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Title [FreeCourseSite.com] Udemy - Complete 2022 Data Science & Machine Learning Bootcamp
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[CourseClub.Me].url 122B
[CourseClub.Me].url 122B
[FreeCourseSite.com].url 127B
[FreeCourseSite.com].url 127B
[GigaCourse.Com].url 49B
[GigaCourse.Com].url 49B
001 Defining the Problem_en.vtt 5.61KB
001 Defining the Problem.mp4 30.02MB
001 How to Translate a Business Problem into a Machine Learning Problem_en.vtt 8.46KB
001 How to Translate a Business Problem into a Machine Learning Problem.mp4 31.01MB
001 Introduction to Linear Regression & Specifying the Problem_en.vtt 7.58KB
001 Introduction to Linear Regression & Specifying the Problem.mp4 26.51MB
001 Setting up the Notebook and Understanding Delimiters in a Dataset_en.vtt 9.80KB
001 Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 53.98MB
001 Set up the Testing Notebook_en.vtt 3.34KB
001 Set up the Testing Notebook.mp4 19.97MB
001 Solving a Business Problem with Image Classification_en.vtt 4.49KB
001 Solving a Business Problem with Image Classification.mp4 19.47MB
001 The Human Brain and the Inspiration for Artificial Neural Networks_en.vtt 9.96KB
001 The Human Brain and the Inspiration for Artificial Neural Networks.mp4 32.71MB
001 What's coming up_en.vtt 2.32KB
001 What's Coming Up_en.vtt 3.36KB
001 What's coming up.mp4 5.23MB
001 What's Coming Up.mp4 12.92MB
001 What is Machine Learning_en.vtt 6.02KB
001 What is Machine Learning.mp4 40.37MB
001 What you'll make_en.vtt 8.73KB
001 What you'll make.mp4 35.52MB
001 Where next.html 3.90KB
001 Windows Users - Install Anaconda_en.vtt 7.72KB
001 Windows Users - Install Anaconda.mp4 32.19MB
002 Create a Full Matrix_en.vtt 19.11KB
002 Create a Full Matrix.mp4 104.85MB
002 Gather & Clean the Data_en.vtt 12.20KB
002 Gather & Clean the Data.mp4 40.86MB
002 Gathering Email Data and Working with Archives & Text Editors_en.vtt 12.26KB
002 Gathering Email Data and Working with Archives & Text Editors.mp4 95.99MB
002 Gathering the Boston House Price Data_en.vtt 7.55KB
002 Gathering the Boston House Price Data.mp4 47.58MB
002 Getting the Data and Loading it into Numpy Arrays_en.vtt 8.19KB
002 Getting the Data and Loading it into Numpy Arrays.mp4 39.66MB
002 How a Machine Learns_en.vtt 6.32KB
002 How a Machine Learns.mp4 10.47MB
002 Installing Tensorflow and Keras for Jupyter_en.vtt 5.90KB
002 Installing Tensorflow and Keras for Jupyter.mp4 31.92MB
002 Joint Conditional Probability (Part 1) Dot Product_en.vtt 11.06KB
002 Joint Conditional Probability (Part 1) Dot Product.mp4 47.03MB
002 Layers, Feature Generation and Learning_en.vtt 24.61KB
002 Layers, Feature Generation and Learning.mp4 124.35MB
002 Mac Users - Install Anaconda_en.vtt 7.07KB
002 Mac Users - Install Anaconda.mp4 39.12MB
002 Saving Tensorflow Models_en.vtt 19.00KB
002 Saving Tensorflow Models.mp4 103.67MB
002 What is Data Science_en.vtt 5.04KB
002 What is Data Science.mp4 39.83MB
002 What Modules Do You Want to See.html 431B
003 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset_en.vtt 13.66KB
003 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 56.73MB
003 Costs and Disadvantages of Neural Networks_en.vtt 16.96KB
003 Costs and Disadvantages of Neural Networks.mp4 76.56MB
003 Count the Tokens to Train the Naive Bayes Model_en.vtt 16.19KB
003 Count the Tokens to Train the Naive Bayes Model.mp4 63.64MB
003 Data Exploration and Understanding the Structure of the Input Data_en.vtt 5.73KB
003 Data Exploration and Understanding the Structure of the Input Data.mp4 20.60MB
003 Does LSD Make You Better at Maths_en.vtt 6.42KB
003 Does LSD Make You Better at Maths.mp4 15.63MB
003 Download the Syllabus.html 994B
003 Explore & Visualise the Data with Python_en.vtt 26.99KB
003 Explore & Visualise the Data with Python.mp4 105.12MB
003 Gathering the CIFAR 10 Dataset_en.vtt 5.48KB
003 Gathering the CIFAR 10 Dataset.mp4 20.60MB
003 How to Add the Lesson Resources to the Project_en.vtt 4.24KB
003 How to Add the Lesson Resources to the Project.mp4 18.95MB
003 Introduction to Cost Functions_en.vtt 8.19KB
003 Introduction to Cost Functions.mp4 39.02MB
003 Joint Conditional Probablity (Part 2) Priors_en.vtt 9.53KB
003 Joint Conditional Probablity (Part 2) Priors.mp4 45.80MB
003 Loading a SavedModel_en.vtt 23.15KB
003 Loading a SavedModel.mp4 85.10MB
003 Stay in Touch!.html 1.05KB
004 Clean and Explore the Data (Part 2) Find Missing Values_en.vtt 16.27KB
004 Clean and Explore the Data (Part 2) Find Missing Values.mp4 107.48MB
004 Converting a Model to Tensorflow.js_en.vtt 18.79KB
004 Converting a Model to Tensorflow.js.mp4 93.61MB
004 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset_en.vtt 11.48KB
004 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 49.34MB
004 Download the 12 Rules to Learn to Code.html 1.12KB
004 Exploring the CIFAR Data_en.vtt 16.11KB
004 Exploring the CIFAR Data.mp4 81.16MB
004 LaTeX Markdown and Generating Data with Numpy_en.vtt 14.82KB
004 LaTeX Markdown and Generating Data with Numpy.mp4 47.01MB
004 Making Predictions Comparing Joint Probabilities_en.vtt 8.78KB
004 Making Predictions Comparing Joint Probabilities.mp4 37.12MB
004 Preprocessing Image Data and How RGB Works_en.vtt 14.37KB
004 Preprocessing Image Data and How RGB Works.mp4 69.24MB
004 Sum the Tokens across the Spam and Ham Subsets_en.vtt 6.89KB
004 Sum the Tokens across the Spam and Ham Subsets.mp4 24.37MB
004 The Intuition behind the Linear Regression Model_en.vtt 9.45KB
004 The Intuition behind the Linear Regression Model.mp4 12.86MB
004 The Naive Bayes Algorithm and the Decision Boundary for a Classifier_en.vtt 5.36KB
004 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 29.41MB
004 Top Tips for Succeeding on this Course.html 2.06KB
005 [Python] - Variables and Types_en.vtt 14.49KB
005 [Python] - Variables and Types.mp4 47.55MB
005 Analyse and Evaluate the Results_en.vtt 19.58KB
005 Analyse and Evaluate the Results.mp4 75.46MB
005 Basic Probability_en.vtt 4.62KB
005 Basic Probability.mp4 9.40MB
005 Calculate the Token Probabilities and Save the Trained Model_en.vtt 8.26KB
005 Calculate the Token Probabilities and Save the Trained Model.mp4 35.29MB
005 Course Resources List.html 1.12KB
005 Importing Keras Models and the Tensorflow Graph_en.vtt 10.32KB
005 Importing Keras Models and the Tensorflow Graph.mp4 49.24MB
005 Introducing the Website Project and Tooling_en.vtt 15.68KB
005 Introducing the Website Project and Tooling.mp4 68.79MB
005 Pre-processing Scaling Inputs and Creating a Validation Dataset_en.vtt 17.80KB
005 Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 61.32MB
005 The Accuracy Metric_en.vtt 6.68KB
005 The Accuracy Metric.mp4 28.66MB
005 Understanding the Power Rule & Creating Charts with Subplots_en.vtt 15.59KB
005 Understanding the Power Rule & Creating Charts with Subplots.mp4 59.49MB
005 Visualising Data (Part 1) Historams, Distributions & Outliers_en.vtt 12.39KB
005 Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 42.62MB
005 What is a Tensor_en.vtt 8.18KB
005 What is a Tensor.mp4 37.85MB
006 [Python] - Lists and Arrays_en.vtt 10.55KB
006 [Python] - Lists and Arrays.mp4 35.09MB
006 [Python] - Loops and the Gradient Descent Algorithm_en.vtt 37.63KB
006 [Python] - Loops and the Gradient Descent Algorithm.mp4 92.92MB
006 Coding Challenge Prepare the Test Data_en.vtt 4.57KB
006 Coding Challenge Prepare the Test Data.mp4 28.62MB
006 Compiling a Keras Model and Understanding the Cross Entropy Loss Function_en.vtt 16.58KB
006 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 76.04MB
006 Creating Tensors and Setting up the Neural Network Architecture_en.vtt 26.20KB
006 Creating Tensors and Setting up the Neural Network Architecture.mp4 110.62MB
006 Download the Complete Notebook Here.html 242B
006 HTML and CSS Styling_en.vtt 33.85KB
006 HTML and CSS Styling.mp4 136.76MB
006 Joint & Conditional Probability_en.vtt 17.25KB
006 Joint & Conditional Probability.mp4 88.31MB
006 Making Predictions using InceptionResNet_en.vtt 17.08KB
006 Making Predictions using InceptionResNet.mp4 103.20MB
006 Visualising Data (Part 2) Seaborn and Probability Density Functions_en.vtt 7.84KB
006 Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 37.60MB
006 Visualising the Decision Boundary_en.vtt 30.30KB
006 Visualising the Decision Boundary.mp4 149.45MB
007 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1)_en.vtt 37.23KB
007 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 229.50MB
007 [Python & Pandas] - Dataframes and Series_en.vtt 24.43KB
007 [Python & Pandas] - Dataframes and Series.mp4 101.40MB
007 Bayes Theorem_en.vtt 13.19KB
007 Bayes Theorem.mp4 51.07MB
007 Coding Challenge Solution Using other Keras Models_en.vtt 11.83KB
007 Coding Challenge Solution Using other Keras Models.mp4 81.98MB
007 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics_en.vtt 12.68KB
007 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 49.63MB
007 Download the Complete Notebook Here.html 242B
007 False Positive vs False Negatives_en.vtt 11.47KB
007 False Positive vs False Negatives.mp4 41.39MB
007 Interacting with the Operating System and the Python Try-Catch Block_en.vtt 21.52KB
007 Interacting with the Operating System and the Python Try-Catch Block.mp4 45.79MB
007 Join the Student Community.html 715B
007 Loading a Tensorflow.js Model and Starting your own Server_en.vtt 33.67KB
007 Loading a Tensorflow.js Model and Starting your own Server.mp4 175.41MB
007 Working with Index Data, Pandas Series, and Dummy Variables_en.vtt 17.97KB
007 Working with Index Data, Pandas Series, and Dummy Variables.mp4 103.76MB
008 [Python] - Module Imports_en.vtt 31.45KB
008 [Python] - Module Imports.mp4 186.86MB
008 [Python] - Tuples and the Pitfalls of Optimisation (Part 2)_en.vtt 29.03KB
008 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 145.22MB
008 Adding a Favicon_en.vtt 6.50KB
008 Adding a Favicon.mp4 24.34MB
008 Any Feedback on this Section.html 512B
008 Any Feedback on this Section.html 527B
008 Download the Complete Notebook Here.html 264B
008 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems_en.vtt 12.76KB
008 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 76.55MB
008 Reading Files (Part 1) Absolute Paths and Relative Paths_en.vtt 10.29KB
008 Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 39.54MB
008 TensorFlow Sessions and Batching Data_en.vtt 18.27KB
008 TensorFlow Sessions and Batching Data.mp4 73.63MB
008 The Recall Metric_en.vtt 5.76KB
008 The Recall Metric.mp4 18.41MB
008 Understanding Descriptive Statistics the Mean vs the Median_en.vtt 10.64KB
008 Understanding Descriptive Statistics the Mean vs the Median.mp4 41.03MB
009 [Python] - Functions - Part 1 Defining and Calling Functions_en.vtt 9.15KB
009 [Python] - Functions - Part 1 Defining and Calling Functions.mp4 27.38MB
009 Any Feedback on this Section.html 526B
009 Introduction to Correlation Understanding Strength & Direction_en.vtt 7.37KB
009 Introduction to Correlation Understanding Strength & Direction.mp4 12.92MB
009 Reading Files (Part 2) Stream Objects and Email Structure_en.vtt 12.75KB
009 Reading Files (Part 2) Stream Objects and Email Structure.mp4 87.76MB
009 Styling an HTML Canvas_en.vtt 35.37KB
009 Styling an HTML Canvas.mp4 172.69MB
009 Tensorboard Summaries and the Filewriter_en.vtt 21.25KB
009 Tensorboard Summaries and the Filewriter.mp4 98.73MB
009 The Precision Metric_en.vtt 8.42KB
009 The Precision Metric.mp4 34.47MB
009 Understanding the Learning Rate_en.vtt 32.56KB
009 Understanding the Learning Rate.mp4 190.14MB
009 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques_en.vtt 25.08KB
009 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 152.54MB
010 [Python] - Functions - Part 2 Arguments & Parameters_en.vtt 18.18KB
010 [Python] - Functions - Part 2 Arguments & Parameters.mp4 99.44MB
010 Calculating Correlations and the Problem posed by Multicollinearity_en.vtt 15.48KB
010 Calculating Correlations and the Problem posed by Multicollinearity.mp4 82.51MB
010 Drawing on an HTML Canvas_en.vtt 33.21KB
010 Drawing on an HTML Canvas.mp4 159.28MB
010 Extracting the Text in the Email Body_en.vtt 5.19KB
010 Extracting the Text in the Email Body.mp4 30.49MB
010 How to Create 3-Dimensional Charts_en.vtt 22.61KB
010 How to Create 3-Dimensional Charts.mp4 152.02MB
010 The F-score or F1 Metric_en.vtt 4.47KB
010 The F-score or F1 Metric.mp4 16.46MB
010 Understanding the Tensorflow Graph Nodes and Edges_en.vtt 18.52KB
010 Understanding the Tensorflow Graph Nodes and Edges.mp4 89.48MB
010 Use the Model to Make Predictions_en.vtt 30.18KB
010 Use the Model to Make Predictions.mp4 173.85MB
011 [Python] - Functions - Part 3 Results & Return Values_en.vtt 14.46KB
011 [Python] - Functions - Part 3 Results & Return Values.mp4 54.13MB
011 [Python] - Generator Functions & the yield Keyword_en.vtt 19.48KB
011 [Python] - Generator Functions & the yield Keyword.mp4 104.22MB
011 A Naive Bayes Implementation using SciKit Learn_en.vtt 29.27KB
011 A Naive Bayes Implementation using SciKit Learn.mp4 145.69MB
011 Data Pre-Processing for Tensorflow.js_en.vtt 10.73KB
011 Data Pre-Processing for Tensorflow.js.mp4 25.56MB
011 Model Evaluation and the Confusion Matrix_en.vtt 9.60KB
011 Model Evaluation and the Confusion Matrix.mp4 40.05MB
011 Name Scoping and Image Visualisation in Tensorboard_en.vtt 23.85KB
011 Name Scoping and Image Visualisation in Tensorboard.mp4 66.81MB
011 Understanding Partial Derivatives and How to use SymPy_en.vtt 17.62KB
011 Understanding Partial Derivatives and How to use SymPy.mp4 102.67MB
011 Visualising Correlations with a Heatmap_en.vtt 21.22KB
011 Visualising Correlations with a Heatmap.mp4 108.43MB
012 [Python] - Objects - Understanding Attributes and Methods_en.vtt 25.93KB
012 [Python] - Objects - Understanding Attributes and Methods.mp4 125.35MB
012 Create a Pandas DataFrame of Email Bodies_en.vtt 6.24KB
012 Create a Pandas DataFrame of Email Bodies.mp4 37.37MB
012 Different Model Architectures Experimenting with Dropout_en.vtt 27.03KB
012 Different Model Architectures Experimenting with Dropout.mp4 173.82MB
012 Download the Complete Notebook Here.html 242B
012 Implementing Batch Gradient Descent with SymPy_en.vtt 11.26KB
012 Implementing Batch Gradient Descent with SymPy.mp4 65.49MB
012 Introduction to OpenCV_en.vtt 34.43KB
012 Introduction to OpenCV.mp4 133.24MB
012 Model Evaluation and the Confusion Matrix_en.vtt 35.46KB
012 Model Evaluation and the Confusion Matrix.mp4 193.26MB
012 Techniques to Style Scatter Plots_en.vtt 18.04KB
012 Techniques to Style Scatter Plots.mp4 83.87MB
013 [Python] - Loops and Performance Considerations_en.vtt 15.67KB
013 [Python] - Loops and Performance Considerations.mp4 106.59MB
013 A Note for the Next Lesson.html 476B
013 Any Feedback on this Section.html 509B
013 Cleaning Data (Part 1) Check for Empty Emails & Null Entries_en.vtt 15.52KB
013 Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 90.33MB
013 Download the Complete Notebook Here.html 242B
013 How to Make Sense of Python Documentation for Data Visualisation_en.vtt 23.06KB
013 How to Make Sense of Python Documentation for Data Visualisation.mp4 138.12MB
013 Prediction and Model Evaluation_en.vtt 16.75KB
013 Prediction and Model Evaluation.mp4 87.34MB
013 Resizing and Adding Padding to Images_en.vtt 24.19KB
013 Resizing and Adding Padding to Images.mp4 147.83MB
014 Any Feedback on this Section.html 521B
014 Calculating the Centre of Mass and Shifting the Image_en.vtt 32.10KB
014 Calculating the Centre of Mass and Shifting the Image.mp4 210.44MB
014 Cleaning Data (Part 2) Working with a DataFrame Index_en.vtt 7.95KB
014 Cleaning Data (Part 2) Working with a DataFrame Index.mp4 46.50MB
014 Download the Complete Notebook Here.html 242B
014 Reshaping and Slicing N-Dimensional Arrays_en.vtt 19.97KB
014 Reshaping and Slicing N-Dimensional Arrays.mp4 95.11MB
014 Working with Python Objects to Analyse Data_en.vtt 23.55KB
014 Working with Python Objects to Analyse Data.mp4 135.45MB
014 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques_en.vtt 25.00KB
014 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 175.24MB
015 [Python] - Tips, Code Style and Naming Conventions_en.vtt 14.66KB
015 [Python] - Tips, Code Style and Naming Conventions.mp4 67.18MB
015 Any Feedback on this Section.html 499B
015 Concatenating Numpy Arrays_en.vtt 7.80KB
015 Concatenating Numpy Arrays.mp4 32.48MB
015 Making a Prediction from a Digit drawn on the HTML Canvas_en.vtt 15.37KB
015 Making a Prediction from a Digit drawn on the HTML Canvas.mp4 98.46MB
015 Saving a JSON File with Pandas_en.vtt 6.09KB
015 Saving a JSON File with Pandas.mp4 43.41MB
015 Understanding Multivariable Regression_en.vtt 6.57KB
015 Understanding Multivariable Regression.mp4 31.51MB
016 Adding the Game Logic_en.vtt 33.58KB
016 Adding the Game Logic.mp4 158.22MB
016 Data Visualisation (Part 1) Pie Charts_en.vtt 14.02KB
016 Data Visualisation (Part 1) Pie Charts.mp4 70.09MB
016 Download the Complete Notebook Here.html 242B
016 How to Shuffle and Split Training & Testing Data_en.vtt 9.99KB
016 How to Shuffle and Split Training & Testing Data.mp4 45.09MB
016 Introduction to the Mean Squared Error (MSE)_en.vtt 11.02KB
016 Introduction to the Mean Squared Error (MSE).mp4 43.24MB
017 Any Feedback on this Section.html 513B
017 Data Visualisation (Part 2) Donut Charts_en.vtt 8.31KB
017 Data Visualisation (Part 2) Donut Charts.mp4 40.95MB
017 Publish and Share your Website!_en.vtt 8.19KB
017 Publish and Share your Website!.mp4 33.31MB
017 Running a Multivariable Regression_en.vtt 8.53KB
017 Running a Multivariable Regression.mp4 40.20MB
017 Transposing and Reshaping Arrays_en.vtt 11.69KB
017 Transposing and Reshaping Arrays.mp4 58.02MB
018 Any Feedback on this Section.html 500B
018 How to Calculate the Model Fit with R-Squared_en.vtt 3.84KB
018 How to Calculate the Model Fit with R-Squared.mp4 21.48MB
018 Implementing a MSE Cost Function_en.vtt 11.74KB
018 Implementing a MSE Cost Function.mp4 54.55MB
018 Introduction to Natural Language Processing (NLP)_en.vtt 7.22KB
018 Introduction to Natural Language Processing (NLP).mp4 37.45MB
019 Introduction to Model Evaluation_en.vtt 3.31KB
019 Introduction to Model Evaluation.mp4 7.34MB
019 Tokenizing, Removing Stop Words and the Python Set Data Structure_en.vtt 16.58KB
019 Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 92.53MB
019 Understanding Nested Loops and Plotting the MSE Function (Part 1)_en.vtt 12.12KB
019 Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 48.88MB
020 Improving the Model by Transforming the Data_en.vtt 18.78KB
020 Improving the Model by Transforming the Data.mp4 81.41MB
020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2)_en.vtt 14.98KB
020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 96.91MB
020 Word Stemming & Removing Punctuation_en.vtt 9.39KB
020 Word Stemming & Removing Punctuation.mp4 46.64MB
021 How to Interpret Coefficients using p-Values and Statistical Significance_en.vtt 9.48KB
021 How to Interpret Coefficients using p-Values and Statistical Significance.mp4 49.02MB
021 Removing HTML tags with BeautifulSoup_en.vtt 9.73KB
021 Removing HTML tags with BeautifulSoup.mp4 86.57MB
021 Running Gradient Descent with a MSE Cost Function_en.vtt 19.24KB
021 Running Gradient Descent with a MSE Cost Function.mp4 74.32MB
022 Creating a Function for Text Processing_en.vtt 7.21KB
022 Creating a Function for Text Processing.mp4 26.36MB
022 Understanding VIF & Testing for Multicollinearity_en.vtt 22.29KB
022 Understanding VIF & Testing for Multicollinearity.mp4 105.42MB
022 Visualising the Optimisation on a 3D Surface_en.vtt 9.35KB
022 Visualising the Optimisation on a 3D Surface.mp4 35.61MB
023 A Note for the Next Lesson.html 476B
023 Download the Complete Notebook Here.html 242B
023 Model Simplification & Baysian Information Criterion_en.vtt 20.18KB
023 Model Simplification & Baysian Information Criterion.mp4 119.72MB
024 Advanced Subsetting on DataFrames the apply() Function_en.vtt 11.79KB
024 Advanced Subsetting on DataFrames the apply() Function.mp4 55.26MB
024 Any Feedback on this Section.html 520B
024 How to Analyse and Plot Regression Residuals_en.vtt 12.85KB
024 How to Analyse and Plot Regression Residuals.mp4 28.05MB
025 [Python] - Logical Operators to Create Subsets and Indices_en.vtt 13.40KB
025 [Python] - Logical Operators to Create Subsets and Indices.mp4 57.42MB
025 Residual Analysis (Part 1) Predicted vs Actual Values_en.vtt 15.74KB
025 Residual Analysis (Part 1) Predicted vs Actual Values.mp4 81.48MB
026 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals_en.vtt 19.68KB
026 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 99.19MB
026 Word Clouds & How to install Additional Python Packages_en.vtt 10.49KB
026 Word Clouds & How to install Additional Python Packages.mp4 50.04MB
027 Creating your First Word Cloud_en.vtt 12.01KB
027 Creating your First Word Cloud.mp4 45.54MB
027 Making Predictions (Part 1) MSE & R-Squared_en.vtt 20.51KB
027 Making Predictions (Part 1) MSE & R-Squared.mp4 126.58MB
028 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals_en.vtt 12.88KB
028 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 63.72MB
028 Styling the Word Cloud with a Mask_en.vtt 14.50KB
028 Styling the Word Cloud with a Mask.mp4 105.94MB
029 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays_en.vtt 17.87KB
029 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 102.57MB
029 Solving the Hamlet Challenge_en.vtt 5.21KB
029 Solving the Hamlet Challenge.mp4 46.79MB
030 [Python] - Conditional Statements - Build a Valuation Tool (Part 2)_en.vtt 18.44KB
030 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 90.12MB
030 Styling Word Clouds with Custom Fonts_en.vtt 12.88KB
030 Styling Word Clouds with Custom Fonts.mp4 99.44MB
031 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module_en.vtt 24.52KB
031 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 200.93MB
031 Create the Vocabulary for the Spam Classifier_en.vtt 15.40KB
031 Create the Vocabulary for the Spam Classifier.mp4 70.09MB
032 Coding Challenge Check for Membership in a Collection_en.vtt 5.31KB
032 Coding Challenge Check for Membership in a Collection.mp4 14.85MB
032 Download the Complete Notebook Here.html 242B
033 Any Feedback on this Section.html 512B
033 Coding Challenge Find the Longest Email_en.vtt 6.55KB
033 Coding Challenge Find the Longest Email.mp4 41.10MB
034 Sparse Matrix (Part 1) Split the Training and Testing Data_en.vtt 13.17KB
034 Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 58.08MB
035 Sparse Matrix (Part 2) Data Munging with Nested Loops_en.vtt 19.39KB
035 Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 91.56MB
036 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files_en.vtt 10.46KB
036 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 61.30MB
037 Coding Challenge Solution Preparing the Test Data_en.vtt 3.93KB
037 Coding Challenge Solution Preparing the Test Data.mp4 18.88MB
038 Checkpoint Understanding the Data_en.vtt 11.87KB
038 Checkpoint Understanding the Data.mp4 74.56MB
039 Download the Complete Notebook Here.html 242B
040 Any Feedback on this Section.html 519B
18162714-ML-Data-Science-Syllabus.pdf 103.97KB
18175084-01-Linear-Regression-checkpoint.ipynb.zip 37.64KB
18175146-01-Linear-Regression-complete.ipynb.zip 75.28KB
18179882-02-Python-Intro.ipynb.zip 36.44KB
18179908-03-Gradient-Descent.ipynb.zip 1.14MB
18179918-04-Multivariable-Regression.ipynb.zip 3.54MB
18179924-06-Bayes-Classifier-Pre-Processing.ipynb.zip 978.02KB
18179928-04-Valuation-Tool.ipynb.zip 2.93KB
18180042-07-Bayes-Classifier-Training.ipynb.zip 5.82KB
18180294-07-Bayes-Classifier-Testing-Inference-Evaluation.ipynb.zip 243.05KB
18180296-08-Naive-Bayes-with-scikit-learn.ipynb.zip 13.26KB
18180490-09-Neural-Nets-Pretrained-Image-Classification.ipynb.zip 571.83KB
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