#### Section 1 : Introduction to the Course

 Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 2 What is Data Science 3:56 Lecture 3 ML Data Science Syllabus Pdf Lecture 4 Top Tips for Succeeding on this Course Text Lecture 5 Course Resources List Text

#### Section 2 : Predict Movie Box Office Revenue with Linear Regression

 Lecture 6 Introduction to Linear Regression & Specifying the Problem 5:25 Lecture 7 Gather & Clean the Data 9:10 Lecture 8 Explore & Visualise the Data with Python 22:9 Lecture 9 The Intuition behind the Linear Regression Model 7:4 Lecture 10 Analyse and Evaluate the Results 15:32 Lecture 11 Download the Complete Notebook Here Text Lecture 12 Join the Student Community Text Lecture 13 Any Feedback on this Section Text

#### Section 3 : Python Programming for Data Science and Machine Learning

 Lecture 14 Windows Users - Install Anaconda 6:35 Lecture 15 Mac Users - Install Anaconda 6:4 Lecture 16 Does LSD Make You Better at Maths 5:20 Lecture 17 Download the 12 Rules to Learn to Code Text Lecture 18 [Python] - Variables and Types 14:7 Lecture 19 [Python] - Lists and Arrays 9:14 Lecture 20 [Python & Pandas] - Dataframes and Series 24:19 Lecture 21 [Python] - Module Imports 29:22 Lecture 22 [Python] - Functions - Part 1 Defining and Calling Functions 7:22 Lecture 23 [Python] - Functions - Part 2 Arguments & Parameters 16:32 Lecture 24 [Python] - Functions - Part 3 Results & Return Values 12:53 Lecture 25 [Python] - Objects - Understanding Attributes and Methods 24:2 Lecture 26 How to Make Sense of Python Documentation for Data Visualisation 22:37 Lecture 27 Working with Python Objects to Analyse Data 22:50 Lecture 28 [Python] - Tips, Code Style and Naming Conventions 11:53 Lecture 29 Download the Complete Notebook Here Text Lecture 30 Any Feedback on this Section Text

#### Section 4 : Introduction to Optimisation and the Gradient Descent Algorithm

 Lecture 31 What's Coming Up 2:25 Lecture 32 How a Machine Learns 5:2 Lecture 33 Introduction to Cost Functions 7:28 Lecture 34 LaTeX Markdown and Generating Data with Numpy 14:54 Lecture 35 Understanding the Power Rule & Creating Charts with Subplots 14:51 Lecture 36 [Python] - Loops and the Gradient Descent Algorithm 35:22 Lecture 37 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1) 37:35 Lecture 38 [Python] - Tuples and the Pitfalls of Optimisation (Part 2) 30:5 Lecture 39 Understanding the Learning Rate 29:39 Lecture 40 How to Create 3-Dimensional Charts 23:42 Lecture 41 Understanding Partial Derivatives and How to use SymPy 18:22 Lecture 42 Implementing Batch Gradient Descent with SymPy 11:41 Lecture 43 [Python] - Loops and Performance Considerations 15:55 Lecture 44 Reshaping and Slicing N-Dimensional Arrays 18:16 Lecture 45 Concatenating Numpy Arrays 7:38 Lecture 46 Introduction to the Mean Squared Error (MSE) 9:53 Lecture 47 Transposing and Reshaping Arrays 12:51 Lecture 48 Implementing a MSE Cost Function 12:16 Lecture 49 Understanding Nested Loops and Plotting the MSE Function (Part 1) 11:2 Lecture 50 Plotting the Mean Squared Error (MSE) on a Surface (Part 2) 16:18 Lecture 51 Running Gradient Descent with a MSE Cost Function 18:58 Lecture 52 Visualising the Optimisation on a 3D Surface 9:33 Lecture 53 Download the Complete Notebook Here Text Lecture 54 Any Feedback on this Section Text

#### Section 5 : Predict House Prices with Multivariable Linear Regression

 Lecture 55 Defining the Problem 4:18 Lecture 56 Gathering the Boston House Price Data 6:30 Lecture 57 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset 12:40 Lecture 58 Clean and Explore the Data (Part 2) Find Missing Values 17:19 Lecture 59 Visualising Data (Part 1) Historams, Distributions & Outliers 11:33 Lecture 60 Visualising Data (Part 2) Seaborn and Probability Density Functions 8:31 Lecture 61 Working with Index Data, Pandas Series, and Dummy Variables 18:4 Lecture 62 Understanding Descriptive Statistics the Mean vs the Median 9:24 Lecture 63 Introduction to Correlation Understanding Strength & Direction 5:42 Lecture 64 Calculating Correlations and the Problem posed by Multicollinearity 14:29 Lecture 65 Visualising Correlations with a Heatmap 21:37 Lecture 66 Techniques to Style Scatter Plots Lecture 67 A Note for the Next Lesson Text Lecture 68 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques 24:0 Lecture 69 Understanding Multivariable Regression 5:45 Lecture 70 How to Shuffle and Split Training & Testing Data 9:19 Lecture 71 Running a Multivariable Regression 8:42 Lecture 72 How to Calculate the Model Fit with R-Squared 4:20 Lecture 73 Introduction to Model Evaluation 2:20 Lecture 74 Improving the Model by Transforming the Data 20:3 Lecture 75 How to Interpret Coefficients using p-Values and Statistical Significance 8:24 Lecture 76 Understanding VIF & Testing for Multicollinearity 21:2 Lecture 77 Model Simplification & Baysian Information Criterion 19:20 Lecture 78 How to Analyse and Plot Regression Residuals 10:43 Lecture 79 Residual Analysis (Part 1) Predicted vs Actual Values 16:30 Lecture 80 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals 19:52 Lecture 81 Making Predictions (Part 1) MSE & R-Squared 18:38 Lecture 82 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals 12:48 Lecture 83 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays 17:54 Lecture 84 [Python] - Conditional Statements - Build a Valuation Tool (Part 2) 19:50 Lecture 85 About Certification Pdf Lecture 86 Download the Complete Notebook Here Text Lecture 87 Any Feedback on this Section Text Lecture 88 About Proctor Testing Pdf

#### Section 6 : Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam

 Lecture 89 Gathering Email Data and Working with Archives & Text Editors 10:36 Lecture 90 How to Add the Lesson Resources to the Project 3:40 Lecture 91 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 92 Basic Probability 4:16 Lecture 93 Joint & Conditional Probability 16:37 Lecture 94 Bayes Theorem 12:16 Lecture 95 Reading Files (Part 1) Absolute Paths and Relative Paths 8:28 Lecture 96 Reading Files (Part 2) Stream Objects and Email Structure Lecture 97 Extracting the Text in the Email Body 5:27 Lecture 98 [Python] - Generator Functions & the yield Keyword 19:36 Lecture 99 Create a Pandas DataFrame of Email Bodies 6:10 Lecture 100 Cleaning Data (Part 1) Check for Empty Emails & Null Entries 15:30 Lecture 101 Cleaning Data (Part 2) Working with a DataFrame Index 8:15 Lecture 102 Saving a JSON File with Pandas 6:6 Lecture 103 Data Visualisation (Part 1) Pie Charts 13:45 Lecture 104 Data Visualisation (Part 2) Donut Charts 8:4 Lecture 105 Introduction to Natural Language Processing (NLP) 6:34 Lecture 107 Word Stemming & Removing Punctuation 9:30 Lecture 108 Removing HTML tags with BeautifulSoup 9:40 Lecture 109 Creating a Function for Text Processing 7:39 Lecture 110 A Note for the Next Lesson Text Lecture 111 Advanced Subsetting on DataFrames the apply() Function 12:16 Lecture 112 [Python] - Logical Operators to Create Subsets and Indices 12:47 Lecture 113 Word Clouds & How to install Additional Python Packages 8:30 Lecture 114 Creating your First Word Cloud 11:52 Lecture 115 Styling the Word Cloud with a Mask 15:25 Lecture 116 Solving the Hamlet Challenge 6:45 Lecture 117 Styling Word Clouds with Custom Fonts 12:52 Lecture 118 Create the Vocabulary for the Spam Classifier 15:41 Lecture 119 Coding Challenge Check for Membership in a Collection 5:15 Lecture 120 Coding Challenge Find the Longest Email 7:17 Lecture 121 Sparse Matrix (Part 1) Split the Training and Testing Data 12:34 Lecture 122 Sparse Matrix (Part 2) Data Munging with Nested Loops 21:20 Lecture 123 Sparse Matrix (Part 3) Using groupby() and Saving 10:52 Lecture 124 Coding Challenge Solution Preparing the Test Data 4:41 Lecture 125 Checkpoint Understanding the Data 11:53 Lecture 126 Download the Complete Notebook Here Text Lecture 127 Any Feedback on this Section Text

#### Section 7 : Train a Naive Bayes Classifier to Create a Spam Filter Part 2

 Lecture 128 Setting up the Notebook and Understanding Delimiters in a Dataset 9:49 Lecture 129 Create a Full Matrix 17:51 Lecture 130 Count the Tokens to Train the Naive Bayes Model 15:2 Lecture 131 Sum the Tokens across the Spam and Ham Subsets 7:38 Lecture 132 Calculate the Token Probabilities and Save the Trained Model 8:4 Lecture 133 Coding Challenge Prepare the Test Data Lecture 134 Download the Complete Notebook Here Text Lecture 135 Any Feedback on this Section Text

#### Section 8 : Test and Evaluate a Naive Bayes Classifier Part 3

 Lecture 136 Set up the Testing Notebook 3:54 Lecture 137 Joint Conditional Probability (Part 1) Dot Product 11:1 Lecture 138 Joint Conditional Probablity (Part 2) Priors 9:40 Lecture 139 Making Predictions Comparing Joint Probabilities 7:47 Lecture 140 The Accuracy Metric 6:31 Lecture 141 Visualising the Decision Boundary 30:54 Lecture 142 False Positive vs False Negatives 10:44 Lecture 143 The Recall Metric 5:28 Lecture 144 The Precision Metric 7:50 Lecture 145 The F-score or F1 Metric 4:29 Lecture 146 A Naive Bayes Implementation using SciKit Learn 29:10 Lecture 147 Download the Complete Notebook Here Text Lecture 148 Any Feedback on this Section Text

#### Section 9 : Introduction to Neural Networks and How to Use Pre-Trained Models

 Lecture 149 The Human Brain and the Inspiration for Artificial Neural Networks 7:36 Lecture 150 Layers, Feature Generation and Learning 20:35 Lecture 151 Costs and Disadvantages of Neural Networks 13:32 Lecture 152 Preprocessing Image Data and How RGB Works Lecture 153 Importing Keras Models and the Tensorflow Graph 9:5 Lecture 154 Making Predictions using InceptionResNet 16:16 Lecture 155 Coding Challenge Solution Using other Keras Models 11:28 Lecture 156 Download the Complete Notebook Here Text Lecture 157 Any Feedback on this Section Text

#### Section 10 : Build an Artificial Neural Network to Recognise Images using Keras

 Lecture 158 Solving a Business Problem with Image Classification 3:33 Lecture 159 Installing Tensorflow and Keras for Jupyter 4:41 Lecture 160 Gathering the CIFAR 10 Dataset 4:51 Lecture 161 Exploring the CIFAR Data 15:47 Lecture 162 Pre-processing Scaling Inputs and Creating a Validation Dataset 14:46 Lecture 163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function 14:7 Lecture 164 Interacting with the Operating System and the Python Try-Catch Block 19:42 Lecture 165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems 12:7 Lecture 166 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques 23:4 Lecture 167 Use the Model to Make Predictions 25:28 Lecture 168 Model Evaluation and the Confusion Matrix 8:33 Lecture 169 Model Evaluation and the Confusion Matrix 32:56 Lecture 170 Download the Complete Notebook Here Text Lecture 171 Any Feedback on this Section Text

#### Section 11 : Use Tensorflow to Classify Handwritten Digits

 Lecture 172 What's coming up 1:38 Lecture 173 Getting the Data and Loading it into Numpy Arrays 7:49 Lecture 174 Data Exploration and Understanding the Structure of the Input Data 5:10 Lecture 175 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset 10:53 Lecture 176 What is a Tensor 6:30 Lecture 177 Creating Tensors and Setting up the Neural Network Architecture 22:55 Lecture 178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics 10:38 Lecture 179 TensorFlow Sessions and Batching Data 16:38 Lecture 180 Tensorboard Summaries and the Filewriter 17:37 Lecture 181 Understanding the Tensorflow Graph Nodes and Edges 14:52 Lecture 182 Name Scoping and Image Visualisation in Tensorboard 20:10 Lecture 183 Different Model Architectures Experimenting with Dropout 21:51 Lecture 184 Prediction and Model Evaluation 14:23 Lecture 185 Download the Complete Notebook Here Text Lecture 186 Any Feedback on this Section Text

#### Section 12 : Serving a Tensorflow Model through a Website

 Lecture 187 What you'll make 6:42 Lecture 188 Saving Tensorflow Models 15:9 Lecture 189 Loading a SavedModel 18:25 Lecture 190 Converting a Model to Tensorflow 14:31 Lecture 191 Introducing the Website Project and Tooling Lecture 192 HTML and CSS Styling 29:18 Lecture 193 Loading a Tensorflow 29:29 Lecture 194 Adding a Favicon 5:34 Lecture 195 Styling an HTML Canvas 32:19 Lecture 196 Drawing on an HTML Canvas 28:24 Lecture 197 Data Pre-Processing for Tensorflow 8:35 Lecture 198 Introduction to OpenCV 30:40 Lecture 199 Resizing and Adding Padding to Images 21:53 Lecture 200 Calculating the Centre of Mass and Shifting the Image 28:54 Lecture 201 Making a Prediction from a Digit drawn on the HTML Canvas 13:31