Neural Networks Fundamentals Course

10 Lesson

5 Hours

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igmGuru's Neural Networks Fundamentals course gives you a clear, structured path into one of AI's most powerful building blocks. From understanding how a neuron fires to building your first trained model, this program turns complex theory into practical skill. Whether you are a student or a working professional, this is where your deep learning journey starts.

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Overview

Neural networks power everything from voice assistants to medical diagnosis tools. This Introduction to Neural Networks course from igmGuru breaks down the core ideas behind artificial neural networks- how they are structured, how they learn from data, and how they are applied across real industries. Designed with beginners in mind, the curriculum steadily builds your understanding without overwhelming you with jargon, giving you both the theory and the confidence to use it.

Prerequisites

Basic familiarity with Python programming
High school-level mathematics (algebra and basic calculus concepts)
Fundamental understanding of what machine learning is (no hands-on experience required)
Exposure to linear algebra concepts such as matrices and vectors is helpful but not mandatory
A curious mindset and willingness to practice with code

Course Objectives

By completing this program, you will build a working knowledge of how neural networks think, learn, and perform- ready to apply across real AI use cases.

Understand the biological inspiration behind artificial neural networks
Grasp the structure and role of input, hidden, and output layers
Apply activation functions such as ReLU, Sigmoid, and Tanh correctly
Learn how forward propagation generates predictions from raw data
Understand the role of loss functions in measuring model error
Implement backpropagation to train a neural network effectively
Use gradient descent and its variants to optimize model weights
Identify and address overfitting using regularization strategies
Build and evaluate a basic neural network model using Python and NumPy

What You Will Learn

Here is a clear picture of the skills and concepts you will walk away with after completing the Neural Networks Fundamentals program at igmGuru:

The history and motivation behind neural networks, from the perceptron to modern deep learning
How a single artificial neuron processes inputs and produces an output
The architecture of multi-layer perceptrons (MLPs) and how depth adds learning power
How different activation functions change the behavior of neurons
The math behind forward propagation, explained step by step
How loss functions quantify the gap between predictions and actual values
Backpropagation: how gradients flow backward to adjust weights
Optimizers like SGD, Momentum, and Adam and when to use each
Techniques like dropout and batch normalization to improve training stability
How to implement a neural network from scratch using Python and NumPy
An introduction to popular frameworks like TensorFlow and Keras for practical model building
Real-world applications: image recognition, spam detection, and sentiment analysis

Who Is This Course For?

This course is built for anyone stepping into the world of AI - no prior deep learning experience needed.

Fresh graduates and students from engineering, science, or computer science backgrounds
Software developers and programmers curious about AI and machine learning
Data analysts looking to level up into machine learning roles
IT professionals planning a career shift into the AI domain
Entrepreneurs and product managers who want to understand neural network capabilities
Researchers from non-CS fields who work with large data sets
Anyone who has completed a basic Python or ML primer and is ready for the next step

Tools and Technologies Covered

You will get hands-on experience with industry-standard tools used by AI practitioners worldwide.

Python 3.x-primary programming language for all exercises
NumPy for matrix operations and building networks from scratch
Matplotlib for visualizing training progress and model behavior
Jupyter Notebook / Google Colab-interactive coding environment
TensorFlow 2.x & Keras for building and training neural network models with minimal boilerplate
Scikit-learn for dataset handling, preprocessing, and evaluation metrics

Career Outcomes

Neural network skills are among the most sought-after in the tech industry. This course positions you for roles that are growing faster than almost any other field.

Machine Learning Engineer-design and deploy learning systems at scale
AI Research Analyst-support model research, benchmarking, and experimentation
Deep Learning Developer-build specialized neural networks for vision, NLP, or audio tasks
Data Scientist-incorporate neural models into data pipelines and analytics workflows
NLP Engineer-apply sequence models to language and text understanding problems
Computer Vision Engineer-leverage convolutional networks for image-based tasks
AI Product Specialist-bridge the gap between technical teams and business stakeholders

Why Choose igmGuru for This Training?

igmGuru has been helping professionals upskill in emerging technologies for years, and this course is built with the same commitment to quality and career impact.

Live instructor-led sessions combined with self-paced recorded content
Industry-experienced trainers with real project backgrounds
Hands-on lab exercises and coding assignments in every module
Lifetime access to course recordings and updated materials
Dedicated doubt-clearing sessions and 1:1 mentorship support
Globally recognized course completion certificate from igmGuru
Job-ready curriculum aligned with what hiring teams actually look for
Active alumni community and placement support network

Key Features

100% Money-Back Guarantee
Flexible Schedule
24 X 7 Lifetime Support
100% Job Assistance
Experienced & Professional Trainers
1 On 1 Training Option Available

Course Curriculum

Lesson 1 - Introduction to Artificial Intelligence and Neural Networks

1. What is Artificial Intelligence and where do neural networks fit

2. Brief history: from perceptrons to deep learning

3. How biological neurons inspired artificial ones

4. Overview of use cases: vision, speech, NLP, and more

Lesson 2 - The Artificial Neuron and Perceptron Model

1. Structure of a single artificial neuron: inputs, weights, bias, and output

2. The perceptron algorithm and its decision boundary

3. Limitations of the perceptron for non-linear problems

4. Moving from single neurons to networks

Lesson 3 - Network Architecture-Layers and Connections

1. Input, hidden, and output layers explained

2. Fully connected (dense) layers and how data flows through them

3. Shallow vs. deep networks-what makes a network 'deep'

4. Common architectural patterns for beginners

Lesson 4 - Activation Functions

1. Why non-linearity matters in neural networks

2. Sigmoid and Tanh: history, formula, and limitations

3. ReLU and its variants: Leaky ReLU, PReLU, ELU

4. Softmax for multi-class output layers

Lesson 5 - Forward Propagation

1. Step-by-step walkthrough of how a prediction is made

2. Matrix multiplication in the forward pass

3. Computing the output for a simple neural network by hand

4. Vectorized implementation in Python/NumPy

Lesson 6 - Loss Functions and Model Evaluation

1. What a loss function measures and why it matters

2. Mean Squared Error for regression tasks

3. Binary and Categorical Cross-Entropy for classification

4. Reading and interpreting loss curves during training

Lesson 7 - Backpropagation and the Chain Rule

1. Intuition behind backpropagation: how errors guide learning

2. The chain rule of calculus applied to neural networks

3. Computing gradients layer by layer

4. Common pitfalls: vanishing and exploding gradients

Lesson 8 - Gradient Descent and Optimizers

1. How gradient descent updates weights to reduce loss

2. Batch, mini-batch, and stochastic gradient descent

3. Momentum and adaptive optimizers: RMSProp, Adam

4. Choosing learning rates and understanding their effect

Lesson 9 - Regularization and Training Best Practices

1. L1 and L2 regularization techniques

2. Dropout: randomly silencing neurons during training

3. Batch normalization for stable, faster training

4. Understanding overfitting and underfitting

5. Early stopping and model checkpointing

Lesson 10 - Building and Training Your First Neural Network

1. Implementing a neural network from scratch using Python and NumPy

2. Using Keras to build the same network with fewer lines of code

3. Training on a real dataset: handwritten digit classification (MNIST)

4. Evaluating performance: accuracy, confusion matrix, and loss plots

5. Saving and loading a trained model

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Course Fees

Online Class Room Program

US $ 199.00

100% Money Back Guarantee

Duration : 5 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 13 Aug 2026
Weekday Batch 17 Aug 2026
Weekend Batch 15 Aug 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

Neural Networks Fundamentals Certification

After completing the Neural Networks Fundamentals Course, you will receive an igmGuru Course Completion Certification. This certification validates your understanding of neural network fundamentals, deep learning concepts, activation functions, forward and backpropagation, model training, optimization techniques, and real-world AI applications.

FAQ's

Q1. Do I need a machine learning background before joining this course?

No. This Neural Networks Fundamentals course is designed for beginners. A basic understanding of Python and high school math is enough to get started.

Q2. How long does it take to complete the course?

The course is structured for 30 hours of learning. You can complete it at your own pace through self-paced recordings or attend live instructor-led sessions on a scheduled calendar.

Q3. Will I get a certificate after completing the course?

Yes. Upon successful completion, igmGuru awards a course completion certificate that you can share on LinkedIn, add to your resume, or use to demonstrate your AI proficiency to employers.

Q4. Is this course enough to get a job as an ML engineer?

This course builds the foundational knowledge you need. For job-readiness, we recommend pairing it with igmGuru's advanced deep learning and machine learning programs. Think of this as the solid first step.

Q5. Which programming language is used in the course?

All coding exercises are in Python 3.x. You will work with NumPy, Matplotlib, and Keras. Prior experience with Python basics is recommended but the course revisits essentials where needed.

Q6. Will I get access to the course materials after it ends?

Yes. Enrolled learners get lifetime access to all recorded sessions, notebooks, datasets, and any updated content that igmGuru adds to the curriculum over time.

Q7. Is this course suitable for non-programmers from business backgrounds?

The course is primarily built for learners with at least a basic Python background. Professionals with no coding experience may find the lab sections challenging, though the conceptual modules are accessible to all.

1-800-7430-173

(US Toll Free)

+91-7240-740-740

(India)