Introduction to the Course
  • What is Machine Learning?
  • What is Data Science?
  • Download the Syllabus
  • Top Tips for Succeeding on this Course
  • Course Resources List
Predict Movie Box Office Revenue with Linear Regression
  • 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
  • Download the Complete Notebook Here
  • Join the Student Community
  • Any Feedback on this Section?
Python Programming for Data Science and Machine Learning
  • Windows Users - Install Anaconda
  • Mac Users - Install Anaconda
  • Does LSD Make You Better at Maths?
  • Download the 12 Rules to Learn to Code
  • [Python] - Variables and Types
  • Python Variable Coding Exercise
  • [Python] - Lists and Arrays
  • Python Lists Coding Exercise
  • [Python & Pandas] - Dataframes and Series
  • [Python] - Module Imports
  • [Python] - Functions - Part 1: Defining and Calling Functions
  • Python Functions Coding Exercise - Part 1
  • [Python] - Functions - Part 2: Arguments & Parameters
  • Python Functions Coding Exercise - Part 2
  • [Python] - Functions - Part 3: Results & Return Values
  • Python Functions Coding Exercise - Part 3
  • [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
  • Download the Complete Notebook Here
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Introduction to Optimisation and the Gradient Descent Algorithm
  • 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 Loops Coding Exercise
  • [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
  • Download the Complete Notebook Here
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Predict House Prices with Multivariable Linear Regression
  • 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)
  • Python Conditional Statement Coding Exercise
  • Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module
  • Download the Complete Notebook Here
  • Any Feedback on this Section?
Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1