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Free AI with Python Course

Name: Artificial Intelligence with Python
Rating: 4.54 (1584 reviews)

Artificial Intelligence with Python

4.54 Intermediate level 11.25 learning hrs 89.8K+ Learners

Learn AI with Python, like neural networks, perceptrons, activation and loss functions, Keras, TensorFlow, and MNIST. Join this free Python with AI course to build and train ANN models for image classification and real AI tasks.

Instructor:

Prof. Mukesh Rao

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Skills you will gain

AI Fundamentals

Python for AI

Neural Networks Basics

Biological vs. Artificial Neurons

Perceptron Mechanism

ANN Architecture

+7 More

What you’ll learn

Write foundational AI scripts using Python.

Process data efficiently using standard Python libraries.

Implement simple machine learning algorithms from scratch.

Build predictive models to analyze data trends.

Key Highlights

Get free course content

Master in-demand skills & tools

Test your skills with quizzes

About this course

This Python with AI course provides a strong foundation in how neural networks work and how they are used to solve machine learning problems. You’ll learn the history behind neural networks, the link between biological and artificial neurons, and the core mechanics of perceptrons, dense networks, and ANN architecture. The course also covers activation functions, softmax, forward propagation, and loss functions, helping you understand how neural networks process data, make predictions, and measure error in both classification and regression tasks.

In this course, you’ll also learn back propagation and gradient descent so you can understand how neural networks improve model performance during training. In addition, you’ll work with Keras and TensorFlow 2.0 to implement neural network models in practice, and apply these concepts in a demo using the MNIST dataset in Jupyter Notebook. By the end of the artificial intelligence using Python course, you’ll be able to explain the structure and training process of neural networks, build basic ANN models with modern frameworks, and apply deep learning concepts to image classification and other real-world machine learning tasks.

Stand out with an industry-recognized certificate

Start learning

10,000+ certificates claimed, get yours today!

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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

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This course is ideal for

Aspiring data scientists starting with Python.
Python developers wanting to explore AI concepts.
Computer science students building ML foundations.
Tech professionals looking to understand predictive modeling.

Artificial Intelligence with Python

4.54

11.25 Hours

Intermediate

89.8K+ learners enrolled so far

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4.6

4.89

4.94

4.7

Learner reviews of the Free Courses

4.54

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73%

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19%

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Joshua Senteu

5.0

★★★★ ★

“Comprehensive Overview of AI with Python”

The Artificial Intelligence with Python course provided a thorough introduction to key AI concepts, including the history of AI, softmax function, and both forward and backward propagation in neural networks. The course’s hands-on approach using TensorFlow and Keras to build models on datasets like wine classification and MNIST was very effective in reinforcing the learning. I gained valuable insights into neural network training and model evaluation techniques.

Akhil Sankar

5.0

★★★★ ★

Qatar

“In depth lecture on Artificial intelligence with Python”

The Artificial Intelligence with Python course provided a solid foundation for learning the key concepts and techniques used in AI. The use of Python, coupled with popular libraries such as TensorFlow, Keras, and scikit-learn, made the course practical and hands-on, which allowed me to directly apply the concepts learned in real-world examples. The explanation of machine learning algorithms such as supervised and unsupervised learning, along with the use of neural networks for tasks like classification and regression, was clear and well-structured.

Rajkumar Chintalapudi

5.0

★★★★ ★

United States

“Artificial Intelligence is very well explained”

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. It encompasses a range of technologies, including machine learning, natural language processing, and robotics. AI enables systems to learn from data, recognize patterns, make decisions, and perform tasks that typically require human intelligence. Applications span various fields, from healthcare and finance to entertainment and autonomous vehicles.

Saiful Islam

5.0

★★★★ ★

“Gained Hands-On Experience in Building AI Models Using Python and Exploring Real-World Applications”

I enjoyed the practical approach of the course, especially the hands-on projects that allowed me to implement various AI algorithms. The Python-based exercises, combined with clear explanations of complex concepts like neural networks and natural language processing, made learning engaging and rewarding. The support from instructors and detailed coding examples were also fantastic for grasping difficult topics.

Ola Koya

5.0

★★★★ ★

United Kingdom

“The highlight of the AI with Python course is that I've been able to develop foundational knowledge in AI and neural networks.”

I gained a solid understanding of AI concepts through a series of well-structured learning videos. Access to detailed course materials reinforced the video lessons and provided additional insights. Exploring the program overviews enhanced my understanding of how AI concepts and Python can be applied in real-world scenarios. I'm thrilled to have successfully completed a quiz on neural networks, which tested my understanding of key concepts related to neural network architectures and functions.

Krishna Somani

4.0

★★★ ★ ☆

India

“The Artificial Intelligence with Python course provided hands-on projects and covered machine learning and natural language processing”

The Artificial Intelligence with Python course provided a valuable learning experience through hands-on projects and a comprehensive curriculum. I explored diverse topics, including machine learning and natural language processing, while gaining proficiency in essential Python libraries like TensorFlow and Scikit-learn. The interactive environment encouraged collaboration with peers and support from instructors, deepening my understanding of AI concepts.

Jorge Luis Matos Baucells

5.0

★★★★ ★

United States

“Excellent Course and Training tool”

Excellent Course and Training tool. Great Videos and material

Rahul VM

5.0

★★★★ ★

India

“The curriculum was well-structured, balancing theoretical concepts with practical applications. ”

The instructors were knowledgeable and approachable, fostering an interactive learning environment. I particularly enjoyed the hands-on projects, which allowed me to apply what I learned in real-world scenarios. The course also encouraged collaboration with peers, enhancing my understanding through group discussions. Overall, it was an enriching experience that has sparked my interest in further exploring AI technologies. Highly recommend for anyone looking to deepen their understanding of artificial intelligence!

Mohit Kumar Dhiman

5.0

★★★★ ★

India

“AI Course Feedback: A Comprehensive Learning Experience”

This AI course provided a solid foundation in the field, covering key concepts and practical applications. The curriculum was well-structured, and the instructors were knowledgeable and engaging. The interactive quizzes and assignments were helpful for reinforcing learning. The course also provided valuable insights into real-world AI applications. Overall, it was a great learning experience.

vijay anand

5.0

★★★★ ★

India

“The Learning Experience for Artificial Intelligence with Python course provides a comprehensive introduction to AI concepts and how to implement them using Python.”

The Learning Experience for Artificial Intelligence with Python course provides a comprehensive introduction to AI concepts and how to implement them using Python. The course covers foundational topics in AI, such as machine learning, deep learning, natural language processing (NLP), and computer vision. Through hands-on coding exercises and projects, students learn how to work with popular Python libraries like TensorFlow, Keras, scikit-learn, NumPy, and Pandas.

Pooja shri

5.0

★★★★ ★

Canada

“Great Learning is a fantastic platform for learners aiming to advance their careers, especially in tech and business domains. ”

Great Learning offers high-quality courses with a well-structured curriculum that combines theoretical concepts with practical applications. The platform provides flexible learning options, including self-paced, live sessions, and in-person formats, making it suitable for learners with different schedules. The courses are taught by experienced faculty, many with industry expertise, which ensures that the content is aligned with current trends. Additionally, Great Learning offers strong career support, including placement.

vaneeza malik

5.0

★★★★ ★

“The course was well-structured, and the online platform was user-friendly. I appreciated the flexibility to learn at my own pace. ”

I thoroughly enjoyed the "AI with Python" course on Great Learning Academy, and several aspects stood out to me. I particularly liked how the course struck a perfect balance between theoretical foundations and practical applications, making complex concepts accessible and engaging. The interactive coding exercises and hands-on projects helped reinforce my understanding, and the instructors' expertise and enthusiasm made the learning experience enjoyable.

Divya Gupta

5.0

★★★★ ★

India

“Artificial Intelligence with Python”

History, Relation between Biological and Artificial Neuron, Perceptron with working, Architecture of ANN, Types of Activation Functions, Forward and Backward Propagation, Loss Function, TensorFlow 2.0, Keras and Implementation

Pankaj shivdutt Pandey

4.0

★★★ ★ ☆

India

“This course provided a deep dive into AI algorithms, Python libraries like TensorFlow, and practical hands-on projects that enhanced my skills.”

This course provided a deep dive into AI algorithms, Python libraries like TensorFlow, and practical hands-on projects that enhanced my skills.

THANGAPERIYASAMY S

5.0

★★★★ ★

India

“Understanding the underlying principles of machine learning algorithms and how they work. Hands-on practice in building and testing neural networks.”

I really enjoyed learning about the different activation functions in neural networks, especially how they help models learn non-linear patterns. It was exciting to understand how the ReLU and Sigmoid functions work in real-world applications. The hands-on coding exercises were especially beneficial for me. Being able to implement algorithms from scratch helped solidify my understanding of theory and its practical applications in machine learning.

Vijayraje Chandrakantrao Jadhav

4.0

★★★ ★ ☆

India

“Python for AI: A Practical Approach”

The Python for AI: A Practical Approach course offers a concise introduction to AI concepts using Python. The course is well-structured, combining theoretical knowledge with practical applications, which aids in understanding. The hands-on projects effectively reinforce learning, allowing students to apply techniques in real-world scenarios. However, some advanced topics could be covered in more depth. Overall, it’s a great starting point for anyone interested in leveraging Python for artificial intelligence.

Siddharth S 21BME1004

5.0

★★★★ ★

India

“Comprehensive and Engaging Learning Experience”

"I really enjoyed the curriculum, which was well-structured and covered a deep range of topics in a clear and engaging manner. The instructor made complex concepts easy to understand, and the quizzes and assignments reinforced learning effectively. The course was easy to follow, yet challenged me to think critically. Overall, it was a great learning experience!"

avinash kumar

4.0

★★★ ★ ☆

India

“KEY SKILLS, CONCEPTS, PERSONAL GROWTH”

appreciated the course's hands-on approach, which allowed me to apply theoretical concepts in real-world projects. The interactive lectures fostered engaging discussions, enhancing my understanding of complex topics. Additionally, the supportive learning environment encouraged collaboration and feedback, making it a valuable experience for my growth in the subject.

Raymond Evanshen

5.0

★★★★ ★

Canada

“I really enjoyed listening to Dr. Mukesh as he was very knowledgable ”

It gave me a great chance to learn about neural networks and their applications in AI systems.

Mohammed Aadil

5.0

★★★★ ★

India

“the course is very good and it is really helpful”

the course is very good and it is really helpful and it helped to improve my skills

Our course instructor

Prof. Mukesh Rao

Senior Faculty, Academics, Great Learning

182.5K+ Learners

17 Courses

Prof. Mukesh Rao is a senior faculty of Data Science in Great Learning and he is responsible for designing data science courses offered and mentoring students with capstone projects. Prof. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science and has conducted extensive corporate training in Data Science and Big Data. He also works as a Data Science Trainer & Consultant for 4v Technologies and conducts training in core big data technologies and data science. He has headed Big Data teams at SourceOne and has worked with tech giants like Wipro Technologies.

Frequently Asked Questions

Will I receive a certificate upon completing this free course?

Yes, upon successful completion of the course and payment of the certificate fee, you will receive a completion certificate that you can add to your resume.

Is this course free?

Yes, you may enroll in the course and access the course content for free. However, if you wish to obtain a certificate upon completion, a non-refundable fee is applicable.

What will I learn in this free Python with AI course?

In this free AI with Python course, you will learn how artificial neural networks work, starting with their history, perceptrons, ANN architecture, activation functions, softmax, forward propagation, loss functions, back propagation, gradient descent, Keras, TensorFlow 2.0, and the MNIST dataset.

Is this free Python AI course suitable for beginners

This artificial intelligence using Python course is listed at the intermediate level. It works best for learners who already know basic Python and want a stronger understanding of neural networks, model training, and deep learning concepts.

Do I need prior Python knowledge to learn AI with Python in this course?

Yes. This learn Python for AI path is better suited for learners who already have some comfort with Python and basic technical concepts, since the course moves into ANN structure, TensorFlow, Keras, and model implementation.

What topics are covered in this course?

You will learn Python for AI modules in this course:

History behind neural networks
Relationship between biological neuron and artificial neuron
Perceptron and working mechanism
Architecture of artificial neural network
Types of activation functions
Softmax function
Forward propagation
Loss function
Demo using keras framework
Back propagation and gradient descent
Tensorflow 2.0
Demo on MNIST data set.

What tools and frameworks are included in this AI with Python free course?

In this Python AI training, you will work with Keras, TensorFlow 2.0, Jupyter Notebook, and the MNIST dataset. These tools help you move from theory into practical ANN model building.

Does this learn Python with AI course explain the math behind neural networks?

Yes. This free Python AI course covers the mathematical side of perceptrons, forward propagation, loss functions, back propagation, and gradient descent, so you understand how neural networks learn and improve.

Will I study classification concepts in this free artificial intelligence using Python course?

Yes. If you want to learn AI with Python, this course covers classification, activation functions, and softmax for multi-class problems, along with practical examples that show how models make predictions

Is this AI with Python course mostly theoretical, or does it include demos

This learn Python with AI course includes practical demos. You will see a Keras-based example and a working implementation on the MNIST dataset in Jupyter Notebook, which helps connect concepts with coding practice.

What skills will I gain from this Python AI training?

The skills you will gain in this course are:

AI Fundamentals
Python for AI
Neural Networks Basics
Biological vs. Artificial Neurons
Perceptron Mechanism
ANN Architecture
Activation Functions
Forward Propagation
Loss Functions
Keras Framework
Gradient Descent
MNIST Application
TensorFlow

How long does it take to complete this Learn AI with Python course?

This AI with Python free course includes about 11.25 hours of learning content. Since it follows a self-paced format, you may complete it at your own pace.

Is this a good choice if I want to learn Python for AI and build real ANN models?

Yes. This Python with AI course moves from neural network foundations to Keras, TensorFlow 2.0, and a practical MNIST implementation, making it useful for learners who want both theory and hands-on ANN model building.

What are the steps to enroll in this Artificial Intelligence with Python course?

Enrolling in Great Learning Academy's AI with Python is a simple and straightforward approach. You will have to sign-up with your E-Mail ID, enter your user details, and then you can start learning at your own pace.

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.

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.

.

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.

Will I have lifetime access to this free course?

Yes, once you enroll, you will have lifetime access to this Great Learning Academy's free course. You can log in and learn at your leisure.

What are my next learning options after this Artificial Intelligence with Python course?

Once you complete this free course, you can opt for a PG Program in Artificial Intelligence that will help advance your career growth in this leading field.

Will I get a certificate after completing this free Artificial Intelligence with Python course?

Yes, you will get a certificate of completion for this course after completing all the modules and cracking the quiz/assessment. The assessment tests your knowledge of both Artificial Intelligence and Python and badges your skills.

Is there any limit on how many times I can take this free course?

Once you enroll in this free Artificial Intelligence with Python course, you will have lifetime access to it. So, you can log in to the course anytime and learn it online at your leisure.

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Free AI with Python Course

Artificial Intelligence with Python

Stand out with an industry-recognized certificate

What you’ll learn

Key Highlights

About this course

Stand out with an industry-recognized certificate

Course outline

History behind neural networks

Relationship between biological neuron and artificial neuron

Perceptron and working mechanism

Architecture of artificial neural network

Types of activation functions

Softmax function

Forward propagation

Loss function

Demo using keras framework

Back propagation and gradient descent

Tensorflow 2.0

Demo on MNIST data set

This course is ideal for

Level up with advanced skills & become job ready with Pro+

Trusted by 10 Million+ Learners globally

Learner reviews of the Free Courses

Our course instructor

Frequently Asked Questions

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