You should have basic Python programming skills (variables, loops, functions, and working with libraries), along with foundational knowledge of statistics (mean, variance, probability distributions) and linear algebra (matrices, vectors). No prior machine learning experience is required.

The course covers machine learning foundations including linear and logistic regression, tree-based algorithms and ensemble methods (Random Forests, XGBoost, LightGBM), Support Vector Machines, clustering algorithms (K-Means, DBSCAN), dimensionality reduction with PCA, and neural network fundamentals including backpropagation, activation functions, and loss functions.

This is a 6-week intensive micro-degree. You should plan to dedicate approximately 10-15 hours per week, including video lectures, reading materials, coding exercises, and hands-on projects. Each week covers a dense chapter with both theoretical concepts and practical implementation.

You'll complete several hands-on projects including a house price prediction system using linear regression, a credit risk assessment model using ensemble techniques, customer segmentation using clustering algorithms, and an image classification project on the MNIST dataset using SVMs and neural networks.

This course prepares you for roles such as Data Scientist, Machine Learning Engineer, and Computer Vision Engineer. You'll gain practical experience building and deploying ML models, evaluating performance with industry-standard metrics, and working with algorithms used across finance, retail, and technology sectors—skills that are highly sought after by employers.

You'll work with Python as the primary programming language, along with NumPy and Pandas for data manipulation, Scikit-learn for classical ML algorithms, TensorFlow/Keras for building neural networks, and XGBoost/LightGBM for gradient boosting. Matplotlib is used for data visualization and model analysis throughout the course.

The Micro Degree course is an online LIVE course, where LIVE sessions will be conducted online on our Classroom platform. Prior to the start of the course, you'll receive preparatory material in the form of recorded content which can be access on the same platform.

In this course instructors will use English language for teaching.

Upon successful registration, you will receive a confirmation email on your registered email ID. In this email you will receive login details for your newly created account on the Edyoda Classroom platform (https://classroom.edyoda.com). Additionally, you will receive a PDF guide containing step-by-step instructions on how to utilize the platform to access live sessions and learning materials.

Our instructors are the industry experts with a minimum working experience of 10 years with a strong technical and teaching background. They bring industry knowledge and practical expertise to the course.

Yes, the course includes online assignments, quizzes, and a final project to reinforce your learning and assess your proficiency in Machine Learning & Deep Learning.

Yes, you can interact with instructors and fellow students through discussion forums, live Q&A sessions. We encourage a supportive learning community.

We offer a 100% money-back guarantee to ensure your complete satisfaction. If you're not satisfied, you can request a full refund within 3 days of purchase or before the second session, whichever comes earlier. Simply contact our support team(support@edyoda.com) with your purchase details, such as the order ID or email address, and share your reason for the refund. Requests made after 3 days or after the second session will not be eligible for a refund. There are no hidden charges, you will receive the full amount paid. Refunds are processed within 7–10 business days and credited back to your original payment method.