Free AI with Python Course
Artificial Intelligence with Python
4.54
Intermediate level
11.25 learning hrs
Learn Artificial Intelligence with Python from basics in this free online training. This free AI with Python course is taught hands-on by experts. Learn about neural networks, Perceptron, Loss Function. Best for Beginners.
Instructor:
Prof. Mukesh Rao
Modules updated 07/2025
About this course
Take a free course on Artificial Intelligence with Python and learn the fundamentals of AI. Learn how to use Python to create intelligent systems, explore machine learning algorithms, and apply AI techniques to real-world problems.
This course will introduce you to Python programming for Artificial Intelligence, with a few demonstrated samples. It shall begin with comprehending the history of Neural Networks. It shall imbibe in you the knowledge of the difference between a biological neuron and an artificial neuron, Perceptron and working mechanism, architecture of ANN, different functions such as activation, softmax, its forward propagation, and loss as you follow the first half of the course. The second part engages you by demonstrating tasks using the Keras framework, propagation and gradient descent, and an application study on the MNIST data set. You will also understand the working of Tensorflow 2.0 in the course. Take up the quiz at the end of the course to test your skills and evaluate your gains to avail of the certificate.
After this free, self-paced, intermediate’s guide to Artificial Intelligence with Python, you can enroll in the Artificial Intelligence courses and embark on your career with the professional Post Graduate certificate and learn different concepts in depth with millions of aspirants worldwide!
Course outline
History behind neural networks
This module will introduce you to the history of neural networks, the early experiments, limitations, and how ANN evolved to build almost every technology that is in use today.
Relationship between biological neuron and artificial neuron
You will understand the supervised learning technique, ANN, and learn how it differs from the biological neural system. In this section, you will also learn why artificial neural networks are dependent on biological neurons to perform a few crucial tasks, along with understanding dense neural networks.
Perceptron and working mechanism
You will understand the mathematical model of biological neurons in this module. As you follow this module, you will also understand how an artificial neuron mimics the biological neuron and the mechanism to do so.
Architecture of artificial neural network
You will understand different elements in artificial neural networks, different layers, and their functionalities in this section. The section briefly discusses how each layer contributes to processing the data to produce accurate output.
Types of activation functions
At the beginning of this section, you will understand what classification is and why it is performed in ANN. You will then understand regression, ANN with respect to Perceptron, and the functions that make the building blocks of ANN as you follow the module.
Softmax function
You will understand the softmax function with multi-class classification in deep neural networks. You will learn the working mechanism of the softmax function with an example as you follow this section.
Forward propagation
This section discusses how the entire data set passes through the different layers of the neural network. You will understand forward propagation and its mechanism with matrix operation, how the process results in the error, and also understand the mechanism to fix this error.
Loss function
This section explains what error functions are, their properties, and the mathematics behind them with an example to help you understand what mean least loss is.
Demo using keras framework
You will understand what Keras is, its elements, features, and working in this section. You will also learn to work with Keras, an interface for Tensorflow, to reduce the cognitive load of ANN with demonstrated programming.
Back propagation and gradient descent
You will understand how an error is resolved using back propagation at the beginning of this section. Later, you will learn about vanishing gradients and exploding gradients concepts and their mathematical functioning to understand gradient descent.
Tensorflow 2.0
You will understand what Tensorflow is and why Keras is used for Tensorflow. You will learn about the tensors, features of the Tensorflow 2.0 version, syntax, and how all machine learning processes can be performed using Tensorflow.
Demo on MNIST data set
You will learn to work with the discussed concepts in the Jupyter notebook with the MNIST dataset in this section.
Get access to the complete curriculum once you enroll in the course
4.54
What our learners enjoyed the most
Skill & tools
63% of learners found all the desired skills & tools
Our course instructor
Prof. Mukesh Rao
Senior Faculty, Academics, Great Learning
Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
Can I sign up for multiple courses from Great Learning Academy at the same time?
Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no stricture to the number of courses you can enroll in at once. All the courses offered by Great Learning Academy are free, so we propound you learn one at a time to get the best out of the courses.
Why choose Great Learning Academy for this free Artificial Intelligence with Python course?
Great Learning is a global educational technology platform committed to developing skilled professionals. Great Learning Academy is a Great Learning project that provides free online courses to assist people in succeeding in their careers. Great Learning Academy's free courses have helped over 4 million students from 140 countries. It's a one-stop destination for all of a student's needs.
This course is not only free and self-paced, but it also includes solved problems, demonstrated codes, and presented examples to help you comprehend the numerous areas that fall under the subject. The course is conducted by topic experts and is carefully tailored to cater to both beginners and professionals.
Who is eligible to take this course?
Anybody with basic knowledge of computer science, probability, calculus and a good hold on Python Programming, interested in learning ANN, Keras, and Tensorflow and understanding their working mechanism can take up the course. So, enroll in our AI with Python today and learn it for free online.
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