Search result for Model training and evaluation Online Courses & Certifications
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Customising your models with TensorFlow 2
by Dr Kevin Webster- 4.8
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Approx. 27 hours to complete
You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. Multiple inputs and outputs [Coding tutorial] Multiple inputs and outputs [Coding tutorial] Variables and Tensors Model subclassing and custom training loops Welcome to week 4 - Model subclassing and custom training loops...
Advanced Computer Vision with TensorFlow
by Laurence Moroney , Eddy Shyu- 4.8
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Approx. 24 hours to complete
d) Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet. Data Prep and Training Overview Evaluation with IoU and Dice Score...
Production Machine Learning Systems
by Google Cloud Training- 4.6
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Approx. 8 hours to complete
In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlow The Components of an ML System: Tuner + Model Evaluation and Validation Training Design Decisions Ingesting data for Cloud-based analytics and ML...
AI Workflow: Machine Learning, Visual Recognition and NLP
by Mark J Grover , Ray Lopez, Ph.D.- 4.5
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Approx. 14 hours to complete
The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. Discuss common regression, classification, and multilabel classification metrics Model Evaluation and Performance Metrics...
Custom and Distributed Training with TensorFlow
by Laurence Moroney , Eddy Shyu- 4.8
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Approx. 24 hours to complete
• Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training. Broadcasting, operator overloading and Numpy compatibility Evaluating variables and changing data types Persistent=true and higher order gradients Connect with your mentors and fellow learners on Slack! Define Training Loop and Validate Model Training steps and data pipeline...
Applied Machine Learning in Python
by Kevyn Collins-Thompson- 4.6
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Approx. 34 hours to complete
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Model Evaluation & Selection Model Selection: Optimizing Classifiers for Different Evaluation Metrics...
Machine Learning With Big Data
by Mai Nguyen , Ilkay Altintas- 4.6
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Approx. 22 hours to complete
This course provides an overview of machine learning techniques to explore, analyze, and leverage data. Summary of Big Data Integration and Processing Goals and Activities in the Machine Learning Process Building and Applying a Classification Model Slides: Model evaluation metrics and methods Model Evaluation Model Evaluation in KNIME and Spark Quiz...
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Cluster Analysis, Association Mining, and Model Evaluation
by Dursun Delen , Julie Pai- 0.0
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Approx. 4 hours to complete
Welcome to Cluster Analysis, Association Mining, and Model Evaluation. We will also explain how a model can be evaluated for performance, and review the differences in analysis types and when to apply them. Evaluating Model Performance Modules 3 and 4 Cluster analysis and segmentation Collaborative filtering and market basket analysis Applications of classification- and regression-type prediction models...
Data Science at Scale - Capstone Project
by Bill Howe- 4.1
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Approx. 6 hours to complete
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Week 3: Construct a training dataset Week 4: Train and evaluate a simple model Milestone: Train a Simple Model...
Supervised Learning: Regression
by Mark J Grover , Miguel Maldonado- 4.8
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Approx. 11 hours to complete
Introduction to Supervised Machine Learning and Linear Regression Training and Test Splits Training and Test Splits Lab - Part 1 Training and Test Splits Lab - Part 2 Training and Test Splits Lab - Part 3 Training and Test Splits Lab - Part 4 Training and Test Splits Demo Regularization and Model Selection...
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