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Machine Learning Fundamentals in Python

Updated 03/2025
Learn the art of Machine Learning and come away as a boss at prediction, pattern recognition, and the beginnings of Deep and Reinforcement Learning.
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Track Description

Machine Learning Fundamentals in Python

Unlock the Power of Machine Learning with Python

Dive into the exciting world of machine learning with Python in this comprehensive Track. You'll start by mastering the fundamentals of supervised learning using the popular scikit-learn library. Work with real-world datasets to build powerful predictive models and gain hands-on experience tackling classification and regression problems.

Explore Unsupervised Learning Techniques

Expand your skills by learning how to uncover hidden patterns and structures in unlabeled data. Using Python's scikit-learn and scipy libraries, you'll:
  • Cluster data points into distinct groups
  • Reduce dimensionality to visualize high-dimensional datasets
  • Extract meaningful insights from complex data
  • Apply unsupervised learning to solve real-world challenges

Dive into Deep Learning with PyTorch

Discover the power of neural networks and deep learning as you learn to build and train models using PyTorch, a cutting-edge deep learning framework. Through interactive exercises, you'll construct your first neural network from scratch while mastering key concepts such as backpropagation and gradient descent. You'll also explore techniques for optimizing model performance by tuning hyperparameters and applying deep learning to tasks like image classification and sentiment analysis.

Explore Reinforcement Learning Fundamentals

Complete your machine learning journey by exploring the fascinating field of reinforcement learning. Using Python's Gymnasium library, you'll learn how intelligent agents can learn optimal behaviors through trial and error. Gain hands-on experience:
  • Formulating reinforcement learning problems
  • Implementing classic algorithms like Q-learning and policy gradients
  • Training agents to solve complex environments
  • Applying reinforcement learning to real-world scenarios like game playing and robotics

Why Machine Learning with Python?

Python has become the go-to language for machine learning due to its simplicity, versatility, and extensive ecosystem of powerful libraries. By learning machine learning with Python, you'll be equipped with the tools and skills needed to tackle diverse problems across industries, from healthcare and finance to marketing and autonomous systems.

Launch Your Machine Learning Career

Whether you're aspiring to become a machine learning engineer, data scientist, or AI researcher, this Track provides the perfect starting point. By completing the courses and projects, you'll have a strong foundation in machine learning and a portfolio of practical examples to showcase your skills. Take the first step towards an exciting and rewarding career in this rapidly growing field.

Prerequisites

There are no prerequisites for this track

  • Course

    1

    Supervised Learning with scikit-learn

    Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

  • Project

    bonus

    Predictive Modeling for Agriculture

    Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

  • Course

    3

    Unsupervised Learning in Python

    Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Machine Learning Fundamentals in Python
4 Courses
Track
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