Description
Supervised Machine Learning Bootcamp Course. Why should you consider the Supervised Machine Learning course? The supervised machine learning algorithms you will learn here are some of the most powerful data science tools you will need to solve regression and classification tasks. These are valuable skills that anyone looking 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 will learn the theory behind all 6 algorithms and then apply your skills to practical case studies corresponding to each of them using the Python Science Kit Learn 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 how it actually works, so stay tuned! Next is K-Nearest-Neighbors – one of the most widely used machine learning algorithms. Why so? Because of its simplicity when using a distance-based criterion for accurate prediction. We follow with 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 get Support Vector Machines – classification and regression models that are able to use different kernels to solve different types 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 coherent manner for the best 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 you to the mathematical formulas behind the algorithms.
– We then move on to building the model to solve a practical problem. This task is done using the famous sklearn 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 like network search and cross-validation to improve model performance.
Above all, we have a range of supplementary exercises and quizzes so that 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 will meet all your learning needs.
What You Will Learn at the Supervised Machine Learning Bootcamp
-
Regression and Classification Algorithms
-
Using SK-Learn and Python to implement supervised machine learning techniques
-
K-Nearest Neighbors for Classification and Regression
-
Ridge and Lasso Regression
-
Practical case studies to train, test, and evaluate and improve model performance
-
Cross validation to optimize parameters
-
Learn how to use metrics like precision, recall, F1 score as well as confusion matrix to evaluate the actual performance of the model.
-
You will dive into the theoretical foundations behind each algorithm with the help of mathematical formulas and intuitive explanations of the concepts.
This course is suitable for those who
- Aspiring Data Scientists and Machine Learning Engineers
- Data scientists and data analysts looking to upgrade their skill set
- Anyone who wants to gain an understanding of the field of machine learning and its vast opportunities
Course Description of 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 title
Prerequisites of the Supervised Machine Learning Bootcamp Course
- This course is open to anyone who wants to learn data science.
- You will need to install Anaconda and Jupyter Notebook. We will show you how to do this step by step.
Course Images
Sample video of the course
installation Guide
After extract, watch with your favorite player.
Subtitles: none
Quality: 720p
download link
File Password: www.downloadly.ir
size
2.6 GB