Complete 2022 Data Science & Machine Learning Bootcamp 

What is Machine Learning?

What is Data Science?

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Course Resources List

Introduction to Linear Regression & Specifying the Problem

Gather & Clean the Data

Explore & Visualise the Data with Python

The Intuition behind the Linear Regression Model

Analyse and Evaluate the Results

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Windows Users - Install Anaconda

Mac Users - Install Anaconda

Does LSD Make You Better at Maths?

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[Python] - Variables and Types

[Python] - Lists and Arrays

[Python & Pandas] - Dataframes and Series

[Python] - Module Imports

[Python] - Functions - Part 1: Defining and Calling Functions

[Python] - Functions - Part 2: Arguments & Parameters

[Python] - Functions - Part 3: Results & Return Values

[Python] - Objects - Understanding Attributes and Methods

How to Make Sense of Python Documentation for Data Visualisation

Working with Python Objects to Analyse Data

[Python] - Tips, Code Style and Naming Conventions

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What's Coming Up?

How a Machine Learns

Introduction to Cost Functions

LaTeX Markdown and Generating Data with Numpy

Understanding the Power Rule & Creating Charts with Subplots

[Python] - Loops and the Gradient Descent Algorithm

[Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1)

[Python] - Tuples and the Pitfalls of Optimisation (Part 2)

Understanding the Learning Rate

How to Create 3-Dimensional Charts

Understanding Partial Derivatives and How to use SymPy

Implementing Batch Gradient Descent with SymPy

[Python] - Loops and Performance Considerations

Reshaping and Slicing N-Dimensional Arrays

Concatenating Numpy Arrays

Introduction to the Mean Squared Error (MSE)

Transposing and Reshaping Arrays

Implementing a MSE Cost Function

Understanding Nested Loops and Plotting the MSE Function (Part 1)

Plotting the Mean Squared Error (MSE) on a Surface (Part 2)

Running Gradient Descent with a MSE Cost Function

Visualising the Optimisation on a 3D Surface

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Defining the Problem

Gathering the Boston House Price Data

Clean and Explore the Data (Part 1): Understand the Nature of the Dataset

Clean and Explore the Data (Part 2): Find Missing Values

Visualising Data (Part 1): Historams, Distributions & Outliers

Visualising Data (Part 2): Seaborn and Probability Density Functions

Working with Index Data, Pandas Series, and Dummy Variables

Understanding Descriptive Statistics: the Mean vs the Median

Introduction to Correlation: Understanding Strength & Direction

Calculating Correlations and the Problem posed by Multicollinearity

Visualising Correlations with a Heatmap

Techniques to Style Scatter Plots

A Note for the Next Lesson

Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques

Understanding Multivariable Regression

How to Shuffle and Split Training & Testing Data

Running a Multivariable Regression

How to Calculate the Model Fit with R-Squared

Introduction to Model Evaluation

Improving the Model by Transforming the Data

How to Interpret Coefficients using p-Values and Statistical Significance

Understanding VIF & Testing for Multicollinearity

Model Simplification & Baysian Information Criterion

How to Analyse and Plot Regression Residuals

Residual Analysis (Part 1): Predicted vs Actual Values

Residual Analysis (Part 2): Graphing and Comparing Regression Residuals

Making Predictions (Part 1): MSE & R-Squared

Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals

Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays

[Python] - Conditional Statements - Build a Valuation Tool (Part 2)

Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module

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How to Translate a Business Problem into a Machine Learning Problem

Gathering Email Data and Working with Archives & Text Editors

How to Add the Lesson Resources to the Project

The Naive Bayes Algorithm and the Decision Boundary for a Classifier

Basic Probability

Joint & Conditional Probability

Bayes Theorem

Reading Files (Part 1): Absolute Paths and Relative Paths

Reading Files (Part 2): Stream Objects and Email Structure

Extracting the Text in the Email Body

[Python] - Generator Functions & the yield Keyword

Create a Pandas DataFrame of Email Bodies

Cleaning Data (Part 1): Check for Empty Emails & Null Entries

Cleaning Data (Part 2): Working with a DataFrame Index

Saving a JSON File with Pandas

Data Visualisation (Part 1): Pie Charts

Data Visualisation (Part 2): Donut Charts

Introduction to Natural Language Processing (NLP)

Tokenizing, Removing Stop Words and the Python Set Data Structure

Word Stemming & Removing Punctuation

Removing HTML tags with BeautifulSoup

Creating a Function for Text Processing

A Note for the Next Lesson

Advanced Subsetting on DataFrames: the apply() Function

[Python] - Logical Operators to Create Subsets and Indices

Word Clouds & How to install Additional Python Packages

Creating your First Word Cloud

Styling the Word Cloud with a Mask

Solving the Hamlet Challenge

Styling Word Clouds with Custom Fonts

Create the Vocabulary for the Spam Classifier

Coding Challenge: Check for Membership in a Collection

Coding Challenge: Find the Longest Email

Sparse Matrix (Part 1): Split the Training and Testing Data

Sparse Matrix (Part 2): Data Munging with Nested Loops

Sparse Matrix (Part 3): Using groupby() and Saving .txt Files

Coding Challenge Solution: Preparing the Test Data

Checkpoint: Understanding the Data

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Setting up the Notebook and Understanding Delimiters in a Dataset

Create a Full Matrix

Count the Tokens to Train the Naive Bayes Model

Sum the Tokens across the Spam and Ham Subsets

Calculate the Token Probabilities and Save the Trained Model

Coding Challenge: Prepare the Test Data

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Set up the Testing Notebook

Joint Conditional Probability (Part 1): Dot Product

Joint Conditional Probablity (Part 2): Priors

Making Predictions: Comparing Joint Probabilities

The Accuracy Metric

Visualising the Decision Boundary

False Positive vs False Negatives

The Recall Metric

The Precision Metric

The F-score or F1 Metric

A Naive Bayes Implementation using SciKit Learn

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The Human Brain and the Inspiration for Artificial Neural Networks

Layers, Feature Generation and Learning

Costs and Disadvantages of Neural Networks

Preprocessing Image Data and How RGB Works

Importing Keras Models and the Tensorflow Graph

Making Predictions using InceptionResNet

Coding Challenge Solution: Using other Keras Models

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Solving a Business Problem with Image Classification

Installing Tensorflow and Keras for Jupyter

Gathering the CIFAR 10 Dataset

Exploring the CIFAR Data

Pre-processing: Scaling Inputs and Creating a Validation Dataset

Compiling a Keras Model and Understanding the Cross Entropy Loss Function

Interacting with the Operating System and the Python Try-Catch Block

Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems

Use Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques

Use the Model to Make Predictions

Model Evaluation and the Confusion Matrix

Model Evaluation and the Confusion Matrix

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Getting the Data and Loading it into Numpy Arrays

Data Exploration and Understanding the Structure of the Input Data

Data Preprocessing: One-Hot Encoding and Creating the Validation Dataset

What is a Tensor?

Creating Tensors and Setting up the Neural Network Architecture

Defining the Cross Entropy Loss Function, the Optimizer and the Metrics

TensorFlow Sessions and Batching Data

Tensorboard Summaries and the Filewriter

Understanding the Tensorflow Graph: Nodes and Edges

Name Scoping and Image Visualisation in Tensorboard

Different Model Architectures: Experimenting with Dropout

Prediction and Model Evaluation

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What you'll make

Saving Tensorflow Models

Loading a SavedModel

Converting a Model to Tensorflow.js

Introducing the Website Project and Tooling

HTML and CSS Styling

Loading a Tensorflow.js Model and Starting your own Server

Adding a Favicon

Styling an HTML Canvas

Drawing on an HTML Canvas

Data Pre-Processing for Tensorflow.js

Introduction to OpenCV

Resizing and Adding Padding to Images

Calculating the Centre of Mass and Shifting the Image

Making a Prediction from a Digit drawn on the HTML Canvas

Adding the Game Logic

Publish and Share your Website!

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Where next?

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