Pluralsight - Machine Learning Literacy

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mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:1.66 GB

Files Included :

1 Course Overview.mp4 (4.54 MB)
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1 Overview.mp4 (1.92 MB)
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2 What to Expect.mp4 (1.47 MB)
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3 On Machine Learning.mp4 (5.87 MB)
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4 What Is Different About Machine Learning.mp4 (4.03 MB)
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5 Learning Types.mp4 (11.29 MB)
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6 Machine Learning Pipeline.mp4 (9.47 MB)
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7 Problem Definition.mp4 (4.73 MB)
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8 Introducing Google Collaboratory.mp4 (8.3 MB)
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9 Summary.mp4 (1.2 MB)
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1 Overview.mp4 (1.45 MB)
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2 Revisiting ML Pipeline.mp4 (1.5 MB)
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3 Understanding Data Sourcing.mp4 (8.29 MB)
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4 CSV Format.mp4 (1.93 MB)
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5 Understanding SciPy.mp4 (6.54 MB)
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6 Demo - Loading Data into Pandas.mp4 (8.88 MB)
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7 Summary.mp4 (1.12 MB)
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01 Overview.mp4 (1.4 MB)
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02 Revisiting ML Pipeline.mp4 (2.14 MB)
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03 Introducing Data Analysis.mp4 (5.5 MB)
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04 Univariant Numerical Analysis.mp4 (12.54 MB)
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05 Bivariant Numerical Analysis.mp4 (7.93 MB)
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06 Demo - Descriptive Stats - Part One.mp4 (14.14 MB)
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07 Demo - Descriptive Stats - Part Two.mp4 (10.01 MB)
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08 Data Visualization.mp4 (10.63 MB)
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09 Demo - Data Visualization - Part One.mp4 (11.67 MB)
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10 Demo - Data Visualization - Part Two.mp4 (10.4 MB)
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11 Summary.mp4 (1021.43 KB)
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01 Overview.mp4 (1.88 MB)
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02 Revisting ML Pipeline.mp4 (2.81 MB)
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03 Data Scaling - The Problem.mp4 (11.24 MB)
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04 Data Scaling - The Solution.mp4 (4.32 MB)
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05 The Need for Data Segregation.mp4 (5.94 MB)
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06 Train Test Split.mp4 (5.05 MB)
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07 KFlod Cross Validation.mp4 (5.25 MB)
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08 Welcoming scikit-learn.mp4 (3.67 MB)
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09 Demo - Data Segregation Techniques.mp4 (8.48 MB)
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10 Summary.mp4 (1.71 MB)
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01 Overview.mp4 (1.5 MB)
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02 Revisiting ML Pipeline.mp4 (1.72 MB)
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03 Scoping Your Focus.mp4 (9.7 MB)
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04 Introducing Derivatives.mp4 (6.81 MB)
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05 Linear Regression.mp4 (5.49 MB)
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06 Variance Bias Tradeoff.mp4 (8.58 MB)
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07 Other Regression Algorithms.mp4 (3.19 MB)
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08 Model Evaluation.mp4 (4.32 MB)
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09 Demo - Deploying and Testing the Model - Part 1.mp4 (18.61 MB)
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10 Demo - Deploying and Testing the Model - Part 2.mp4 (15.76 MB)
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11 Summary.mp4 (2.87 MB)
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1 Overview.mp4 (1.74 MB)
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2 Handling Features.mp4 (2.95 MB)
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3 Model Improvement.mp4 (2.14 MB)
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4 Automated ML.mp4 (6.45 MB)
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5 Operationalization.mp4 (2.63 MB)
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6 Team Data Science Process.mp4 (4.21 MB)
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7 Summary.mp4 (3.74 MB)
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1 Course Overview.mp4 (3.23 MB)
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01 Version Check.mp4 (552.03 KB)
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02 Module Overview.mp4 (1.93 MB)
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03 Prerequisites and Course Outline.mp4 (2.27 MB)
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04 The Need for Data Preparation.mp4 (6.1 MB)
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05 Insufficient Data.mp4 (10.02 MB)
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06 Too Much Data.mp4 (6.35 MB)
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07 Non-representative Data, Missing Values, Outliers, Duplicates.mp4 (3.55 MB)
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08 Dealing with Missing Data.mp4 (7.56 MB)
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09 Dealing with Outliers.mp4 (8.17 MB)
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10 Oversampling and Undersampling to Balance Datasets.mp4 (7.13 MB)
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11 Overfitting and Underfitting.mp4 (4.24 MB)
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12 Module Summary.mp4 (2.14 MB)
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01 Module Overview.mp4 (1.83 MB)
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02 Handling Missing Values.mp4 (12.87 MB)
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03 Cleaning Data.mp4 (15.02 MB)
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04 Visualizing Relationships.mp4 (8.4 MB)
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05 Building a Regression Model.mp4 (14.85 MB)
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06 Univariate Feature Imputation Using the Simple Imputer.mp4 (14.99 MB)
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07 Multivariate Feature Imputation Using the Iterative Imputer.mp4 (12.14 MB)
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08 Missing Value Indicator.mp4 (3.97 MB)
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09 Feature Imputation as a Part of an Machine Learning Pipeline.mp4 (7.85 MB)
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10 Module Summary.mp4 (2.06 MB)
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01 Module Overview.mp4 (3.97 MB)
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02 Numeric Data.mp4 (8.04 MB)
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03 Scaling and Standardizing Features.mp4 (9.3 MB)
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04 Normalizing and Binarizing Features.mp4 (12.24 MB)
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05 Categorical Data.mp4 (4.89 MB)
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06 Numeric Encoding of Categorical Data.mp4 (7.27 MB)
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07 Label Encoding and One-hot Encoding.mp4 (15.24 MB)
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08 Discretization of Continuous Values Using Pandas Cut.mp4 (6.48 MB)
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09 Discretization of Continuous Values Using the KBins Discretizer.mp4 (7.42 MB)
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10 Building a Regression Model with Discretized Data.mp4 (6.72 MB)
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11 Module Summary.mp4 (1.88 MB)
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1 Module Overview.mp4 (1.82 MB)
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2 The Curse of Dimensionality.mp4 (7.77 MB)
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3 Reducing Complexity in Data.mp4 (4.73 MB)
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4 Feature Selection to Reduce Dimensions.mp4 (5.56 MB)
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5 Filter Methods.mp4 (6.39 MB)
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6 Embedded Methods.mp4 (7.64 MB)
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7 Module Summary.mp4 (2.04 MB)
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1 Module Overview.mp4 (1.84 MB)
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2 Feature Correlations.mp4 (17.28 MB)
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3 Using the Correlation Matrix to Detect Multi-collinearity.mp4 (10.35 MB)
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4 Using Variance Inflation Factor to Detect Multi-collinearity.mp4 (6.57 MB)
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5 Features Selection Using Missing Values Threshold and Variance Threshold.mp4 (13.08 MB)
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6 Univariate Feature Selection Using Chi2 and ANOVA.mp4 (14.09 MB)
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7 Feature Selection Using Wrapper Methods.mp4 (16.17 MB)
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8 Feature Selection Using Embedded Methods.mp4 (7.74 MB)
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9 Module Summary.mp4 (1.98 MB)
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1 Course Overview.mp4 (3.22 MB)
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01 Module Overview.mp4 (1.9 MB)
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02 Prerequisites and Course Outline.mp4 (2.53 MB)
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03 A Case Study - Sentiment Analysis.mp4 (10.11 MB)
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04 Sentiment Analysis as a Binary Classification Problem.mp4 (3.59 MB)
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05 Rule Based vs ML Based Analysis.mp4 (9.98 MB)
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06 Traditional Machine Learning Systems.mp4 (6.82 MB)
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07 Representation Machine Learning Systems.mp4 (3.76 MB)
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08 Deep Learning and Neural Networks.mp4 (8.48 MB)
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09 Traditional ML vs Deep Learning.mp4 (4.91 MB)
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10 Traditional ML Algorithms and Neural Network Design.mp4 (6.68 MB)
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11 Module Summary.mp4 (2.1 MB)
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1 Module Overview.mp4 (1.95 MB)
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2 Choosing the Right Machine Learning Problem.mp4 (9.91 MB)
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3 Supervised and Unsupervised Learning.mp4 (13.05 MB)
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4 Reinforcement Learning.mp4 (10.86 MB)
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5 Recommendation Systems.mp4 (6.7 MB)
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6 Module Summary.mp4 (2.17 MB)
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01 Module Overview.mp4 (2.89 MB)
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02 Regression Models.mp4 (3.44 MB)
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03 Choosing Regression Algorithms.mp4 (6.62 MB)
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04 Evaluating Regression Models.mp4 (8.08 MB)
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05 Types of Classification.mp4 (6.02 MB)
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06 Choosing Classification Algorithms.mp4 (4.72 MB)
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07 Evaluating Classifiers.mp4 (6.41 MB)
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08 Clustering Models.mp4 (8.96 MB)
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09 The Curse of Dimensionality.mp4 (8.58 MB)
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10 Dimensionality Reduction Techniques.mp4 (4.21 MB)
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11 Module Summary.mp4 (1.81 MB)
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01 Module Overview.mp4 (1.99 MB)
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02 Install and Set Up.mp4 (3.57 MB)
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03 Exploring the Regression Dataset.mp4 (5.81 MB)
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04 Simple Regression Using Analytical and Machine Learning Techniques.mp4 (10.61 MB)
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05 Multiple Regression Using Analytical and Machine Learning Techniques.mp4 (4.73 MB)
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06 Exploring the Classification Dataset.mp4 (7.77 MB)
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07 Classification Using Logistic Regression.mp4 (10.48 MB)
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08 Classification Using Decision Trees.mp4 (6.8 MB)
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09 Clustering Using K-means.mp4 (15.06 MB)
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10 Dimensionality Reduction Using Principal Component Analysis.mp4 (9.28 MB)
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11 Dimensionality Reduction Using Manifold Learning.mp4 (13.19 MB)
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12 Module Summary.mp4 (2.1 MB)
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1 Module Overview.mp4 (1.77 MB)
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2 The Machine Learning Workflow.mp4 (6.75 MB)
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3 Case Study - PyTorch on the Cloud.mp4 (8.32 MB)
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4 Ensemble Learning.mp4 (10.05 MB)
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5 Averaging and Boosting, Voting and Stacking.mp4 (3.68 MB)
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6 Custom Neural Networks - Their Characteristics and Applications.mp4 (5.83 MB)
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7 Module Summary.mp4 (1.98 MB)
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1 Module Overview.mp4 (1.71 MB)
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2 Classification Using Hard Voting and Soft Voting.mp4 (11.33 MB)
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3 Exploring and Preprocessing the Regression Dataset.mp4 (6.4 MB)
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4 Regression Using Bagging and Pasting.mp4 (9.81 MB)
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5 Regression Using Gradient Boosting.mp4 (9.02 MB)
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6 Regression Using Neural Networks.mp4 (14.34 MB)
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7 Summary and Further Study.mp4 (2.57 MB)
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1 Course Overview.mp4 (3.96 MB)
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01 Module Overview.mp4 (2.08 MB)
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02 Prerequisites and Course Outline.mp4 (1.67 MB)
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03 Rule-based vs ML-based Learning.mp4 (11.32 MB)
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04 Traditional ML vs Representation ML.mp4 (5.86 MB)
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05 The Machine Learning Workflow.mp4 (5.06 MB)
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06 Choosing the Right Model Based on Data.mp4 (8.73 MB)
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07 Supervised vs Unsupervised Learning.mp4 (7.76 MB)
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08 Transfer Learning, Cold Start ML and Warm Start ML.mp4 (8.4 MB)
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09 Popular Machine Learning Frameworks.mp4 (5.44 MB)
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10 Demo - Getting Started with scikit-learn.mp4 (3.72 MB)
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11 Module Summary.mp4 (2.23 MB)
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01 Module Overview.mp4 (1.99 MB)
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02 Building and Evaluating Regression Models.mp4 (7.8 MB)
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03 Demo - Linear Regression Using Numeric Features.mp4 (15.19 MB)
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04 Demo - Exploring Regression Data.mp4 (8.37 MB)
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05 Demo - Preprocessing Numeric and Categorical Data and Fitting a Regression Model.mp4 (8.95 MB)
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06 Choosing Regression Algorithms.mp4 (4.31 MB)
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07 Regularized Regression Models - Lasso, Ridge, and Elastic Net.mp4 (6.09 MB)
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08 Stochastic Gradient Descent.mp4 (3.6 MB)
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09 Demo - Multiple Types of Regression.mp4 (10.48 MB)
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10 Module Summary.mp4 (2.18 MB)
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01 Module Overview.mp4 (1.89 MB)
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02 Types of Classifiers.mp4 (6.6 MB)
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03 Understanding Logistic Regression Intuitively.mp4 (8.38 MB)
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04 Demo - Building and Training a Binary Classification Model.mp4 (11.85 MB)
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05 Understanding Support Vector and Nearest Neighbors Classification.mp4 (6.34 MB)
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06 Understanding Decision Tree and Naive Bayes Classification.mp4 (8.29 MB)
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07 Demo - Building Classification Models Using Multiple Techniques.mp4 (13.24 MB)
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08 Demo - Using Warm Start with an Ensemble Classifier.mp4 (5.69 MB)
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09 Demo - Performing Multiclass Classification on Text Data.mp4 (12.84 MB)
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10 Module Summary.mp4 (1.66 MB)
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01 Module Overview.mp4 (1.85 MB)
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02 Clustering as an Unsupervised Learning Technique.mp4 (6.53 MB)
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03 Choosing Clustering Algorithms.mp4 (6.27 MB)
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04 Categorizing Clustering Algorithms.mp4 (4.81 MB)
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05 K-means Clustering.mp4 (4.19 MB)
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06 Hierarchical Clustering.mp4 (5.72 MB)
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07 Demo - Performing K-means Clustering on Unlabeled Data.mp4 (10.19 MB)
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08 Demo - Clustering Using Labeled Data.mp4 (15.61 MB)
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09 Demo - Agglomerative Clustering.mp4 (18.2 MB)
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10 Summary and Further Study.mp4 (2.1 MB)
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1 Course Overview.mp4 (3.28 MB)
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2 Version Check.mp4 (550.5 KB)
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01 Module Overview.mp4 (2.32 MB)
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02 Prerequisites and Course Outline.mp4 (1.83 MB)
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03 The Classic Machine Learning Workflow.mp4 (5.1 MB)
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04 New Realities of Deployed Models.mp4 (10.38 MB)
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05 Overfitting.mp4 (6.6 MB)
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06 Training-serving Skew.mp4 (9.56 MB)
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07 Concept Drift.mp4 (9.44 MB)
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08 Concerted Adversaries.mp4 (3.66 MB)
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09 Deploying Machine Learning Models.mp4 (3.74 MB)
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10 Module Summary.mp4 (2.25 MB)
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01 Module Overview.mp4 (1.77 MB)
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02 Serializing Model Parameters.mp4 (5.04 MB)
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03 Demo - Serializing and Deserializing Models Using JSON.mp4 (15.96 MB)
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04 Demo - Using Pickle and Joblib to Serialize and Deserialize Models.mp4 (10.57 MB)
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05 Demo - Checkpointing Models and Resuming Training from a Checkpoint.mp4 (11.91 MB)
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06 Demo - Serializing Pre-processors and Models.mp4 (11.36 MB)
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07 Demo - Serializing Pipelines.mp4 (4.03 MB)
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08 Using Flask for Model Deployment.mp4 (3.19 MB)
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09 Demo - Deploying a Model for Prediction Using Flask.mp4 (13.1 MB)
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10 Module Summary.mp4 (1.93 MB)
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1 Module Overview.mp4 (2.14 MB)
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2 Event-driven Serverless Compute.mp4 (7.53 MB)
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3 Demo - Serializing Classification Models.mp4 (8.03 MB)
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4 Demo - Uploading Pickle Files to Cloud Storage.mp4 (11.28 MB)
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5 Demo - Deploying a Model to Google Cloud Functions.mp4 (13.53 MB)
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6 Demo - Performing Predictions Using Cloud Functions.mp4 (9.46 MB)
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7 Module Summary.mp4 (1.86 MB)
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01 Module Overview.mp4 (2.01 MB)
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02 Introducing the Google AI Platform.mp4 (9.79 MB)
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03 Demo - Getting Started with Cloud AI Platform.mp4 (7.13 MB)
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04 Demo - Creating a Model and a Version.mp4 (10.4 MB)
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05 Demo - Scheduling an Evaluation Job to Sample Prediction Instances.mp4 (12.34 MB)
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06 Demo - Testing the Deployed Model Using the Web Console.mp4 (6 MB)
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07 Demo - Model Predictions Using the gcloud Command Line Utility.mp4 (6.16 MB)
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08 Demo - Invoking the Predictions API Using cURL.mp4 (10.67 MB)
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09 Demo - Monitoring Deployed Models Using Stackdriver.mp4 (18.04 MB)
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10 Module Summary.mp4 (2.05 MB)
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01 Module Overview.mp4 (2.17 MB)
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02 Introducing Amazon SageMaker.mp4 (3.68 MB)
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03 Training a Model on SageMaker.mp4 (4.31 MB)
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04 Deploying a Model on SageMaker.mp4 (5.65 MB)
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05 Demo - Creating a SageMaker Notebook Instance.mp4 (14.59 MB)
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06 Demo - Getting Started with SageMaker for Distributed Training.mp4 (5.81 MB)
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07 Demo - Tensor Flow Script for Distributed Training.mp4 (15.59 MB)
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08 Demo - Distributed Training Using the SageMaker Tensor Flow Estimator.mp4 (17.27 MB)
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09 Demo - Deploying the Model for Predictions.mp4 (14.75 MB)
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10 Demo - Auditing and Compliance Using Cloud Trail.mp4 (10.15 MB)
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11 Summary and Further Study.mp4 (2.53 MB)
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