Path Machine Learning Literacy (2021)

[AD-TEAM]

1.67 GB | 00:06:26 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

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