Course: Machine & Deep Learning Algorithms
duration: 7 hours |
Language: English (US) |
access duration: 90 days |
Details
This course explores the fundamentals of machine & deep learning algorithms. 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 machine learning. Then study an introduction to regression and how it works. And take a look at the characteristics of regression such as simplicity and versatility, which have led to widespread adoption of this technique in a number of different fields. You’ ll examine how to load data from a CSV (comma-separated values) file into a Pandas data frame and prepare the data for training a classification model. Then use the scikit-learn library to build and train a LinearSVC classification model and evaluate its performance with available model evaluation functions. Finally, you’ll learn about the imbalanced-learn library that integrates with Pandas ML (machine learning). This offers several techniques to address the imbalance in datasets used for classification.
Result
After completing this course, you are familiar with the basics of machine & deep learning algorithms. You are ready to implement this knowledge in your daily tasks.
Prerequisites
You have basic IT knowledge.
Content
Machine & Deep Learning Algorithms
Machine & Deep Learning Algorithms: Introduction
Examine the fundamentals of machine learning and how Pandas ML can be used to build ML models. The workings of Support Vector Machines to perform classification of data is also covered.
Machine & Deep Learning Algorithms: Regression & Clustering
Explore the fundamentals of regression and clustering and discover how to use a confusion matrix to evaluate classification models.
Machine & Deep Learning Algorithms: Data Preperation in Pandas ML
Classification, regression, and clustering are some of the most commonly used machine learning techniques and there are various algorithms available for these tasks. Explore their application in Pandas ML.
Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML
The imbalanced-learn library that integrates with Pandas ML offers several techniques to address the imbalance in datasets used for classification. Explore oversampling, undersampling, and a combination of these techniques.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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What is included?
Certificate of participation | Yes |
Monitor Progress | Yes |
Award Winning E-learning | Yes |
Mobile ready | Yes |
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