The Supervised Machine Learning Bootcamp

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 83 lectures (5h 52m) | 2.76 GB

Data Science, Python, sk learn, Decision Trees, Random Forests, KNNs, Ridge Lasso Regression, SVMs

Why should you consider taking the Supervised Machine Learning course?

The supervised machine learning algorithms you will learn here are some of the most powerful data science tools you need to solve regression and classification tasks. These are invaluable skills anyone who wants to work as a machine learning engineer and data scientist should have in their toolkit.

Naïve Bayes, KNNs, 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 tailored to each one of them, using Python’s sci-kit learn library.

First, we cover naïve Bayes – a powerful technique based on Bayesian statistics. Its strong point is that it’s great at performing tasks in real-time. Some of the most common use cases are filtering spam e-mails, flagging inappropriate comments on social media, or performing sentiment analysis. In the course, we have a practical example of how exactly that works, so stay tuned!

Next up is K-nearest-neighbors – one of the most widely used machine learning algorithms. Why is that? Because of its simplicity when using distance-based metrics to make accurate predictions.

We’ll follow up with decision tree algorithms, which will serve as the basis for our next topic – namely random forests. They are powerful ensemble learners, capable of harnessing the power of multiple decision trees to make accurate predictions.

After that, we’ll meet Support Vector Machines – classification and regression models, capable of utilizing different kernels to solve a wide variety of problems. In the practical part of this section, we’ll build a model for classifying mushrooms as either poisonous or edible. Exciting!

Finally, you’ll learn about Ridge and Lasso Regression – they are regularization algorithms that improve the linear regression mechanism by limiting the power of individual features and preventing overfitting. We’ll go over the differences and similarities, as well as the pros and cons of both regression techniques.

Each section of this course is organized in a uniform way for an optimal learning experience:

– We start with the fundamental theory for each algorithm. To enhance your understanding of the topic, we’ll walk you through a theoretical case, as well as introduce mathematical formulas behind the algorithm.

– Then, we move on to building a model in order to solve a practical problem with it. This is done using Python’s famous sklearn library.

– We analyze the performance of our models with the aid of metrics such as accuracy, precision, recall, and the F1 score.

– We also study various techniques such as grid search and cross-validation to improve the model’s performance.

To top it all off, we have a range of complementary exercises and quizzes, so that you can enhance your skill set. Not only that, but we also offer comprehensive course materials to guide you through the course, which you can consult at any time.

The lessons have been created in 365’s unique teaching style many of you are familiar with. We aim to deliver complex topics in an easy-to-understand way, focusing on practical application and visual learning.

With the power of animations, quiz questions, exercises, and well-crafted course notes, the Supervised Machine Learning course will fulfill all your learning needs.

If you want to take your data science skills to the next level and add in-demand tools to your resume, this course is the perfect choice for you.

What you’ll learn

• Regression and Classification Algorithms
• Using sk-learn and Python to implement supervised machine learning techniques
• K-nearest neighbors for both classification and regression
• Naïve Bayes
• Ridge and Lasso Regression
• Decision Trees
• Random Forests
• Support Vector Machines
• Practical case studies for training, testing and evaluating and improving model performance
• Cross-validation for parameter optimization
• Learn to use metrics such as Precision, Recall, F1-score, as well as a confusion matrix to evaluate true model performance
• You will dive into the theoretical foundation behind each algorithm with the aid of intuitive explanation of formulas and mathematical notions