem 00:05:25 Duration
Lecture 2 Gather & Clean the Data 00:09:10 Duration
Lecture 3 Explore & Visualise the Data with Python 00:22:09 Duration
Lecture 4 The Intuition behind the Linear Regression Model 00:07:04 Duration
Lecture 5 Analyse and Evaluate the Results 00:15:32 Duration

Section 3 : Python Programming for Data Science and Machine Learning
Lecture 1 Windows Users - Install Anaconda 00:06:35 Duration
Lecture 2 Mac Users - Install Anaconda 00:06:04 Duration
Lecture 3 Does LSD Make You Better at Maths 00:05:20 Duration
Lecture 4 [Python] - Variables and Types 00:14:07 Duration
Lecture 5 [Python] - Lists and Arrays 00:09:14 Duration
Lecture 6 [Python & Pandas] - Dataframes and Series 00:24:19 Duration
Lecture 7 [Python] - Module Imports 00:29:22 Duration
Lecture 8 [Python] - Functions - Part 1 Defining and Calling Functions 00:07:22 Duration
Lecture 9 [Python] - Functions - Part 2 Arguments & Parameters 00:16:32 Duration
Lecture 10 [Python] - Functions - Part 3 Results & Return Values 00:12:53 Duration
Lecture 11 [Python] - Objects - Understanding Attributes and Methods 00:24:02 Duration
Lecture 12 How to Make Sense of Python Documentation for Data Visualisation 00:22:37 Duration
Lecture 13 Working with Python Objects to Analyse Data 00:22:50 Duration
Lecture 14 [Python] - Tips, Code Style and Naming Conventions 00:11:53 Duration

Section 4 : Introduction to Optimisation and the Gradient Descent Algorithm
Lecture 1 What's Coming Up 00:02:25 Duration
Lecture 2 How a Machine Learns 00:05:02 Duration
Lecture 3 Introduction to Cost Functions 00:07:28 Duration
Lecture 4 LaTeX Markdown and Generating Data with Numpy 00:14:54 Duration
Lecture 5 Understanding the Power Rule & Creating Charts with Subplots 00:14:51 Duration
Lecture 6 [Python] - Loops and the Gradient Descent Algorithm 00:35:22 Duration
Lecture 7 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1) 00:37:35 Duration
Lecture 8 [Python] - Tuples and the Pitfalls of Optimisation (Part 2) 00:30:05 Duration
Lecture 9 Understanding the Learning Rate 00:29:39 Duration
Lecture 10 How to Create 3-Dimensional Charts 00:23:42 Duration
Lecture 11 Understanding Partial Derivatives and How to use SymPy 00:18:22 Duration
Lecture 12 Implementing Batch Gradient Descent with SymPy 00:11:41 Duration
Lecture 13 [Python] - Loops and Performance Considerations 00:15:55 Duration
Lecture 14 Reshaping and Slicing N-Dimensional Arrays 00:18:16 Duration
Lecture 15 Concatenating Numpy Arrays 00:07:38 Duration
Lecture 16 Introduction to the Mean Squared Error (MSE) 00:09:53 Duration
Lecture 17 Transposing and Reshaping Arrays 00:12:51 Duration
Lecture 18 Implementing a MSE Cost Function 00:12:16 Duration
Lecture 19 Understanding Nested Loops and Plotting the MSE Function (Part 1) 00:11:02 Duration
Lecture 20 Plotting the Mean Squared Error (MSE) on a Surface (Part 2) 00:16:18 Duration
Lecture 21 Running Gradient Descent with a MSE Cost Function 00:18:58 Duration
Lecture 22 Visualising the Optimisation on a 3D Surface 00:09:33 Duration

Section 5 : Predict House Prices with Multivariable Linear Regression
Lecture 1 Defining the Problem 00:04:18 Duration
Lecture 2 Gathering the Boston House Price Data 00:06:30 Duration
Lecture 3 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset 00:12:40 Duration
Lecture 4 Clean and Explore the Data (Part 2) Find Missing Values 00:17:19 Duration
Lecture 5 Visualising Data (Part 1) Historams, Distributions & Outliers 00:11:33 Duration
Lecture 6 Visualising Data (Part 2) Seaborn and Probability Density Functions 00:08:31 Duration
Lecture 7 Working with Index Data, Pandas Series, and Dummy Variables 00:18:04 Duration
Lecture 8 Understanding Descriptive Statistics the Mean vs the Median 00:09:24 Duration
Lecture 9 Introduction to Correlation Understanding Strength & Direction 00:05:42 Duration
Lecture 10 Calculating Correlations and the Problem posed by Multicollinearity 00:14:29 Duration
Lecture 11 Visualising Correlations with a Heatmap 00:21:37 Duration
Lecture 12 Techniques to Style Scatter Plots
Lecture 13 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques 00:24:00 Duration
Lecture 14 Understanding Multivariable Regression 00:05:45 Duration
Lecture 15 How to Shuffle and Split Training & Testing Data 00:09:19 Duration
Lecture 16 Running a Multivariable Regression 00:08:42 Duration
Lecture 17 How to Calculate the Model Fit with R-Squared 00:04:20 Duration
Lecture 18 Introduction to Model Evaluation 00:02:20 Duration
Lecture 19 Improving the Model by Transforming the Data 00:20:03 Duration
Lecture 20 How to Interpret Coefficients using p-Values and Statistical Significance 00:08:24 Duration
Lecture 21 Understanding VIF & Testing for Multicollinearity 00:21:02 Duration
Lecture 22 Model Simplification & Baysian Information Criterion 00:19:20 Duration
Lecture 23 How to Analyse and Plot Regression Residuals 00:10:43 Duration
Lecture 24 Residual Analysis (Part 1) Predicted vs Actual Values 00:16:30 Duration
Lecture 25 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals 00:19:52 Duration
Lecture 26 Making Predictions (Part 1) MSE & R-Squared 00:18:38 Duration
Lecture 27 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals 00:12:48 Duration
Lecture 28 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays 00:17:54 Duration
Lecture 29 [Python] - Conditional Statements - Build a Valuation Tool (Part 2) 00:19:50 Duration

Section 6 : Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam
Lecture 1 Gathering Email Data and Working with Archives & Text Editors 00:10:36 Duration
Lecture 2 How to Add the Lesson Resources to the Project 00:03:40 Duration
Lecture 3 Basic Probability 00:04:16 Duration
Lecture 4 Joint & Conditional Probability 00:16:37 Duration
Lecture 5 Bayes Theorem 00:12:16 Duration
Lecture 6 Reading Files (Part 1) Absolute Paths and Relative Paths 00:08:28 Duration
Lecture 7 Reading Files (Part 2) Stream Objects and Email Structure
Lecture 8 Extracting the Text in the Email Body 00:05:27 Duration
Lecture 9 [Python] - Generator Functions & the yield Keyword 00:19:36 Duration
Lecture 10 Create a Pandas DataFrame of Email Bodies 00:06:10 Duration
Lecture 11 Cleaning Data (Part 1) Check for Empty Emails & Null Entries 00:15:30 Duration
Lecture 12 Cleaning Data (Part 2) Working with a DataFrame Index 00:08:15 Duration
Lecture 13 Saving a JSON File with Pandas 00:06:06 Duration
Lecture 14 Data Visualisation (Part 1) Pie Charts 00:13:45 Duration
Lecture 15 Data Visualisation (Part 2) Donut Charts 00:08:04 Duration
Lecture 16 Introduction to Natural Language Processing (NLP) 00:06:34 Duration
Lecture 17 Word Stemming & Removing Punctuation 00:09:30 Duration
Lecture 18 Removing HTML tags with BeautifulSoup 00:09:40 Duration
Lecture 19 Creating a Function for Text Processing 00:07:39 Duration
Lecture 20 Advanced Subsetting on DataFrames the apply() Function 00:12:16 Duration
Lecture 21 [Python] - Logical Operators to Create Subsets and Indices 00:12:47 Duration
Lecture 22 Word Clouds & How to install Additional Python Packages 00:08:30 Duration
Lecture 23 Creating your First Word Cloud 00:11:52 Duration
Lecture 24 Styling the Word Cloud with a Mask 00:15:25 Duration
Lecture 25 Solving the Hamlet Challenge 00:06:45 Duration
Lecture 26 Styling Word Clouds with Custom Fonts 00:12:52 Duration
Lecture 27 Create the Vocabulary for the Spam Classifier 00:15:41 Duration
Lecture 28 Coding Challenge Check for Membership in a Collection 00:05:15 Duration
Lecture 29 Coding Challenge Find the Longest Email 00:07:17 Duration
Lecture 30 Sparse Matrix (Part 1) Split the Training and Testing Data 00:12:34 Duration
Lecture 31 Sparse Matrix (Part 2) Data Munging with Nested Loops 00:21:20 Duration
Lecture 32 Sparse Matrix (Part 3) Using groupby() and Saving 00:10:52 Duration
Lecture 33 Coding Challenge Solution Preparing the Test Data 00:04:41 Duration
Lecture 34 Checkpoint Understanding the Data 00:11:53 Duration

Section 7 : Train a Naive Bayes Classifier to Create a Spam Filter Part 2
Lecture 1 Setting up the Notebook and Understanding Delimiters in a Dataset 00:09:49 Duration
Lecture 2 Create a Full Matrix 00:17:51 Duration
Lecture 3 Count the Tokens to Train the Naive Bayes Model 00:15:02 Duration
Lecture 4 Sum the Tokens across the Spam and Ham Subsets 00:07:38 Duration
Lecture 5 Calculate the Token Probabilities and Save the Trained Model 00:08:04 Duration
Lecture 6 Coding Challenge Prepare the Test Data

Section 8 : Test and Evaluate a Naive Bayes Classifier Part 3
Lecture 1 Set up the Testing Notebook 00:03:54 Duration
Lecture 2 Joint Conditional Probability (Part 1) Dot Product 00:11:01 Duration
Lecture 3 Joint Conditional Probablity (Part 2) Priors 00:09:40 Duration
Lecture 4 Making Predictions Comparing Joint Probabilities 00:07:47 Duration
Lecture 5 The Accuracy Metric 00:06:31 Duration
Lecture 6 Visualising the Decision Boundary 00:30:54 Duration
Lecture 7 False Positive vs False Negatives 00:10:44 Duration
Lecture 8 The Recall Metric 00:05:28 Duration
Lecture 9 The Precision Metric 00:07:50 Duration
Lecture 10 The F-score or F1 Metric 00:04:29 Duration
Lecture 11 A Naive Bayes Implementation using SciKit Learn 00:29:10 Duration

Section 9 : Introduction to Neural Networks and How to Use Pre-Trained Models
Lecture 1 The Human Brain and the Inspiration for Artificial Neural Networks 00:07:36 Duration
Lecture 2 Layers, Feature Generation and Learning 00:20:35 Duration
Lecture 3 Costs and Disadvantages of Neural Networks 00:13:32 Duration
Lecture 4 Preprocessing Image Data and How RGB Works
Lecture 5 Importing Keras Models and the Tensorflow Graph 00:09:05 Duration
Lecture 6 Making Predictions using InceptionResNet 00:16:16 Duration
Lecture 7 Coding Challenge Solution Using other Keras Models 00:11:28 Duration

Section 10 : Build an Artificial Neural Network to Recognise Images using Keras
Lecture 1 Solving a Business Problem with Image Classification 00:03:33 Duration
Lecture 2 Installing Tensorflow and Keras for Jupyter 00:04:41 Duration
Lecture 3 Gathering the CIFAR 10 Dataset 00:04:51 Duration
Lecture 4 Exploring the CIFAR Data 00:15:47 Duration
Lecture 5 Pre-processing Scaling Inputs and Creating a Validation Dataset 00:14:46 Duration
Lecture 6 Compiling a Keras Model and Understanding the Cross Entropy Loss Function 00:14:07 Duration
Lecture 7 Interacting with the Operating System and the Python Try-Catch Block 00:19:42 Duration
Lecture 8 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems 00:12:07 Duration
Lecture 9 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques 00:23:04 Duration
Lecture 10 Use the Model to Make Predictions 00:25:28 Duration
Lecture 11 Model Evaluation and the Confusion Matrix 00:08:33 Duration
Lecture 12 Model Evaluation and the Confusion Matrix 00:32:56 Duration

Section 11 : Use Tensorflow to Classify Handwritten Digits
Lecture 1 What's coming up 00:01:38 Duration
Lecture 2 Getting the Data and Loading it into Numpy Arrays 00:07:49 Duration
Lecture 3 Data Exploration and Understanding the Structure of the Input Data 00:05:10 Duration
Lecture 4 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset 00:10:53 Duration
Lecture 5 What is a Tensor 00:06:30 Duration
Lecture 6 Creating Tensors and Setting up the Neural Network Architecture 00:22:55 Duration
Lecture 7 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics 00:10:38 Duration
Lecture 8 TensorFlow Sessions and Batching Data 00:16:38 Duration
Lecture 9 Tensorboard Summaries and the Filewriter 00:17:37 Duration
Lecture 10 Understanding the Tensorflow Graph Nodes and Edges 00:14:52 Duration
Lecture 11 Name Scoping and Image Visualisation in Tensorboard 00:20:10 Duration
Lecture 12 Different Model Architectures Experimenting with Dropout 00:21:51 Duration
Lecture 13 Prediction and Model Evaluation 00:14:23 Duration

Section 12 : Serving a Tensorflow Model through a Website
Lecture 1 What you'll make 00:06:42 Duration
Lecture 2 Saving Tensorflow Models 00:15:09 Duration
Lecture 3 Loading a SavedModel 00:18:25 Duration
Lecture 4 Converting a Model to Tensorflow 00:14:31 Duration
Lecture 5 Introducing the Website Project and Tooling
Lecture 6 HTML and CSS Styling 00:29:18 Duration
Lecture 7 Loading a Tensorflow 00:29:29 Duration
Lecture 8 Adding a Favicon 00:05:34 Duration
Lecture 9 Styling an HTML Canvas 00:32:19 Duration
Lecture 10 Drawing on an HTML Canvas 00:28:24 Duration
Lecture 11 Data Pre-Processing for Tensorflow 00:08:35 Duration
Lecture 12 Introduction to OpenCV 00:30:40 Duration
Lecture 13 Resizing and Adding Padding to Images 00:21:53 Duration
Lecture 14 Calculating the Centre of Mass and Shifting the Image 00:28:54 Duration
Lecture 15 Making a Prediction from a Digit drawn on the HTML Canvas 00:13:31 Duration