4.3
Beginner
4h
Updated 3 months ago
Machine Learning for Beginners
Gain insights into ML fundamentals, foundational mathematics, coding models, and real-world apps. Discover making perceptrons and exploring scikit-learn for classification, regression, and clustering.
This course explains machine learning for absolute beginners by building a visual understanding of the underlying concepts. It covers some foundational mathematics behind the machine learning models and then guides you in coding for models to solve real-world machine learning problems.
You’ll begin by understanding the limitations of traditional coding techniques in solving machine learning problems. Next, you’ll get familiar with the machine learning process. Then, you’ll build your first machine learning model from scratch—a single perceptron. The course then takes you from a single neuron to a multilayer perceptron to solve a non-linearly separable classification dataset. Finally, the course introduces Python’s library, scikit-learn, where you’ll learn to build models for classification, regression, and unsupervised clustering.
This course aims to make you a lifelong learner and serves as a great starting point for a successful career in machine learning.
This course explains machine learning for absolute beginners by building a visual understanding of the underlying concepts. It c...Show More
WHAT YOU'LL LEARN
An understanding of the fundamentals of machine learning
The ability to build the model of a simple perceptron from scratch
Hands-on experience building a multilayer neural network from single neurons
Hands-on experience solving machine learning problems such as classification, regression, and clustering using Python and sklearn
An understanding of the fundamentals of machine learning
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Content
1.
The Machine Learning Problem
4 Lessons
Get familiar with human-like pattern recognition via machine learning techniques for image analysis.
2.
The Machine Learning Process
5 Lessons
Grasp the fundamentals of data acquisition, modeling, training, prediction, and evaluation in machine learning.
3.
From a Single Neuron to Artificial Neural Networks
4 Lessons
Work your way through non-linear data challenges, neural networks, gradient descent, and MLP using scikit-learn.
4.
Code for Machine Learning Using scikit-learn
4 Lessons
Apply your skills to using scikit-learn for multiclass classification, regression, clustering, and ML challenges.
5.
Concluding Thoughts
1 Lessons
Dig into the nuances of machine learning's place within the broader AI landscape.
Certificate of Completion
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