5.Predict House Prices with Multivariable Linear Regression\ 4:45 Working with Index Data, Pandas Series, and Dummy Variables 22:50 Understanding Descriptive Statistics the Mean vs the Median 33:03 Introduction to Correlation Understanding Strength & Direction 39:45 Calculating Correlations and the Problem posed by Multicollinearity 54:14 Visualising Correlations with a Heatmap 1:15:51 Techniques to Style Scatter Plots 1:33:12 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques 1:57:12 Understanding Multivariable Regression 2:03:19 How to Shuffle and Split Training & Testing Data 2:13:41 Running a Multivariable Regression 2:22:23 How to Calculate the Model Fit with R-Squared 2:26:43 Introduction to Model Evaluation 2:29:22 Improving the Model by Transforming the Data 2:49:44 How to Interpret Coefficients using p-Values and Statistical Significance 2:58:54 Understanding VIF & Testing for Multicollinearity 3:20:44 Model Simplification & Baysian Information Criterion 3:40:19 How to Analyse and Plot Regression Residuals 3:51:17 Residual Analysis (Part 1) Predicted vs Actual Values 4:07:48 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals 4:27:40 Making Predictions (Part 1) MSE & R-Squared 4:47:10 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals 4:59:58 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays 5:18:23 [Python] - Conditional Statements - Build a Valuation Tool (Part 2) 5:38:13 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module 6.Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1\ 6:05:50 How to Translate a Business Problem into a Machine Learning Problem 6:13:16 Gathering Email Data and Working with Archives & Text Editors 6:24:29 How to Add the Resources to the Project 6:28:09 The Naive Bayes Algorithm and the Decision Boundary for a Classifier 6:33:12 Basic Probability 6:37:47 Joint & Conditional Probability 6:54:24 Bayes Theorem 7:06:40 Reading Files (Part 1) Absolute Paths and Relative Paths 7:16:12 Reading Files (Part 2) Stream Objects and Email Structure 7:28:35 Extracting the Text in the Email Body 7:34:02 [Python] - Generator Functions & the yield Keyword 7:54:04 Create a Pandas DataFrame of Email Bodies 8:00:15 Cleaning Data (Part 1) Check for Empty Emails & Null Entries 8:15:58 Cleaning Data (Part 2) Working with a DataFrame Index 8:24:13 Saving a JSON File with Pandas 8:30:20 Data Visualisation (Part 1) Pie Charts 8:44:12 Data Visualisation (Part 2) Donut Charts 8:52:17 Introduction to Natural Language Processing (NLP) 9:00:03 Tokenizing, Removing Stop Words and the Python Set Data Structure 9:15:54 Word Stemming & Removing Punctuation 9:25:24 Removing HTML tags with BeautifulSoup 9:35:04 Creating a Function for Text Processing 9:42:44 Advanced Subsetting on DataFrames the apply() Function 9:55:00 [Python] - Logical Operators to Create Subsets and Indices 10:07:47 Word Clouds & How to install Additional Python Packages 10:17:01 Creating your First Word Cloud 10:28:53 Styling the Word Cloud with a Mask 10:44:19 Solving the Hamlet Challenge 10:51:04 Styling Word Clouds with Custom Fonts 11:03:57 Create the Vocabulary for the Spam Classifier 11:19:38 Coding Challenge Check for Membership in a Collection 11:24:53 Coding Challenge Find the Longest Email 11:32:10 Sparse Matrix (Part 1) Split the Training and Testing Data 11:45:27 Sparse Matrix (Part 2) Data Munging with Nested Loops