AI Luminaries
A curated collection of videos featuring top experts in artificial intelligence.
Overview
Learners will analyze real-world datasets, prepare and transform features, and apply regression algorithms to predict numerical outcomes with confidence. By the end of this course, learners will be able to structure datasets for modeling, handle missing and inconsistent data, encode categorical variables appropriately, and evaluate regression models using training and test data.
This course is designed to build practical, job-ready skills in predictive analytics by walking learners through the complete regression workflow. Rather than focusing only on theory, the course emphasizes hands-on data preparation techniques such as imputation, feature replacement, ordinal encoding, and dataset validation. Learners gain a clear understanding of how real-world data issues impact model performance and how to address them systematically.
What makes this course unique is its end-to-end, implementation-driven approach. Each concept is reinforced through realistic data scenarios that mirror industry practices in pricing analytics. By completing this course, learners will be able to confidently design, train, and evaluate regression models, making them well prepared for applied data science, business analytics, and machine learning roles where accurate price prediction is essential.
Syllabus 2
- Foundations of Price Prediction with Regression 3 hours
This module introduces learners to the fundamentals of predicting prices using regression techniques. Learners explore how real-world factors influence pricing, learn to structure datasets correctly for regression analysis, and apply essential data preparation techniques such as indexing, test valu…
- Feature Engineering and Regression Modeling 2.5 hours
This module focuses on advanced data preparation and modeling techniques required for effective regression-based price prediction. Learners perform feature engineering, convert categorical variables into quantitative and ordinal forms, validate dataset structure, and apply proper train–test splitti…
Advantages and disadvantages
Advantages
- Structured courses with graded quizzes, assignments and deadlines you can reset.
- A shareable certificate from the university or company when you pay.
- Financial aid is often approved for students in India (apply 15 days before you need it).
- Covers niche tools that universities don't teach.
- Self-paced: start any time.
- A clear syllabus (2 parts) you can see before you start.
Disadvantages
- Paid after a 7-day free trial (Coursera Plus or per course).
- Some courses can be audited for free, but graded work and certificates need payment.
- Many courses are produced quickly from slides and screen recordings; read the reviews first.
Some points apply to every course of this kind; see how we rank.
This course is paid. Here is how to take it for free
- Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
- Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.
Taught by
- EDUCBA
EDUCBA is in Tier 5: mass-produced or unclear of our institution ranking (36/100). Thousands of courses, mostly slides and screen recordings.
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