Supervised Machine Learning Course with Certificate

About Course

If you want to move beyond machine learning theory and actually understand how predictive models work, this supervised machine learning course is built to get you there. Taught by Srikanth Varma, Lead DSML Instructor at Scaler with 13 years of AI/ML experience including a stint as Senior ML Scientist at Amazon, the course runs 25 hours and 7 minutes across 8 modules and 7 challenges, making it a genuinely thorough free machine learning course with certificate, not a quick overview.

The course builds up algorithm by algorithm, starting with Linear and Logistic Regression, where you cover the geometric and mathematical intuition, regularization (L1/L2), and hyperparameter search using grid and random search. From there it moves into K-Nearest Neighbors and Naive Bayes, covering distance measures, cross-validation, and handling imbalanced data, before getting into Support Vector Machines with kernel tricks and Decision Trees built on entropy and information gain. This progression is what makes the course cover real machine learning concepts in depth rather than just naming algorithms, each module pairs the math with working code samples in Python.

The final module ties everything together with Ensemble Models, Bagging, Random Forests, Boosting, XGBoost, AdaBoost, and Stacking, showing how top-performing models in the real world (and on Kaggle) actually combine these individual algorithms. By the end, you'll have solid machine learning basics, hands-on experience with Scikit-Learn, Pandas, and NumPy, and enough grounding to confidently learn machine learning at a more advanced level. It's self-paced throughout, and completing all 8 modules earns you a Scaler Certificate of Excellence, making this a strong machine learning course for beginners who want both theory and implementation.

Pre-requisites for free Supervised Learning certification course

1. Basic Programming Knowledge: A fundamental understanding of any programming language is recommended. Familiarity with Python is particularly beneficial, as it's widely used in data science and machine learning.
2. Understanding of Mathematics: Basic knowledge of mathematics, particularly in areas like algebra, statistics, and probability, is beneficial as they underpin many machine learning concepts.
3. Familiarity with Data Structures: Basic understanding of data structures like arrays, lists, and dictionaries can be advantageous as they are frequently used in coding machine learning algorithms.
4. Willingness to Learn: As this is a beginner-level course, the most important prerequisite is a curiosity and willingness to learn new concepts. Even without a strong background in the above areas, the course is designed to be accessible and instructive for all, covering essential machine learning basics.

Who should learn this free Supervised Learning course?

1. Aspiring Data Scientists: Those planning to build a career in data science can greatly benefit from this course, as supervised machine learning is a foundational element of the field.
2. Software Engineers: Software engineers looking to enhance their skills and dive into the realm of AI and machine learning will find this course invaluable.
3. Statisticians and Analysts: Professionals already working with data, like statisticians and analysts, could use this course to understand how machine learning techniques can augment their current data analysis methods.
4. Academics and Researchers: Scholars in fields where data analysis is key (like social sciences, biomedical research, etc) could learn machine learning through this course to leverage machine learning in their research.