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| [CourseClub.ME].url |
122б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| [GigaCourse.Com].url |
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| 01.1 Course Resources.html |
122б |
| 01.1 Course Resources.html |
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| 01.1 Course Resources.html |
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| 01.1 Course Resources.html |
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| 01.1 Course Resources.html |
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| 01.1 Course Resources.html |
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| 01.1 Course Resources.html |
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| 01.1 Course Resources.html |
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| 01.1 Course Resources.html |
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| 01.1 SpamData.zip |
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| 01.2 Course Resources.html |
122б |
| 01.2 SpamData.zip |
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| 01. Defining the Problem.mp4 |
39.91Мб |
| 01. Defining the Problem.srt |
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| 01. How to Translate a Business Problem into a Machine Learning Problem.mp4 |
42.26Мб |
| 01. How to Translate a Business Problem into a Machine Learning Problem.srt |
9.69Кб |
| 01. Introduction to Linear Regression & Specifying the Problem.mp4 |
30.32Мб |
| 01. Introduction to Linear Regression & Specifying the Problem.srt |
8.74Кб |
| 01. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 |
72.50Мб |
| 01. Setting up the Notebook and Understanding Delimiters in a Dataset.srt |
11.17Кб |
| 01. Set up the Testing Notebook.mp4 |
26.45Мб |
| 01. Set up the Testing Notebook.srt |
3.82Кб |
| 01. Solving a Business Problem with Image Classification.mp4 |
30.53Мб |
| 01. Solving a Business Problem with Image Classification.srt |
4.97Кб |
| 01. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 |
51.81Мб |
| 01. The Human Brain and the Inspiration for Artificial Neural Networks.srt |
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| 01. What's coming up.mp4 |
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| 01. What's Coming Up.mp4 |
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| 01. What's coming up.srt |
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| 01. What's Coming Up.srt |
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| 01. What is Machine Learning.mp4 |
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| 01. What is Machine Learning.srt |
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| 01. What you'll make.mp4 |
38.44Мб |
| 01. What you'll make.srt |
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| 01. Where next.html |
3.93Кб |
| 01. Windows Users - Install Anaconda.mp4 |
49.60Мб |
| 01. Windows Users - Install Anaconda.srt |
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| 02.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip |
6.39Кб |
| 02.1 Course Resources.html |
122б |
| 02.1 MNIST.zip |
14.77Мб |
| 02.1 SpamData.zip |
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| 02.1 The-Numbers Movie Budgets.html |
102б |
| 02.2 cost_revenue_dirty.csv |
374.68Кб |
| 02. Create a Full Matrix.mp4 |
132.24Мб |
| 02. Create a Full Matrix.srt |
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| 02. Gather & Clean the Data.mp4 |
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| 02. Gather & Clean the Data.srt |
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| 02. Gathering Email Data and Working with Archives & Text Editors.mp4 |
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| 02. Gathering Email Data and Working with Archives & Text Editors.srt |
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| 02. Gathering the Boston House Price Data.mp4 |
56.24Мб |
| 02. Gathering the Boston House Price Data.srt |
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| 02. Getting the Data and Loading it into Numpy Arrays.mp4 |
52.82Мб |
| 02. Getting the Data and Loading it into Numpy Arrays.srt |
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| 02. How a Machine Learns.mp4 |
22.78Мб |
| 02. How a Machine Learns.srt |
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| 02. Installing Tensorflow and Keras for Jupyter.mp4 |
42.10Мб |
| 02. Installing Tensorflow and Keras for Jupyter.srt |
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| 02. Joint Conditional Probability (Part 1) Dot Product.mp4 |
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| 02. Joint Conditional Probability (Part 1) Dot Product.srt |
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| 02. Layers, Feature Generation and Learning.mp4 |
146.70Мб |
| 02. Layers, Feature Generation and Learning.srt |
27.79Кб |
| 02. Mac Users - Install Anaconda.mp4 |
52.41Мб |
| 02. Mac Users - Install Anaconda.srt |
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| 02. Saving Tensorflow Models.mp4 |
109.98Мб |
| 02. Saving Tensorflow Models.srt |
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| 02. What is Data Science.mp4 |
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| 02. What is Data Science.srt |
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| 02. What Modules Do You Want to See.html |
431б |
| 03.1 ML Data Science Syllabus.pdf |
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| 03.1 MNIST_Model_Load_Files.zip |
2.84Мб |
| 03.1 Try Jupyter in your Browser.html |
85б |
| 03.2 12 TF SavedModel Export Completed.ipynb.zip |
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| 03.2 cost_revenue_clean.csv |
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| 03. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 |
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| 03. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt |
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| 03. Costs and Disadvantages of Neural Networks.mp4 |
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| 03. Costs and Disadvantages of Neural Networks.srt |
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| 03. Count the Tokens to Train the Naive Bayes Model.mp4 |
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| 03. Count the Tokens to Train the Naive Bayes Model.srt |
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| 03. Data Exploration and Understanding the Structure of the Input Data.mp4 |
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| 03. Data Exploration and Understanding the Structure of the Input Data.srt |
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| 03. Does LSD Make You Better at Maths.mp4 |
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| 03. Does LSD Make You Better at Maths.srt |
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| 03. Download the Syllabus.html |
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| 03. Explore & Visualise the Data with Python.mp4 |
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| 03. Explore & Visualise the Data with Python.srt |
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| 03. Gathering the CIFAR 10 Dataset.mp4 |
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| 03. Gathering the CIFAR 10 Dataset.srt |
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| 03. How to Add the Lesson Resources to the Project.mp4 |
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| 03. How to Add the Lesson Resources to the Project.srt |
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| 03. Introduction to Cost Functions.mp4 |
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| 03. Introduction to Cost Functions.srt |
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| 03. Joint Conditional Probablity (Part 2) Priors.mp4 |
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| 03. Joint Conditional Probablity (Part 2) Priors.srt |
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| 03. Loading a SavedModel.mp4 |
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| 03. Loading a SavedModel.srt |
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| 03. Stay in Touch!.html |
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| 04.1 01 Linear Regression (checkpoint).ipynb.zip |
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| 04.1 12 Rules to Learn to Code.pdf |
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| 04.1 App Brewery Cornell Notes Template.html |
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| 04.1 TF_Keras_Classification_Images.zip |
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| 04.1 TFJS.zip |
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| 04. Clean and Explore the Data (Part 2) Find Missing Values.mp4 |
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| 04. Clean and Explore the Data (Part 2) Find Missing Values.srt |
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| 04. Converting a Model to Tensorflow.js.mp4 |
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| 04. Converting a Model to Tensorflow.js.srt |
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| 04. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 |
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| 04. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.srt |
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| 04. Download the 12 Rules to Learn to Code.html |
1.13Кб |
| 04. Exploring the CIFAR Data.mp4 |
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| 04. Exploring the CIFAR Data.srt |
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| 04. LaTeX Markdown and Generating Data with Numpy.mp4 |
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| 04. Making Predictions Comparing Joint Probabilities.mp4 |
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| 04. Making Predictions Comparing Joint Probabilities.srt |
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| 04. Preprocessing Image Data and How RGB Works.mp4 |
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| 04. Preprocessing Image Data and How RGB Works.srt |
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| 04. Sum the Tokens across the Spam and Ham Subsets.mp4 |
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| 04. Sum the Tokens across the Spam and Ham Subsets.srt |
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| 04. The Intuition behind the Linear Regression Model.mp4 |
29.63Мб |
| 04. The Intuition behind the Linear Regression Model.srt |
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| 04. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 |
33.39Мб |
| 04. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.srt |
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| 04. Top Tips for Succeeding on this Course.html |
2.09Кб |
| 05. [Python] - Variables and Types.mp4 |
71.36Мб |
| 05. [Python] - Variables and Types.srt |
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| 05.1 math_garden_stub.zip |
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| 05. Analyse and Evaluate the Results.mp4 |
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| 05. Analyse and Evaluate the Results.srt |
22.41Кб |
| 05. Basic Probability.mp4 |
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| 05. Basic Probability.srt |
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| 05. Calculate the Token Probabilities and Save the Trained Model.mp4 |
53.45Мб |
| 05. Calculate the Token Probabilities and Save the Trained Model.srt |
9.44Кб |
| 05. Course Resources List.html |
1.13Кб |
| 05. Importing Keras Models and the Tensorflow Graph.mp4 |
65.47Мб |
| 05. Importing Keras Models and the Tensorflow Graph.srt |
11.44Кб |
| 05. Introducing the Website Project and Tooling.mp4 |
78.04Мб |
| 05. Introducing the Website Project and Tooling.srt |
17.19Кб |
| 05. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 |
93.16Мб |
| 05. Pre-processing Scaling Inputs and Creating a Validation Dataset.srt |
19.92Кб |
| 05. The Accuracy Metric.mp4 |
40.54Мб |
| 05. The Accuracy Metric.srt |
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| 05. Understanding the Power Rule & Creating Charts with Subplots.mp4 |
90.17Мб |
| 05. Understanding the Power Rule & Creating Charts with Subplots.srt |
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| 05. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 |
64.55Мб |
| 05. Visualising Data (Part 1) Historams, Distributions & Outliers.srt |
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| 05. What is a Tensor.mp4 |
45.39Мб |
| 05. What is a Tensor.srt |
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| 06. [Python] - Loops and the Gradient Descent Algorithm.mp4 |
287.46Мб |
| 06. [Python] - Loops and the Gradient Descent Algorithm.srt |
44.03Кб |
| 06.1 01 Linear Regression (complete).ipynb.zip |
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| 06. Coding Challenge Prepare the Test Data.mp4 |
35.60Мб |
| 06. Coding Challenge Prepare the Test Data.srt |
5.14Кб |
| 06. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 |
103.60Мб |
| 06. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.srt |
18.63Кб |
| 06. Creating Tensors and Setting up the Neural Network Architecture.mp4 |
150.86Мб |
| 06. Creating Tensors and Setting up the Neural Network Architecture.srt |
29.05Кб |
| 06. Download the Complete Notebook Here.html |
242б |
| 06. HTML and CSS Styling.mp4 |
150.23Мб |
| 06. HTML and CSS Styling.srt |
37.89Кб |
| 06. Joint & Conditional Probability.mp4 |
141.82Мб |
| 06. Joint & Conditional Probability.srt |
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| 06. Making Predictions using InceptionResNet.mp4 |
134.58Мб |
| 06. Making Predictions using InceptionResNet.srt |
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| 06. Python Variable Coding Exercise.html |
156б |
| 06. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 |
57.32Мб |
| 06. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt |
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| 06. Visualising the Decision Boundary.mp4 |
205.31Мб |
| 06. Visualising the Decision Boundary.srt |
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| 07. [Python] - Lists and Arrays.mp4 |
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| 07. [Python] - Lists and Arrays.srt |
12.15Кб |
| 07.1 07 Bayes Classifier - Training.ipynb.zip |
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| 07.1 x_test2_ylabel1.txt |
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| 07.2 x_test0_ylabel7.txt |
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| 07.3 x_test1_ylabel2.txt |
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| 07. Bayes Theorem.mp4 |
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| 07. Bayes Theorem.srt |
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| 07. Coding Challenge Solution Using other Keras Models.mp4 |
103.53Мб |
| 07. Coding Challenge Solution Using other Keras Models.srt |
12.94Кб |
| 07. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 |
75.11Мб |
| 07. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.srt |
14.15Кб |
| 07. Download the Complete Notebook Here.html |
242б |
| 07. False Positive vs False Negatives.mp4 |
63.25Мб |
| 07. False Positive vs False Negatives.srt |
12.81Кб |
| 07. Interacting with the Operating System and the Python Try-Catch Block.mp4 |
133.41Мб |
| 07. Interacting with the Operating System and the Python Try-Catch Block.srt |
23.69Кб |
| 07. Join the Student Community.html |
730б |
| 07. Loading a Tensorflow.js Model and Starting your own Server.mp4 |
188.04Мб |
| 07. Loading a Tensorflow.js Model and Starting your own Server.srt |
37.18Кб |
| 07. Python Loops Coding Exercise.html |
156б |
| 07. Working with Index Data, Pandas Series, and Dummy Variables.mp4 |
140.76Мб |
| 07. Working with Index Data, Pandas Series, and Dummy Variables.srt |
20.72Кб |
| 08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 |
291.33Мб |
| 08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).srt |
42.99Кб |
| 08.1 09 Neural Nets Pretrained Image Classification.ipynb.zip |
571.83Кб |
| 08. Adding a Favicon.mp4 |
41.51Мб |
| 08. Adding a Favicon.srt |
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| 08. Any Feedback on this Section.html |
512б |
| 08. Any Feedback on this Section.html |
527б |
| 08. Download the Complete Notebook Here.html |
264б |
| 08. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 |
100.42Мб |
| 08. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.srt |
14.10Кб |
| 08. Python Lists Coding Exercise.html |
156б |
| 08. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 |
60.90Мб |
| 08. Reading Files (Part 1) Absolute Paths and Relative Paths.srt |
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| 08. TensorFlow Sessions and Batching Data.mp4 |
100.32Мб |
| 08. TensorFlow Sessions and Batching Data.srt |
20.50Кб |
| 08. The Recall Metric.mp4 |
28.15Мб |
| 08. The Recall Metric.srt |
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| 08. Understanding Descriptive Statistics the Mean vs the Median.mp4 |
62.18Мб |
| 08. Understanding Descriptive Statistics the Mean vs the Median.srt |
12.14Кб |
| 09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 |
219.01Мб |
| 09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).srt |
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| 09. [Python & Pandas] - Dataframes and Series.mp4 |
153.20Мб |
| 09. [Python & Pandas] - Dataframes and Series.srt |
28.09Кб |
| 09.1 lsd_math_score_data.csv |
155б |
| 09. Any Feedback on this Section.html |
526б |
| 09. Introduction to Correlation Understanding Strength & Direction.mp4 |
33.09Мб |
| 09. Introduction to Correlation Understanding Strength & Direction.srt |
8.40Кб |
| 09. Reading Files (Part 2) Stream Objects and Email Structure.mp4 |
104.32Мб |
| 09. Reading Files (Part 2) Stream Objects and Email Structure.srt |
14.57Кб |
| 09. Styling an HTML Canvas.mp4 |
187.37Мб |
| 09. Styling an HTML Canvas.srt |
39.42Кб |
| 09. Tensorboard Summaries and the Filewriter.mp4 |
128.29Мб |
| 09. Tensorboard Summaries and the Filewriter.srt |
23.21Кб |
| 09. The Precision Metric.mp4 |
53.33Мб |
| 09. The Precision Metric.srt |
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| 09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 |
191.54Мб |
| 09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.srt |
28.28Кб |
| 10. [Python] - Module Imports.mp4 |
232.07Мб |
| 10. [Python] - Module Imports.srt |
36.12Кб |
| 10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 |
111.43Мб |
| 10. Calculating Correlations and the Problem posed by Multicollinearity.srt |
17.83Кб |
| 10. Drawing on an HTML Canvas.mp4 |
171.97Мб |
| 10. Drawing on an HTML Canvas.srt |
37.83Кб |
| 10. Extracting the Text in the Email Body.mp4 |
47.43Мб |
| 10. Extracting the Text in the Email Body.srt |
6.00Кб |
| 10. The F-score or F1 Metric.mp4 |
24.71Мб |
| 10. The F-score or F1 Metric.srt |
4.48Кб |
| 10. Understanding the Learning Rate.mp4 |
236.60Мб |
| 10. Understanding the Learning Rate.srt |
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| 10. Understanding the Tensorflow Graph Nodes and Edges.mp4 |
115.75Мб |
| 10. Understanding the Tensorflow Graph Nodes and Edges.srt |
21.25Кб |
| 10. Use the Model to Make Predictions.mp4 |
218.25Мб |
| 10. Use the Model to Make Predictions.srt |
32.97Кб |
| 11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 |
41.61Мб |
| 11. [Python] - Functions - Part 1 Defining and Calling Functions.srt |
10.49Кб |
| 11. [Python] - Generator Functions & the yield Keyword.mp4 |
133.16Мб |
| 11. [Python] - Generator Functions & the yield Keyword.srt |
22.32Кб |
| 11. A Naive Bayes Implementation using SciKit Learn.mp4 |
195.10Мб |
| 11. A Naive Bayes Implementation using SciKit Learn.srt |
33.68Кб |
| 11. Data Pre-Processing for Tensorflow.js.mp4 |
61.89Мб |
| 11. Data Pre-Processing for Tensorflow.js.srt |
11.92Кб |
| 11. How to Create 3-Dimensional Charts.mp4 |
193.48Мб |
| 11. How to Create 3-Dimensional Charts.srt |
26.10Кб |
| 11. Model Evaluation and the Confusion Matrix.mp4 |
62.76Мб |
| 11. Model Evaluation and the Confusion Matrix.srt |
10.80Кб |
| 11. Name Scoping and Image Visualisation in Tensorboard.mp4 |
155.37Мб |
| 11. Name Scoping and Image Visualisation in Tensorboard.srt |
26.26Кб |
| 11. Visualising Correlations with a Heatmap.mp4 |
168.65Мб |
| 11. Visualising Correlations with a Heatmap.srt |
24.37Кб |
| 12.1 08 Naive Bayes with scikit-learn.ipynb.zip |
13.26Кб |
| 12.1 math_garden_stub 12.12 checkpoint.zip |
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| 12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip |
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| 12. Create a Pandas DataFrame of Email Bodies.mp4 |
48.66Мб |
| 12. Create a Pandas DataFrame of Email Bodies.srt |
7.23Кб |
| 12. Different Model Architectures Experimenting with Dropout.mp4 |
213.67Мб |
| 12. Different Model Architectures Experimenting with Dropout.srt |
30.11Кб |
| 12. Download the Complete Notebook Here.html |
242б |
| 12. Introduction to OpenCV.mp4 |
235.33Мб |
| 12. Introduction to OpenCV.srt |
38.37Кб |
| 12. Model Evaluation and the Confusion Matrix.mp4 |
251.83Мб |
| 12. Model Evaluation and the Confusion Matrix.srt |
40.50Кб |
| 12. Python Functions Coding Exercise - Part 1.html |
156б |
| 12. Techniques to Style Scatter Plots.mp4 |
128.53Мб |
| 12. Techniques to Style Scatter Plots.srt |
20.56Кб |
| 12. Understanding Partial Derivatives and How to use SymPy.mp4 |
132.81Мб |
| 12. Understanding Partial Derivatives and How to use SymPy.srt |
20.23Кб |
| 13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 |
128.20Мб |
| 13. [Python] - Functions - Part 2 Arguments & Parameters.srt |
20.76Кб |
| 13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip |
120.11Кб |
| 13. A Note for the Next Lesson.html |
476б |
| 13. Any Feedback on this Section.html |
509б |
| 13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 |
121.94Мб |
| 13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.srt |
17.96Кб |
| 13. Download the Complete Notebook Here.html |
242б |
| 13. Implementing Batch Gradient Descent with SymPy.mp4 |
86.82Мб |
| 13. Implementing Batch Gradient Descent with SymPy.srt |
12.93Кб |
| 13. Prediction and Model Evaluation.mp4 |
110.72Мб |
| 13. Prediction and Model Evaluation.srt |
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| 13. Resizing and Adding Padding to Images.mp4 |
157.50Мб |
| 13. Resizing and Adding Padding to Images.srt |
26.86Кб |
| 14. [Python] - Loops and Performance Considerations.mp4 |
131.07Мб |
| 14. [Python] - Loops and Performance Considerations.srt |
18.07Кб |
| 14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip |
6.60Кб |
| 14. Any Feedback on this Section.html |
521б |
| 14. Calculating the Centre of Mass and Shifting the Image.mp4 |
223.26Мб |
| 14. Calculating the Centre of Mass and Shifting the Image.srt |
35.49Кб |
| 14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4 |
61.83Мб |
| 14. Cleaning Data (Part 2) Working with a DataFrame Index.srt |
9.23Кб |
| 14. Download the Complete Notebook Here.html |
242б |
| 14. Python Functions Coding Exercise - Part 2.html |
156б |
| 14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 |
214.40Мб |
| 14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt |
28.70Кб |
| 15. [Python] - Functions - Part 3 Results & Return Values.mp4 |
82.63Мб |
| 15. [Python] - Functions - Part 3 Results & Return Values.srt |
16.55Кб |
| 15. Any Feedback on this Section.html |
499б |
| 15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4 |
104.41Мб |
| 15. Making a Prediction from a Digit drawn on the HTML Canvas.srt |
17.04Кб |
| 15. Reshaping and Slicing N-Dimensional Arrays.mp4 |
140.81Мб |
| 15. Reshaping and Slicing N-Dimensional Arrays.srt |
22.96Кб |
| 15. Saving a JSON File with Pandas.mp4 |
56.35Мб |
| 15. Saving a JSON File with Pandas.srt |
6.92Кб |
| 15. Understanding Multivariable Regression.mp4 |
48.80Мб |
| 15. Understanding Multivariable Regression.srt |
7.52Кб |
| 16.1 math_garden_stub complete.zip |
4.09Мб |
| 16. Adding the Game Logic.mp4 |
172.83Мб |
| 16. Adding the Game Logic.srt |
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| 16. Concatenating Numpy Arrays.mp4 |
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| 16. Concatenating Numpy Arrays.srt |
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| 16. Data Visualisation (Part 1) Pie Charts.mp4 |
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| 16. Data Visualisation (Part 1) Pie Charts.srt |
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| 16. How to Shuffle and Split Training & Testing Data.mp4 |
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| 16. How to Shuffle and Split Training & Testing Data.srt |
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| 16. Python Functions Coding Exercise - Part 3.html |
156б |
| 17. [Python] - Objects - Understanding Attributes and Methods.mp4 |
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| 17. [Python] - Objects - Understanding Attributes and Methods.srt |
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| 17. Data Visualisation (Part 2) Donut Charts.mp4 |
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| 17. Data Visualisation (Part 2) Donut Charts.srt |
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| 17. Introduction to the Mean Squared Error (MSE).mp4 |
64.56Мб |
| 17. Introduction to the Mean Squared Error (MSE).srt |
12.61Кб |
| 17. Publish and Share your Website!.mp4 |
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| 17. Publish and Share your Website!.srt |
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| 17. Running a Multivariable Regression.mp4 |
55.56Мб |
| 17. Running a Multivariable Regression.srt |
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| 18. Any Feedback on this Section.html |
500б |
| 18. How to Calculate the Model Fit with R-Squared.mp4 |
32.40Мб |
| 18. How to Calculate the Model Fit with R-Squared.srt |
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| 18. How to Make Sense of Python Documentation for Data Visualisation.mp4 |
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| 18. How to Make Sense of Python Documentation for Data Visualisation.srt |
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| 18. Introduction to Natural Language Processing (NLP).mp4 |
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| 18. Introduction to Natural Language Processing (NLP).srt |
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| 18. Transposing and Reshaping Arrays.mp4 |
86.90Мб |
| 18. Transposing and Reshaping Arrays.srt |
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| 19. Implementing a MSE Cost Function.mp4 |
81.11Мб |
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| 19. Introduction to Model Evaluation.mp4 |
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| 19. Introduction to Model Evaluation.srt |
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| 19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 |
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| 19. Tokenizing, Removing Stop Words and the Python Set Data Structure.srt |
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| 19. Working with Python Objects to Analyse Data.mp4 |
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| 20. [Python] - Tips, Code Style and Naming Conventions.mp4 |
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| 20. [Python] - Tips, Code Style and Naming Conventions.srt |
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| 20. Improving the Model by Transforming the Data.mp4 |
126.87Мб |
| 20. Improving the Model by Transforming the Data.srt |
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| 20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 |
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| 20. Understanding Nested Loops and Plotting the MSE Function (Part 1).srt |
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| 20. Word Stemming & Removing Punctuation.mp4 |
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| 20. Word Stemming & Removing Punctuation.srt |
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| 21.1 02 Python Intro.ipynb.zip |
36.44Кб |
| 21. Download the Complete Notebook Here.html |
242б |
| 21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 |
65.41Мб |
| 21. How to Interpret Coefficients using p-Values and Statistical Significance.srt |
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| 21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 |
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| 21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).srt |
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| 21. Removing HTML tags with BeautifulSoup.mp4 |
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| 22. Any Feedback on this Section.html |
513б |
| 22. Creating a Function for Text Processing.mp4 |
53.91Мб |
| 22. Creating a Function for Text Processing.srt |
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| 22. Running Gradient Descent with a MSE Cost Function.mp4 |
111.22Мб |
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| 22. Understanding VIF & Testing for Multicollinearity.mp4 |
143.82Мб |
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| 23. A Note for the Next Lesson.html |
476б |
| 23. Model Simplification & Baysian Information Criterion.mp4 |
150.15Мб |
| 23. Model Simplification & Baysian Information Criterion.srt |
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| 23. Visualising the Optimisation on a 3D Surface.mp4 |
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| 23. Visualising the Optimisation on a 3D Surface.srt |
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| 24.1 03 Gradient Descent.ipynb.zip |
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| 24. Advanced Subsetting on DataFrames the apply() Function.mp4 |
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| 24. Advanced Subsetting on DataFrames the apply() Function.srt |
13.53Кб |
| 24. Download the Complete Notebook Here.html |
242б |
| 24. How to Analyse and Plot Regression Residuals.mp4 |
64.18Мб |
| 24. How to Analyse and Plot Regression Residuals.srt |
14.76Кб |
| 25. [Python] - Logical Operators to Create Subsets and Indices.mp4 |
86.41Мб |
| 25. [Python] - Logical Operators to Create Subsets and Indices.srt |
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| 25. Any Feedback on this Section.html |
520б |
| 25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 |
124.42Мб |
| 25. Residual Analysis (Part 1) Predicted vs Actual Values.srt |
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| 26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 |
153.01Мб |
| 26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt |
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| 26. Word Clouds & How to install Additional Python Packages.mp4 |
79.48Мб |
| 26. Word Clouds & How to install Additional Python Packages.srt |
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| 27. Creating your First Word Cloud.mp4 |
98.44Мб |
| 27. Creating your First Word Cloud.srt |
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| 27. Making Predictions (Part 1) MSE & R-Squared.mp4 |
152.68Мб |
| 27. Making Predictions (Part 1) MSE & R-Squared.srt |
23.72Кб |
| 28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 |
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| 28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt |
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| 28. Styling the Word Cloud with a Mask.mp4 |
131.37Мб |
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| 29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 |
131.31Мб |
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| 29. Solving the Hamlet Challenge.mp4 |
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| 30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 |
134.38Мб |
| 30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt |
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| 30. Styling Word Clouds with Custom Fonts.mp4 |
127.29Мб |
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| 31. Create the Vocabulary for the Spam Classifier.mp4 |
106.96Мб |
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| 31. Python Conditional Statement Coding Exercise.html |
156б |
| 32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 |
244.17Мб |
| 32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt |
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| 32. Coding Challenge Check for Membership in a Collection.mp4 |
32.34Мб |
| 32. Coding Challenge Check for Membership in a Collection.srt |
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| 33.1 04 Multivariable Regression.ipynb.zip |
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| 33.2 04 Valuation Tool.ipynb.zip |
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| 33.3 boston_valuation.py |
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| 33. Coding Challenge Find the Longest Email.mp4 |
54.47Мб |
| 33. Coding Challenge Find the Longest Email.srt |
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| 33. Download the Complete Notebook Here.html |
242б |
| 34. Any Feedback on this Section.html |
512б |
| 34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 |
87.62Мб |
| 34. Sparse Matrix (Part 1) Split the Training and Testing Data.srt |
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| 35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 |
137.23Мб |
| 35. Sparse Matrix (Part 2) Data Munging with Nested Loops.srt |
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| 36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 |
80.50Мб |
| 36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.srt |
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| 37. Coding Challenge Solution Preparing the Test Data.mp4 |
28.92Мб |
| 37. Coding Challenge Solution Preparing the Test Data.srt |
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| 38. Checkpoint Understanding the Data.mp4 |
96.37Мб |
| 38. Checkpoint Understanding the Data.srt |
13.65Кб |
| 39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip |
978.02Кб |
| 39. Download the Complete Notebook Here.html |
242б |
| 40. Any Feedback on this Section.html |
519б |