This learning track introduces the fundamental concepts of various Classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, Decision Trees, and Model Evaluation & Model Selection techniques along with their Python implementation in solving real-world problems.
Learn the fundamental concepts and develop a working understanding of the Logistic Regression and the Naive Bayes classification algorithms for predictive analysis.
Understand and apply the advanced classification algorithms like Support Vector Machine and Tree models, covering their theory and implementation in Python.
Learn how to perform detailed model evaluation using different evaluations metrics and techniques to select the best model.
Learn how to apply the concepts learned on live data across industries to generate insights.
What’s included
Shareable Certificate
Earn a sharable certificate upon completion
Lifetime Access
Access this learning track for life once completed
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this learning track on a desktop
Skills You Will Learn
Logistic Regression
Naive Bayes
Support Vector Machines
Decision Tree Models
Model Building
Model Evaluation
Model Validation
Model Selection
Syllabus
Machine Learning – Logistic Regression
Logistic Regression in Python
Getting Started with Naive Bayes Classifiers
Naive Bayes in Python
Support Vector Machines in ML
Support Vector Machines in Python
Understanding Decision Trees
Tree Models in Python
Model Evaluation Techniques – Classification Models
Predicting Heart Disease with Logistic Regression
Predict Credit Card Customer Attrition – Application of Logistic Regression
Using Naive Bayes Classifier to predict Water Potability
Predict Customer Attrition Using Naïve Bayes Classification
Applying SVM classifier to predict the drug type
Detection of Breast Cancer in A Clinical Trial – Application Of SVM
Build a Regression Tree for Predicting Spend on Credit Card
Segmentation on Conversion of Insurance Leads
Identify risk class and eligibility of a customer: Application of Machine Learning
Identify Customers with Higher Likelihood of Credit Card Attrition – Application of Decision Tree
Customer Churn Prediction for a Telecom Client
Recognizing human activity – An application of supervised machine learning
Predicting the Survival of patients with Hepatocellular carcinoma (HCC)
Predictive Maintenance – Leveraging Machine data
How it Works
Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.
With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.
Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.
Learner Outcomes
Complete learning tracks to earn sharable certificates and badges. These awarded items will be look great in your portfolio, resume, and LinkedIn as you showcase your skills and project experience to employers and colleagues.
Understand and apply the fundamental concepts of Logistic Regression and Naive Bayes algorithm as a classification model on text data.
Develop a working understanding of advanced classification algorithms like Support Vector machines, Decision Tree and the other Tree Models using Python.
Ability to perform detailed model evaluation and validation with respect to evaluation metrics and different model selection techniques to identify the best model.
Interpret and visualize the outcome of the applied analytics techniques.
WHAT THE COMMUNITY SAYS ABOUT US
“Rolai is the best program available for someone looking to enhance their skills”
Connor McEachron
Planning & Analytics @ Brooks Brothers
WHAT THE COMMUNITY SAYS ABOUT US
“Great way to learn data analytics and data science”
Balaji Reddy
Manager – Applications Development
WHAT THE COMMUNITY SAYS ABOUT US
“The courses were excellent and covered topics that I didn’t expect”
Aadarsha G
Student At Ohio Wesleyan University
All the Most Frequently Asked Questions
What People Are Asking About Data Education
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Our courses range from Beginner to Advanced. We have courses and learning tracks that are available for beginners, we do however recommend having a basic level of analytics before starting any beginner course.
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Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.
Course Catalog 
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Difficulty: Advanced
Duration: Approximately 5 Weeks
