Tutorials > Machine Learning Basics: Python, Numpy & Scikit-Learn

Machine Learning Basics: Python, Numpy & Scikit-Learn

Date: 28 June 2025 | Posted by: 0nelove

Learn core ML algorithms from scratch—linear regression, neural networks, and more—using real data and practical coding.
What you'll learn
Understand core ML concepts: neural networks, 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
Requirements
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
Description
Are you eager to break into the world of machine learning but overwhelmed by where to begin? This course offers a clear, hands-on, and structured path into the field, guiding you from foundational concepts to practical model building—without requiring any prior experience in machine learning.In this course, you'll not only understand how machine learning works, but also build your own models from scratch, code by code, using powerful tools like Python, Numpy, Pandas, Matplotlib, Scikit-learn, and Keras. You'll go beyond the "plug-and-play" use of libraries to really grasp what's happening under the hood—giving you confidence, depth, and true skill.Tools & Frameworks You'll Learn and UsePython, Numpy, Pandas, Matplotlib, SeabornScikit-learn and KerasGoogle Colab (no setup required!)RDKit for molecular analysisDeepChem and MolGraph for advanced applicationsWhat Makes This Course Different?This is not a passive, lecture-only course. It's a hands-on, problem-focused journey where you'll:Write algorithms manually before using libraries—so you understand the logicWork with real datasets, including molecular chemistry and image recognitionBuild regression and classification models using Python and Google ColabTrain and evaluate neural networks from scratch and using KerasApply advanced topics like regularization, batching, overfitting prevention, and graph convolutionExplore tools used in cutting-edge applications like drug discovery (RDKit and DeepChem)You'll learn not just how to use machine learning tools, but also how and why they work—empowering you to build your own solutions, not just replicate tutorials.If you're serious about learning machine learning the right way—from intuition to implementation—this course will give you a solid foundation and practical skillset that you can apply immediately.Join now, and start building real machine learning models with Python today.
Who this course is for
Beginners in machine learning, AI, data science, or Python
Students or researchers in science
Professionals looking to apply ML to real-world data problems, especially in the life sciences

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