AlphaML: Classical Machine Learning
Learn the foundations of Machine Learning in this experiential 2-month-long workshop! This is a highly gamified and hands-on program, with assignments, hackathons, and guided projects!
About the Course
Are you interested in a career in Machine Learning?
Are you overwhelmed by the unstructured content out there?
Unsure of whether someone with your educational background or professional experience can learn ML?
Come join our 2 month long program, AlphaML, where we start from 0, and empower you to be able to execute impactful hackathons and industry-grade projects on your own!
What's more? We keep you motivated via our highly gamified peer-learning!
What are you going to learn?
- Mathematics for ML: Linear Algebra, Probability, Calculus, Information Theory
- Tools & Libraries, Github
- Statistical ML - Mathematical Theory, Geometric Interpretation, and Practical Code in Classification, Regression & Density Estimation.
- Practical Consideration - Imbalanced Datasets, Bias & Variance, Feature Selection, Handling Missing Values, etc
- Models: Linear Regression, SVM, Ensemble Learning (Bagging, Boosting, etc), Random Forest, Decision Tree, Nearest Neighbour, etc.
- Hackathon!
- Industy-Grade Project!
Get bonuses worth Rs. 50,000/- for FREE when you join:
- 2 Weeks Code for Machine Learning: Python, Sklearn, Pandas, Matplotlib, Seaborn, Tensorflow (Introductory)
- Live Guest Masterclass from a MAANG+ Engineer
- SQL Flash Cards for Data Scientists
- Deep Learning Teaser
Course Content
- Linear Algebra - Norms, Vectors, Eigen Decom, Determinants, Matrix Transformation, Solving a system of linear equations, etc.
- Probability - Random Variable, Independent Events, Bayes Rule, etc.
- Calculus - Basic Formulae (Derivatives)
- Information Theory - KL Divergence, Entropy,, Statistical Measures (Variance, Co-variance Matrix, etc)
- GitHub - How to make your profile stand out/sample READMEs, etc.
- Python - Sklearn, Pandas, Matplotlib, Seaborn & EDA
- Classification - Linear Regression, LDF, SVM, KNN
- Regression - Above applicable methods + Decision Tree, Random Forest, Ensemble Learning
- Density Estimation - Clustering (KMeans), Dimensionality Reduction (PCA)
- Practical Considerations - Bias and variance, Feature Selection, Missing Values, Categorical Values, etc.
- Learning Algorithms - Gradient Descent, Convex Functions, etc.
- Hackathon
- Project
Reviews
"This course is, *without exaggeration*, one of the finest I've encountered in the realm of online learning. The curriculum is on par with what you'd expect from an Ivy League university, yet it's offered at a remarkably affordable price. Abbhinav sir, the instructor, is a standout feature of this course. He has a unique talent for making challenging concepts accessible and engaging. He provides multiple perspectives on every topic – logical, practical, and geometrical – ensuring a well-rounded understanding. What sets this course apart is the dedication and student-centric approach of the instructor. If a student falls behind or struggles, extra time and resources are devoted to bringing them up to speed. Every concern is taken seriously, fostering an environment where every learner feels valued and supported. The camaraderie among students and the networking opportunities are invaluable. It’s more than an educational program; it's a transformative journey into the world of machine learning. Highly recommended for anyone looking to dive deep into machine learning with guidance from one of the best in the field!"
"Thank you for the constant push and motivation, due to which I was able to allocate time despite my hectic work schedule. I am very thankful to Abbhinav for creating this course and making the content easily consumable for someone who has untouched the math for a very long time after completing college."
"The "No nonsense approach" of the program is the best thing about it - getting directly into what machine learning is, and how you can apply it. They don't make you read all the nitty-gritty of machine learning at the start - you learn it by doing it; and that's the only way, if not, you will fall into tutorial hell. One of the BEST THINGS about the program is BY FAR the response of the team, whenever you have any problem. "
"It is one of the best value-for-money courses that I have attended. The team is very inspiring, and they spring into action whenever you need support. In fact, a lot of times they go out of their way to accommodate our requests. Abbhinav is a brilliant teacher, and the way he explains concepts is just awesome. All you need to do is find a QUIET PLACE and listen to his lectures. THE CONCEPTS JUST ENTER YOUR BRAIN in no time! If you truly want to 'learn', just ask them for help."
"After attending Abbhinav's classes, I realized that he is a very fine teacher/mentor, who has helped me establish the core AI/ML concepts and the underlying mathematical/statistical principles in a way I can grab effortlessly. I highly appreciate and recommend his teaching style, as well as his knowledge that helps synthesize the subject matter with more clarity. The hackathons and projects are baked into the course in a very innovative manner to instill a real sense of accomplishment. Highly recommended!"
Introductory Boot Camp: 3 Hours!
Get a taste of ML and go from ZERO-TO-HERO using India's ONLY example-driven experiential workshop, with market insights and a roadmap to pivot to ML roles!
SigmaML: Deep Learning, Computer Vision and NLP
SigmaML is our 12-week program on Deep Learning, Computer Vision, and Natural Language Processing with our usual experiential format spin on it!