Description
The Supervised Machine Learning Bootcamp course. Why should you consider a supervised machine learning course? The supervised machine learning algorithms you’ll learn here are some of the most powerful data science tools you’ll need to solve regression and classification tasks. These are valuable skills that anyone who wants to work as a machine learning engineer and data scientist should have in their toolbox. Simple Bayes, KNN, Support Vector Machines, Decision Trees, Random Forests, Ridge and Lasso Regression. In this course, you’ll learn the theory behind all 6 algorithms and then apply your skills to practical case studies tailored to each of them using the Python Science Kit learning library. First, we cover simple Bayes – a powerful technique based on Bayesian statistics. Its strength is that it excels at performing tasks in real time. Some of the most common uses are filtering spam emails, flagging inappropriate comments on social media, or performing sentiment analysis. In this course, we have a working example of exactly how it works, so stay tuned! Next is K-nearest-neighbors – one of the most widely used machine learning algorithms. Why is that? Because of its simplicity when using distance-based criteria for accurate prediction. We follow decision tree algorithms, which will serve as the basis for our next topic – random forests. They are powerful learners that can harness the power of multiple decision trees to make accurate predictions. After that, we come across Support Vector Machines – classification and regression models that are able to use different kernels to solve a wide variety of problems. In the practical part of this section, we will build a model to classify mushrooms as poisonous or edible. Exciting! Finally, you will be introduced to Ridge and Lasso Regression – they are regularization algorithms that improve the mechanism of linear regression by limiting the strength of individual features and preventing overfitting. We will discuss the differences and similarities as well as the advantages and disadvantages of both regression techniques. Each section of the course is organized in a consistent manner for an optimal learning experience:
– We start with the fundamental theory for each algorithm. To enhance your understanding of the topic, we will guide you through a theoretical case and also introduce the mathematical formulas behind the algorithm.
– Then we go to the construction of the model to solve the practical problem with it. This work is done using the famous Sklaren Python library.
– We analyze the performance of our models with the help of metrics such as accuracy, precision, recall and F1 score.
– We also study various techniques such as network search and cross-validation to improve model performance.
To top it all off, we have a series of complementary exercises and quizzes so you can expand your skill set. Not only that, but we also provide comprehensive course materials to guide you through the course that you can refer to anytime. These lessons are created in the unique 365 teaching style that many of you are familiar with. Our goal is to present complex topics in an easy and understandable way, with a focus on practical application and visual learning. With the power of animations, quizzes, exercises and well-crafted course notes, the supervised machine learning course meets all your learning needs.
What you will learn in The Supervised Machine Learning Bootcamp
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Regression and classification algorithms
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Using sk-learn and Python to implement supervised machine learning techniques
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K-nearest neighbors for classification and regression
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Ridge and lasso regression
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Random forests
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Support vector machines
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Practical case studies to train, test and evaluate and improve model performance
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Cross-validation to optimize parameters
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Learn to use metrics such as precision, recall, F1 score as well as confusion matrix to evaluate the actual performance of the model.
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You will dive into the theoretical foundations behind each algorithm with the help of intuitive explanations of mathematical formulas and concepts.
This course is suitable for people who
- Aspiring data scientists and machine learning engineers
- Data scientists and data analysts are looking to upgrade their skill sets
- Anyone who wants to gain an understanding of the field of machine learning and its vast opportunities
Course details of The Supervised Machine Learning Bootcamp
- Publisher: Udemy
- teacher: 365 Careers
- Training level: beginner to advanced
- Training duration: 5 hours and 51 minutes
- Number of courses: 83
Course headings
Prerequisites of The Supervised Machine Learning Bootcamp course
- The course is open to everyone who wants to learn data science.
- You’ll need to install Anaconda and Jupyter Notebook. We will show you how to do that step by step.
Course images
Sample video of the course
Installation guide
After Extract, view with your favorite Player.
Subtitle: None
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
2.6 GB
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