Analytics
Exploratory Data Analysis
Building Classification Models with scikit-learn
This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.
Security+Learn the skills to keep up with tomorrow’s cybersecurity threats.
₹1,467/mo
Security+Learn the skills to keep up with tomorrow’s cybersecurity threats.
₹1,027
Why choose Core Tech?
✓ Compare courses before making a decision
Check Latest Price →Price may vary. Check latest price on provider site.
Course Insight
Suitable for beginner learners. This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.
Beginner FriendlySelf-Paced Learning
SKILLS TO
MASTER
ModelingTrending
Predictive Machine LearningSQL Querying
Relational Data ManagementCareers:Relevant for professionals pursuing roles within Data Science.
💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.
Quick Facts
Below sections are verified from last major sync. For real-time updates and today's latest lectures, Check official page here.
What You’ll Learn
Perhaps the most ground-breaking advances in machine learning have come from applying machine learning to classification problems.
In this course, Building Classification Models with scikit-learn you will gain the ability to enumerate the different types of classification algorithms and correctly implement them in scikit-learn.
First, you will learn what classification seeks to achieve, and how to evaluate classifiers using accuracy, precision, recall, and ROC curves.
Next, you will discover how to implement various classification techniques such as logistic regression, and Naive Bayes classification.
You will then understand other more advanced forms of classification, including those using Support Vector Machines, Decision Trees and Stochastic Gradient Descent.
Finally, you will round out the course by understanding the hyperparameters that these various classification models possess, and how these can be optimized.
When you're finished with this course, you will have the skills and knowledge to select the correct classification algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.
See how this course curriculum compares with alternatives
Outcomes
- Course Overview : 1min.
- Understanding Classification as a Machine Learning Problem : 32mins.
- Building a Simple Classification Model : 39mins.
- Performing Classification Using Multiple Techniques : 49mins.
- Hyperparameter Tuning for Classification Models : 15mins.
- Applying Classification Models to Images : 15mins.
See side-by-side differences in learning outcomes
FAQs
Top Alternatives
Highly-rated courses worth your attention
⭐ 4(57+ learners)