Learn core ML algorithms from scratch—linear regression, neural networks, and more—using real data and practical coding.
Sub Category
- Data Science
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Objectives
- Understand core ML concepts: loss functions, gradient descent, epochs, and learning rates
- Build and train linear and logistic regression models from scratch in Python
- Use Numpy, Pandas, Matplotlib, and Scikit-learn to work with real datasets
- Implement neural networks step-by-step, including forward and backpropagation
- Classify handwritten digits using the MNIST dataset and Keras
- Prevent overfitting with regularization techniques like early stopping and dropout
- Work with molecular data using RDKit and visualize chemical structures
- Apply graph convolution techniques to molecular structures using MolGraph
- Learn DeepChem to train models on molecular datasets
Pre Requisites
- No prior experience in machine learning or data science is required
- Familiarity with using Google Colab or Jupyter Notebooks is helpful (not mandatory)
- Basic understanding of Python programming (variables, loops, functions)
- A working laptop or desktop with internet access
- Curiosity and willingness to learn by coding and experimenting
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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