Download The Supervised Machine Learning Course

What you would learn in The Supervised Machine Learning Course?

What are the reasons to take this Supervised Machine Learning course?

The machine learning algorithms you'll learn in this course are among the most effective data science tools you require to complete regression and classification tasks. These are essential skills that anyone who plans to be an engineer or data scientist must be equipped with within their tools.

Naive Bayes, KNNs, Support Vector Machines, Decision Trees Random Forests, Ridge and Lasso Regression.

Through this class, you'll be introduced to the theories behind each algorithm and apply the knowledge to case studies tailored to each algorithm using Python's sci-kit learning library.

The first step is to cover naive Bayes, a highly effective technique based on Bayesian statistics. Its main strength is its ability to work in real time. One of the most frequent applications is filtering emails containing spam, flagging inappropriate remarks on social media, or conducting sentiment analysis. This course will provide a concrete illustration of exactly how that operates, so stay tuned!

Next is K-nearest neighbors - among the top and most commonly employed algorithms for machine learning. What's the reason? Because it's simple to use distance-based metrics to create precise predictions.

The next step is to explore decision tree algorithms that will be the foundation for our next subject - specifically random forests. They're powerful ensemble learners who harness the power of many decision trees to produce precise predictions.

In the next section, we'll discuss Support Vector Machines - classification and regression models that utilize kernels to address various issues. In this segment's hands-on portion, we'll develop the model to classify mushrooms as edible or poisonous. Exciting!

In the final section, you'll be introduced to Ridge and Lasso Regression - they are regularization algorithms that help improve the linear regression process by limiting the potential of the individual features and stopping overfitting. We'll discuss the similarities and differences and the advantages and disadvantages of both regression methods.

Every course section is structured consistently to ensure the best learning experience.

We begin with the basic theory behind every algorithm. To increase your understanding of the subject, we'll guide you through a hypothetical case and introduce mathematical formulas for the algorithm.

Then, we create a model to solve a specific issue with it. This is done with the sklearn library, a well-known Python program.

We evaluate the effectiveness of our models with the help of metrics like accuracy, precision-recall, precision, and F1 score. F1 score.

We also research various methods, such as cross-validation and grid search, to increase the model's efficiency.

To top it off, we also have a selection of complementary exercises and quizzes to help you improve your skills. In addition, we also provide extensive course materials that will assist you in the course, and these materials are available for you to access at any time.

The lessons were developed using 365's unique teaching style that most of you have come across. We want to teach complex subjects in an easy-to-understand approach, focusing on the practical application of learning.

The super-supervised Machine Learning course will fulfill your learning needs through the effectiveness of animated animations, quizzes, exercises, and well-written notes for the course.

If you'd want to elevate your knowledge of data science and include the most sought-after techniques on ton resume, This course is the right choice for you.

Course Content:

• Algorithms for Regression and Classification
• Utilizing skLearn along with Python to implement supervised machine learning methods
• K-nearest neighbors are used for both regression and classification
• Naive Bayes
• Ridge as well as Lasso Regression
• Decision Trees
• Random Forests
• Support Vector Machines
• Practical case studies to train as well as testing and evaluate, and improve the performance of models
• Cross-validation of parameter optimization for cross-validation
• Learn to make use of metrics like Precision-Recall, F1-score, and Recall as well as confusion matrix to assess the real performance of models
• You will explore the mathematical basis of every algorithm with the help of a clear explanation of formulas and mathematical concepts

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