Understanding the working of Neural Networks

  • BeginnerLevel

  • 1196+Students Enrolled

  • 2 Hrs Duration

  • 4.7Average Rating

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About this Course

  • This course introduces the neural network basics, explaining how neurons, weights, and activations build learning models.
  • Explore fundamentals of neural network design, including layers, forward propagation, and backward propagation.
  • Understand what is neural network in machine learning through intuitive visualizations and examples.
  • Learn the fundamentals of deep networks that form the backbone of modern AI.
  • Enroll in this neural network course free to grasp the foundations behind image and text recognition.
  • Strengthen your AI foundation with this short and practical course on deep learning principles.

Learning Outcomes

Neuron Fundamentals

Neurons, Activations & Weights Combining to Build an AI Model.

Network Architecture

Learn layer organization & fundamentals of neural network design.

Deep Learning Core

Explore fundamentals of deep networks and real-world AI applications.

Who Should Enroll

  • Beginners seeking to understand neural network basics from scratch.
  • AI or ML enthusiasts exploring what is neural network in machine learning.
  • Students wanting a neural network course free to start their AI journey.
  • Professionals building strong foundations in deep learning and AI concepts.

Course Curriculum

This free deep learning course introduces neural network basics through intuitive visuals, examples, and exercises. Learn fundamentals of neural network structures, how data flows through layers, and what is a neural network in machine learning.

tools

  1. 1. Exploring Deep Learning

  2. 2. Deep Learning Epochs

  3. 3. Neuron Know-How

  4. 4. Lets get Networking

  5. 5. Following the Framework

  6. 6. Scalars and Vectors

  7. 7. Building a neuron with PyTorch

  1. 1. How are Neural Networks trained - Forward Propagation

  2. 2. Understanding Loss Functions

  3. 3. Optimization Techniques - Gradient Descent

  4. 4. What is Back Propagation?

  5. 5. Types of Gradient Descent

  6. 6. Common Optimization Techniques

  7. 7. Building a Deep Neural Network

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Apoorv Vishnoi

Training Head, Analytics Vidhya

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

Get this Course Now

With this course you’ll get

  • 2 Hours

    Duration

  • Apoorv Vishnoi

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

A neural network is a model inspired by the human brain. It learns from data using connected layers of artificial neurons. These layers help identify patterns, classify data, and make accurate predictions automatically. Neural networks are the foundation of many AI systems today.

Neural networks include neurons, weights, activation functions, and layers. Each element plays a key role in data processing. This neural network basics course explains every component step by step. Learners can easily understand design principles through visuals, examples, and guided explanations.

Yes. This is a free neural network course open to all learners. It is perfect for beginners who want to understand how AI models learn and make predictions. The course explains everything clearly without requiring any prior programming or machine learning experience.

A neural network in machine learning helps systems learn from data. It recognizes patterns, processes inputs, and produces meaningful outputs. Neural networks are used in image recognition, speech processing, and natural language models. They help machines make accurate data-driven predictions and decisions.

Deep networks have several hidden layers between input and output. These layers capture complex data relationships. They improve accuracy and model performance. Deep networks form the basis of advanced AI systems like computer vision, natural language understanding, and predictive analytics.

Yes. The course is designed for complete beginners. It explains neural network basics using simple examples, analogies, and minimal math. Every topic builds progressively, helping learners understand key ideas quickly without prior coding or AI experience. It’s easy, structured, and practical.

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