1.Introduction to the Course\ 0:00 What is Machine Learning 4:39 What is Data Science 2.Predict Movie Box Office Revenue with Linear Regression\ 8:48 Introduction to Linear Regression & Specifying the Problem 14:56 Gather & Clean the Data 24:46 Explore & Visualise the Data with Python 47:14 The Intuition behind the Linear Regression Model 54:38 Analyse and Evaluate the Results 3.Python Programming for Data Science and Machine Learning\ 1:10:26 Windows Users - Install Anaconda 1:17:11 Mac Users - Install Anaconda 1:23:25 Does LSD Make You Better at Maths 1:28:55 [Python] - Variables and Types 1:43:16 [Python] - Lists and Arrays 1:53:41 [Python & Pandas] - Dataframes and Series 2:18:13 [Python] - Module Imports 2:47:47 [Python] - Functions - Part 1 Defining and Calling Functions 2:55:34 [Python] - Functions - Part 2 Arguments & Parameters 3:12:53 [Python] - Functions - Part 3 Results & Return Values 3:26:31 [Python] - Objects - Understanding Attributes and Methods 3:50:49 How to Make Sense of Python Documentation for Data Visualisation 4:13:59 Working with Python Objects to Analyse Data 4:36:49 [Python] - Tips, Code Style and Naming Conventions 4.Introduction to Optimisation and the Gradient Descent Algorithm\ 4:49:27 What's Coming Up 4:52:09 How a Machine Learns 4:57:35 Introduction to Cost Functions 5:05:03 LaTeX Markdown and Generating Data with Numpy 5:20:29 Understanding the Power Rule & Creating Charts with Subplots 5:35:20 [Python] - Loops and the Gradient Descent Algorithm 6:12:21 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1) 6:49:57 [Python] - Tuples and the Pitfalls of Optimisation (Part 2) 7:20:02 Understanding the Learning Rate 7:49:42 How to Create 3-Dimensional Charts 8:14:21 Understanding Partial Derivatives and How to use SymPy 8:32:43 Implementing Batch Gradient Descent with SymPy 8:45:08 [Python] - Loops and Performance Considerations 9:01:03 Reshaping and Slicing N-Dimensional Arrays 9:20:17 Concatenating Numpy Arrays 9:27:56 Introduction to the Mean Squared Error (MSE) 9:38:46 Transposing and Reshaping Arrays 9:51:37 Implementing a MSE Cost Function 10:03:53 Understanding Nested Loops and Plotting the MSE Function (Part 1) 10:15:42 Plotting the Mean Squared Error (MSE) on a Surface (Part 2) 10:32:00 Running Gradient Descent with a MSE Cost Function 10:51:53 Visualising the Optimisation on a 3D Surface 5.Predict House Prices with Multivariable Linear Regression\ 11:01:26 Defining the Problem 11:06:12 Gathering the Boston House Price Data 11:13:12 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset 11:26:16 Clean and Explore the Data (Part 2) Find Missing Values 11:43:35 Visualising Data (Part 1) Historams, Distributions & Outliers 11:56:15 Visualising Data (Part 2) Seaborn and Probability Density Functions