Artificial Intelligence #4:SVM & Logistic Classifier methods

🤖 Unlock the Secrets of AI with Sobhan's "Artificial Intelligence #4: SVM & Logistic Classifier" 🚀

Course Title: Classification methods for students & professionals. Learn Support Vector Machine & Bayes Classification in Python 🏫✨

Overview:

In this comprehensive AI Course, you'll dive deep into the world of Support Vector Machines (SVM) and Logistic Regression. These powerful classification techniques are essential tools for any machine learning practitioner. Through this course, you'll gain practical experience in classifying data with these methods using Python, the language of choice for modern-day data science.

What You'll Learn:

Support Vector Machine (SVM):

• Understand the principles behind SVM as a supervised learning model.
• Learn how SVM finds the optimal boundary between different classes in a dataset with the maximum margin separation.
• Discover the power of SVM to perform linear classification and how it can be extended to non-linear classification using kernel functions.
• Master the implementation of SVM for real-world datasets.

Logistic Regression:

• Explore the concept of logistic regression, a statistical model that predicts the probability of categorical classes.
• Trace back to 1958 with David Cox's development of binary logistic models and understand how they can estimate probabilities for more than two categories.
• Learn to interpret the odds ratios provided by logistic regression and apply this knowledge to make informed decisions based on your data.

Hands-On Learning:

You'll work with various datasets to classify them using both SVM and Logistic Regression:

1. Random Dataset - A great starting point for understanding the basics.
2. IRIS Flowers - A classic dataset for testing SVM and Logistic Regression algorithms.
3. Handwritten Digits - A more complex dataset to test your skills with non-linear classification.

In the second section, you'll tackle datasets with non-linear structures:

1. Blobs - A dataset designed to challenge SVM and Logistic Regression capabilities on non-linearly separable data.
2. IRIS Flowers - Revisit this dataset to apply your newly gained skills in non-linear classification.
3. Handwritten Digits - Approach this dataset again with advanced techniques to improve classification accuracy.

Why Enroll?

• Money-Back Guarantee: If you find the course unsatisfactory for your needs within 30 days, we offer a full refund – no questions asked! 💰
• Lifetime Access: Once enrolled, you have unlimited access to this course for as long as you need. 🗝️
• Free Course Updates: Any updates made to the course after your enrollment are entirely free. ⬆️
• Full Support: I'm here to support you with any issues or suggestions related to the course. 🤝
• Preview Lectures: Check out the FREE PREVIEW lectures for a quick insight into what the full course has to offer! 👀

Take Action Now! 🏃‍♂️

With the knowledge gained from this course, you'll be well-equipped to tackle real-world problems with AI. Don't let this opportunity pass you by. Click the "Take This Course" button today and embark on a journey to master SVM & Logistic Regression in Python!

Ready to transform your data into insights? Enroll now and let's navigate the AI landscape together! 🌟

Best Regards, Sobhan 😊