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Supervised Machine Learning – Classification
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.
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8 Courses
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14 Projects & Case Studies
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Difficulty: Advanced
Prior education or professional experience is strongly recommended
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Duration: Approximately 5 Weeks
Suggested learning pace is 4-5hr/week
Course Overview
- 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
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
- 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.
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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.
Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.
No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.
- We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
- We also work with enterprises and universities to develop new content directed towards their industry and expertise.
