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The Complete Data Science & Machine Learning Bootcamp
Introduction to the Course
Why this Course? (2:28)
What is Machine Learning? (4:39)
What is Data Science? (4:08)
Download the Course Syllabus
Top Tips for Succeeding on this Course
Course Resource List
Download the 12 Rules to Learn to Code
Join the London App Brewery Slack Channel
How to Use Slack to get Support & Participate in the L.A.B. Community
Predict Movie Box Office Revenue with Linear Regression
Introduction to Linear Regression & Specifying the Problem (6:07)
Gather & Clean the Data (9:50)
Explore & Visualise the Data with Python (22:28)
The Intuition behind the Linear Regression Model (7:23)
Analyse and Evaluate the Results (15:48)
Download the Complete Notebook Here
Python Programming for Data Science and Machine Learning
Windows Users - Install Anaconda (6:44)
Mac Users - Install Anaconda (6:13)
Does LSD Make You Better at Maths? (5:30)
Download the 12 Rules to Learn to Code
Python Variables and Types (14:20)
Python Lists and Arrays (10:24)
Python Dataframes and Series (24:31)
Python Module Imports (29:34)
Python Functions - Part 1: Defining and Calling Functions (7:46)
Python Functions - Part 2: Arguments & Parameters (17:19)
Python Functions - Part 3: Results & Return Values (13:37)
Objects in Python: Understanding Attributes and Methods (24:17)
How to Make Sense of Python Documentation for Data Visualisation (23:10)
Working with Python Objects to Analyse Data (22:50)
Python Tips, Code Style and Naming Conventions (12:37)
Download the Complete Notebook Here
Introduction to Optimisation and the Gradient Descent Algorithm
What's Coming Up? (2:42)
How a Machine Learns (5:25)
Introduction to Cost Functions (7:28)
LaTeX Markdown and Generating Data with Numpy (15:25)
Understanding the Power Rule & Creating Charts with Subplots (14:51)
Python Loops and the Gradient Descent Algorithm (37:00)
Advanced Python Functions and the Pitfalls of Optimisation (Part 1) (37:35)
Python Tuples and the Pitfalls of Optimisation (Part 2) (30:05)
Understanding the Learning Rate (29:39)
How to Create 3-Dimensional Charts (24:38)
Understanding Partial Derivatives and How to use SymPy (18:22)
Implementing Batch Gradient Descent with SymPy (12:24)
Python Loops and Performance Considerations (15:55)
Reshaping and Slicing N-Dimensional Arrays (19:13)
Concatenating Numpy Arrays (7:38)
Introduction to the Mean Squared Error (MSE) (10:49)
Transposing and Reshaping Arrays (12:51)
Implementing a MSE Cost Function (12:15)
Understanding Nested Loops and Plotting the MSE Function (Part 1) (11:48)
Plotting the Mean Squared Error (MSE) on a Surface (Part 2) (16:17)
Running Gradient Descent with a MSE Cost Function (19:53)
Visualising the Optimisation on a 3D Surface (9:33)
Download the Complete Notebook Here
Predict House Prices with Multivariable Linear Regression
Defining the Problem (4:45)
Gathering the Boston House Price Data (6:59)
Clean and Explore the Data (Part 1): Understand the Nature of the Dataset (13:03)
Clean and Explore the Data (Part 2): Find Missing Values (17:18)
Visualising Data (Part 1): Historams, Distributions & Outliers (12:39)
Visualising Data (Part 2): Seaborn and Probability Density Functions (8:30)
Working with Index Data, Pandas Series, and Dummy Variables (18:04)
Understanding Descriptive Statistics: the Mean vs the Median (10:13)
Introduction to Correlation: Understanding Strength & Direction (6:41)
Calculating Correlations and the Problem posed by Multicollinearity (14:28)
Visualising Correlations with a Heatmap (21:37)
Techniques to Style Scatter Plots (17:20)
Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques (24:00)
Understanding Multivariable Regression (6:06)
How to Shuffle and Split Training & Testing Data (10:21)
Running a Multivariable Regression (8:42)
How to Calculate the Model Fit with R-Squared (4:19)
Introduction to Model Evaluation (2:39)
Improving the Model by Transforming the Data (20:22)
How to Interpret Coefficients using p-Values and Statistical Significance (9:09)
Understanding VIF & Testing for Multicollinearity (21:49)
Model Simiplication & Baysian Information Criterion (19:35)
How to Analyse and Plot Regression Residuals (10:58)
Residual Analysis (Part 1): Predicted vs Actual Values (16:30)
Residual Analysis (Part 2): Graphing and Comparing Regression Residuals (19:51)
Making Predictions (Part 1): MSE & R-Squared (19:29)
Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals (12:48)
Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays (18:24)
Build a Valuation Tool (Part 2): if-else clauses & optional function arguments (19:50)
Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module (27:36)
Download the Complete Notebook Here
Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1
How to Translate a Business Problem into a Machine Learning Problem (7:25)
Gathering Email Data and Working with Archives & Text Editors (11:12)
How to Add the Lesson Resources to the Project (3:40)
The Naive Bayes Algorithm and the Decision Boundary for a Classifier (5:02)
Basic Probability (4:34)
Joint & Conditional Probability (16:37)
Bayes Theorem (12:15)
Reading Files (Part 1): Absolute Paths and Relative Paths (9:31)
Reading Files (Part 2): Stream Objects and Email Structure (12:22)
Extracting the Text in the Email Body (5:26)
Python Generator Functions & the yield Keyword (20:02)
Create a Pandas DataFrame of Email Bodies (6:09)
Cleaning Data (Part 1): Check for Empty Emails & Null Entries (15:43)
Cleaning Data (Part 2): Working with a DataFrame Index (8:15)
Saving a JSON File with Pandas (6:06)
Data Visualisation (Part 1): Pie Charts (13:52)
Data Visualisation (Part 2): Donut Charts (8:04)
Introduction to Natural Language Processing (NLP) (7:46)
Tokenizing, Removing Stop Words and the Python Set Data Structure (15:50)
Word Stemming & Removing Punctuation (9:30)
Removing HTML tags with BeautifulSoup (9:40)
Creating a Function for Text Processing (7:39)
Advanced Subsetting on DataFrames: the apply() Function (12:16)
Creating Subsets with Python Logical Operators and Indices (12:47)
Word Clouds & How to install Additional Python Packages (9:13)
Creating your First Word Cloud (11:51)
Styling the Word Cloud with a Mask (15:25)
Solving the Hamlet Challenge (6:45)
Styling Word Clouds with Custom Fonts (12:52)
Create the Vocabulary for the Spam Classifier (15:41)
Coding Challenge: Check for Membership in a Collection (5:15)
Coding Challenge: Find the Longest Email (7:17)
Sparse Matrix (Part 1): Split the Training and Testing Data (13:16)
Sparse Matrix (Part 2): Data Munging with Nested Loops (21:19)
Sparse Matrix (Part 3): Using groupby() and Saving .txt Files (10:52)
Coding Challenge Solution: Preparing the Test Data (4:40)
Checkpoint: Understanding the Data (11:53)
Download the Complete Notebook Here
Train a Naive Bayes Classifier to Create a Spam Filter: Part 2
Setting up the Notebook and Understanding Delimiters in a Dataset (10:22)
Create a Full Matrix (18:11)
Count the Tokens to Train the Naive Bayes Model (16:00)
Sum the Tokens across the Spam and Ham Subsets (7:37)
Calculate the Token Probabilities and Save the Trained Model (8:04)
Coding Challenge: Prepare the Test Data (4:44)
Download the Complete Notebook Here
Test and Evaluate a Naive Bayes Classifier: Part 3
Set up the Testing Notebook (3:53)
Joint Conditional Probability (Part 1): Dot Product (11:39)
Joint Conditional Probablity (Part 2): Priors (9:39)
Making Predictions: Comparing Joint Probabilities (8:17)
The Accuracy Metric (6:54)
Visualising the Decision Boundary (30:54)
False Positive vs False Negatives (11:35)
The Recall Metric (5:43)
The Precision Metric (8:04)
The F-score or F1 Metric (4:29)
A Naive Bayes Implementation using SciKit Learn (29:42)
Download the Complete Notebook Here
Introduction to Neural Networks and How to Use Pre-Trained Models
The Human Brain and the Inspiration for Artificial Neural Networks (8:10)
Layers, Feature Generation and Learning (21:10)
Costs and Disadvantages of Neural Networks (13:48)
Preprocessing Image Data and How RGB Works (13:23)
Importing Keras Models and the Tensorflow Graph (9:19)
Making Predictions using InceptionResNet (16:43)
Coding Challenge Solution: Using other Keras Models (11:27)
Download the Complete Notebook Here
Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
Solving a Business Problem with Image Classification (3:54)
Installing Tensorflow and Keras for Jupyter (5:08)
Gathering the CIFAR 10 Dataset (5:05)
Exploring the CIFAR Data (15:57)
Pre-processing: Scaling Inputs and Creating a Validation Dataset (15:53)
Compiling a Keras Model and Understanding the Cross Entropy Loss Function (14:32)
Interacting with the Operating System and the Python Try-Catch Block (19:50)
Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems (12:19)
Use Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques (23:21)
Use the Model to Make Predictions (26:26)
Model Evaluation and the Confusion Matrix (9:00)
Model Evaluation and the Confusion Matrix (33:25)
Download the Complete Notebook Here
Use Tensorflow to Classify Handwritten Digits
What's coming up? (1:50)
Getting the Data and Loading it into Numpy Arrays (7:48)
Data Exploration and Understanding the Structure of the Input Data (5:27)
Data Preprocessing: One-Hot Encoding and Creating the Validation Dataset (11:21)
What is a Tensor? (6:49)
Creating Tensors and Setting up the Neural Network Architecture (23:40)
Defining the Cross Entropy Loss Function, the Optimizer and the Metrics (11:25)
TensorFlow Sessions and Batching Data (17:20)
Tensorboard Summaries and the Filewriter (18:00)
Understanding the Tensorflow Graph: Nodes and Edges (15:37)
Name Scoping and Image Visualisation in Tensorboard (20:55)
Different Model Architectures: Experimenting with Dropout (22:36)
Prediction and Model Evaluation (15:03)
Download the Complete Notebook Here
Serving a Tensorflow Model through a Website
What you'll make (7:05)
Saving Tensorflow Models (16:03)
Loading a SavedModel (19:25)
Converting a Model to Tensorflow.js (15:14)
Introducing the Website Project and Tooling (12:55)
HTML and CSS Styling (30:03)
Loading a Tensorflow.js Model and Starting your own Server (30:13)
Adding a Favicon (5:45)
Styling an HTML Canvas (32:58)
Drawing on an HTML Canvas (28:24)
Data Pre-Processing for Tensorflow.js (9:12)
Introduction to OpenCV (30:49)
New Lecture
Resizing and Adding Padding to Images (21:52)
Making a Prediction from a Digit drawn on the HTML Canvas (14:04)
Adding the Game Logic (31:26)
Publish and Share your Website! (7:22)
Next Steps
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Joint Conditional Probablity (Part 2): Priors
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