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