Machine & Deep Learning Algorithms: Introduction
Machine Learning
| Beginner
- 7 videos | 45m 19s
- Includes Assessment
- Earns a Badge
Examine fundamentals of machine learning (ML) and how Pandas ML can be used to build ML models in this 7-video course. The working of Support Vector Machines to perform classification of data are also covered. Begin by learning about different kinds of machine learning algorithms, such as regression, classification, and clustering, as well as their specific applications. Then look at the process involved in learning relationships between input and output during the training phase of ML. This leads to an introduction to Pandas ML, and the benefits of combining Pandas, scikit-learn, and XGBoost into a single library to ease the task of building and evaluating ML models. You will learn about Support Vector Machines, which are a supervised machine learning algorithm, and how they are used to find a hyperplane to divide data points into categories. Learners then study the concept of overfitting in machine learning, and the problems associated with a model overfitted to training data. and how to mitigate the issue. The course concludes with an exercise in machine learning and classification.
WHAT YOU WILL LEARN
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Recognize the different kinds of machine learning algorithms such as regression, classification, and clustering, as well as their specific applications
Describe the process involved in learning a relationship between input and output during the training phase of machine learning
Identify the benefits of combining pandas, scikit-learn, and xgboost into a single library to ease the task of building and evaluating ml models -
Describe what support vector machines are and how they are used to find a hyperplane to divide data points into categories
Recognize the problems associated with a model that is overfitted to training data and how to mitigate the issue
Define what unsupervised learning is, list the features of svms, and describe the issues one may run into when using an overfitted model for predictions
IN THIS COURSE
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1m 58s
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8m 39sUpon completion of this video, you will be able to recognize the different kinds of machine learning algorithms, such as regression, classification, and clustering, as well as their specific applications. FREE ACCESS
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3. How Machine Learning Works7m 19sUpon completion of this video, you will be able to describe the process involved in learning a relationship between input and output during the training phase of machine learning. FREE ACCESS
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4. Introduction to Pandas ML5m 45sIn this video, you will learn how to identify the benefits of combining Pandas, scikit-learn, and XGBoost into a single library. This will ease the task of building and evaluating ML models. FREE ACCESS
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5. Support Vector Machines6m 7sAfter completing this video, you will be able to describe what Support Vector Machines are and how they are used to find a hyperplane to divide data points into categories. FREE ACCESS
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6. Overfitting7m 48sUpon completion of this video, you will be able to recognize problems associated with a model that is overfitted to training data and how to mitigate the issue. FREE ACCESS
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7. Exercise: Machine Learning and Classification7m 44sIn this video, you will learn how to define unsupervised learning, list the features of SVMs, and describe the issues one may run into when using an overfitted model for predictions. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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