In the modern world of artificial intelligence, deep learning plays a crucial role in solving complex real-world problems. Deep learning, a subset of machine learning, leverages artificial neural networks (ANN) to mimic the human brain’s ability to learn patterns and make decisions. With the rise of tools like Keras, implementing deep learning models has become more accessible, even for beginners.
Online IT Guru offers a deep learning course designed to equip learners with both theoretical knowledge and hands-on skills. This comprehensive program focuses on AI deep learning with Keras, providing the foundation to build and deploy sophisticated models for tasks such as image classification, natural language processing, and predictive analytics.
Whether you are a data scientist, software engineer, or a tech enthusiast aiming to upskill in the field of artificial intelligence, this course delivers the practical expertise needed to thrive in today’s data-driven industries.
Why Choose Our Deep Learning Course
Our deep learning course combines the strength of AI concepts with practical exposure to popular frameworks like Keras and TensorFlow. The course is designed by industry experts and covers:
- The fundamentals of neural networks, deep learning, and their applications
- Building and training models using Keras
- Solving business problems with AI-driven solutions
- Working on real-world datasets through projects and case studies
What makes Online IT Guru unique is our focus on job-oriented training, lifetime access to learning material, and placement assistance.
What You Will Learn
The deep learning course at Online IT Guru takes you through a well-structured curriculum that includes:
1. Introduction to Artificial Intelligence and Deep Learning
- What is AI and how deep learning fits into the AI ecosystem
- History and evolution of neural networks
- Applications of deep learning in industries like healthcare, finance, retail, and more
2. Mathematical Foundations of Deep Learning
- Linear algebra and calculus for neural networks
- Probability theory and statistics in model evaluation
- Understanding gradient descent and backpropagation
3. Neural Network Architecture
- Structure of artificial neurons
- Activation functions: ReLU, sigmoid, tanh
- Layers: input, hidden, and output layers
- Overfitting, underfitting, and regularization techniques
4. Keras for Deep Learning
- Introduction to the Keras library
- Building your first deep learning model using Keras
- Model compilation, training, and evaluation
- Hyperparameter tuning in Keras
5. Advanced Architectures
- Convolutional Neural Networks (CNNs) for image analysis
- Recurrent Neural Networks (RNNs) and LSTMs for sequence data
- Transformers and their role in modern NLP
- Autoencoders for unsupervised learning
6. Natural Language Processing (NLP) with Deep Learning
- Tokenization, embeddings, and sequence modeling
- Building sentiment analysis models
- Text generation using RNNs and transformers
7. Computer Vision with Deep Learning
- Image classification and object detection
- Transfer learning with pretrained models
- Using Keras for image augmentation
8. Deploying Deep Learning Models
- Saving and loading Keras models
- Deploying models on cloud platforms
- Building APIs for AI services
9. Working on Real-Time Projects
Our deep learning course includes 2 capstone projects:
- Project 1: Image classification using CNN and Keras
- Project 2: Text sentiment classifier with RNN
Who Should Enroll
This deep learning course is ideal for:
- Data scientists and machine learning engineers
- Software developers looking to upskill in AI
- Analysts seeking to advance their careers with deep learning expertise
- Students and researchers in AI and machine learning fields
Prerequisites
While this course is beginner-friendly, having prior knowledge in the following areas is recommended:
- Basic Python programming
- Fundamental statistics and linear algebra
- Basic understanding of machine learning
Key Features of Our Deep Learning Course
- 30+ hours of high-quality video lessons
- Real-life case studies and projects
- 12 assignments designed to reinforce learning
- Lifetime access to LMS and resources
- Flexible self-paced and instructor-led training options
- 24/7 technical and academic support
- Certification upon completion
- Job assistance with resume forwarding and interview preparation
Course Syllabus Overview
Module 1: AI and Deep Learning Overview
This module introduces the foundational concepts that form the basis of the course.
- What is AI, Machine Learning, and Deep Learning?
- Artificial Intelligence (AI) refers to the broader concept of machines being able to carry out tasks that typically require human intelligence. This includes reasoning, learning, perception, and decision-making.
- Machine Learning (ML) is a subset of AI that focuses on algorithms that allow computers to learn from data and improve over time without being explicitly programmed.
- Deep Learning (DL) is a specialized branch of ML that uses artificial neural networks to model and solve complex problems. DL is particularly powerful for tasks like image recognition, speech processing, and natural language understanding.
- Use Cases Across Industries
- AI, ML, and DL are transforming industries worldwide. In healthcare, deep learning aids in medical image analysis and drug discovery. In finance, it powers fraud detection and algorithmic trading. In retail, it enhances personalized recommendations. Autonomous vehicles, smart assistants, and robotics are further examples where these technologies play a vital role.
Module 2: Introduction to Neural Networks
This module explores neural networks—the fundamental building blocks of deep learning.
- Biological vs Artificial Neurons
- Neural networks are inspired by the human brain’s structure. A biological neuron processes and transmits information through electrical and chemical signals. Similarly, an artificial neuron (or node) takes inputs, applies a function (usually non-linear), and generates output for the next layer.
- Artificial neural networks consist of layers of such neurons organized in various architectures to learn from data.
- Single-layer and Multi-layer Networks
- A single-layer network (also known as a perceptron) is the simplest form and can solve only linearly separable problems.
- Multi-layer networks (multi-layer perceptrons or MLPs) consist of input, hidden, and output layers. These can model complex, non-linear relationships thanks to activation functions applied at each layer.
Module 3: Deep Learning Techniques
This module focuses on the core techniques that help neural networks learn effectively.
- Activation Functions and Loss Functions
- Activation functions introduce non-linearity into neural networks, enabling them to learn complex patterns. Popular activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh.
- Loss functions measure how well a model’s predictions align with actual outcomes. Examples include mean squared error for regression and categorical cross-entropy for classification.
- Optimization Algorithms
- Training a neural network involves minimizing the loss function. Optimization algorithms like Stochastic Gradient Descent (SGD), Adam, and RMSprop adjust network weights to reduce error over time. Each has advantages for specific scenarios.
- Handling Overfitting and Data Imbalance
- Overfitting occurs when a model learns noise or irrelevant details from training data and performs poorly on new data. Techniques like dropout, regularization (L1/L2), and data augmentation help mitigate this.
- When classes are imbalanced (e.g., fraud detection where fraud cases are rare), methods like resampling, class weighting, and synthetic data generation (SMOTE) can improve performance.
Module 4: Building Neural Networks with Keras
This practical module teaches you to build models using Keras, a user-friendly deep learning API.
- Keras Architecture
- Keras provides a high-level interface to build deep learning models, running on top of frameworks like TensorFlow. It simplifies defining layers, compiling models, and managing training loops.
- Building and Compiling Models
- You’ll learn how to create models using Sequential and Functional APIs, define layers, choose activation and loss functions, and compile models with optimizers and metrics.
- Training and Validating Models
- Keras makes it easy to train models with model.fit() and validate them using test or validation datasets. You’ll also explore callbacks for monitoring progress, early stopping, and saving best-performing models.
Module 5: CNNs, RNNs, and Transformers
This module covers specialized architectures for advanced tasks.
- Deep Dive into CNN Architecture
- Convolutional Neural Networks (CNNs) are designed for processing grid-like data such as images. Key components include convolutional layers, pooling layers, and fully connected layers. CNNs excel at extracting spatial features, making them ideal for tasks like object detection, image segmentation, and facial recognition.
- Applications of RNN and LSTM
- Recurrent Neural Networks (RNNs) are designed for sequential data (e.g., time series, speech). They maintain memory of previous inputs to inform current processing. Long Short-Term Memory (LSTM) networks address RNNs’ limitations by handling long-term dependencies effectively. Applications include sentiment analysis, speech recognition, and language translation.
- Introduction to Transformer Models
- Transformers revolutionized natural language processing (NLP) by enabling models to focus on different parts of input sequences using attention mechanisms. They form the basis of state-of-the-art models like BERT and GPT.
Module 6: NLP and Computer Vision Applications
This module applies learned concepts to two major AI fields.
- Text Preprocessing and Embeddings
- In NLP tasks, text must be cleaned (lowercasing, removing stopwords) and converted into numeric form. Word embeddings (like Word2Vec or GloVe) map words to dense vector representations capturing semantic meaning.
- Sentiment Analysis and Text Generation
- Sentiment analysis classifies text into categories (positive, negative, neutral), widely used in social media monitoring and customer feedback. Text generation involves models learning patterns of language to produce new text, useful in chatbots and content creation.
- Image Augmentation and Classification
- Image augmentation techniques (flipping, rotation, cropping) artificially expand training datasets and help prevent overfitting. Image classification involves predicting the label of an image using trained CNNs.
Module 7: Advanced Topics and Deployment
This module focuses on taking models from research to production.
- Transfer Learning
- Transfer learning leverages pre-trained models (e.g., VGG, ResNet, BERT) and fine-tunes them for specific tasks, saving time and resources. This is especially effective when you have limited labeled data.
- Serving Models via APIs
- Once trained, models can be served through RESTful APIs, enabling integration with web and mobile applications for real-time predictions.
- Deploying on Cloud (AWS, Azure, GCP)
- You’ll explore how to deploy models on popular cloud platforms for scalable inference. Cloud services provide compute resources, managed infrastructure, and monitoring tools to support production workloads.
Module 8: Capstone Projects
This final module consolidates your learning through hands-on projects.
- CNN-based Image Classifier
- You’ll build a complete image classification system, from preprocessing and model design to training, validation, deep learning course and evaluation on real-world datasets (e.g., classifying animals or medical images).
- RNN-based Sentiment Analysis Tool
- You’ll design and train a sentiment analysis model using RNNs or LSTMs on text data such as movie reviews or tweets, learning to handle preprocessing, sequence padding, and evaluation metrics.
Benefits of Learning Deep Learning Online
- Learn at your own pace from anywhere
- Access to industry-relevant projects
- Hands-on experience with Keras, TensorFlow, and Python
- Opportunity to build a portfolio of deep learning projects
- Guidance for certification and placement
Career Opportunities After Completing the Deep Learning Course
Completing this deep learning course opens doors to roles such as:
- Deep Learning Engineer
- AI Researcher
- Computer Vision Specialist
- NLP Engineer
- Data Scientist
- AI Consultant
Companies across sectors including tech, healthcare, finance, automotive, and retail seek professionals skilled in AI deep learning and Keras.
Certification
Upon successful completion of the course and projects, learners will receive a certification from Online IT Guru, recognized across industries as proof of expertise in AI deep learning using Keras.
FAQs
1. What is the duration of the deep learning course?
The course includes 30+ hours of video content along with projects and assignments.
2. Do I need prior experience in AI or machine learning?
While helpful, beginners with basic Python and math knowledge can follow the course.
3. Will I get a certificate after completing the course?
Yes, you will receive a certificate that you can add to your resume and LinkedIn profile.
4. Does the course provide hands-on projects?
Yes, the course includes two capstone projects and several practical assignments.
5. Is job assistance provided after the course?
Online IT Guru provides placement support and resume forwarding to partner companies.
6. What tools will I learn in this course?
You will work extensively with Keras, TensorFlow, Python, and other relevant libraries.
7. Can I access the course material anytime?
Yes, lifetime access to LMS and learning resources is included.
8. How is the deep learning course delivered?
We offer both live online training and self-paced learning options.
9. What is the fee structure?
The course fee varies by batch and training mode. Discounts are available for group enrollments.
10. How do I enroll in the course?
You can enroll online through our website and access a free demo before joining.
Conclusion
Online IT Guru’s deep learning course is designed to transform learners into deep learning professionals ready for the AI-driven world. With a focus on AI deep learning with Keras, the course combines theoretical understanding and hands-on experience to help you build, deploy, and scale AI solutions.
By enrolling in this program, you gain the skills, confidence, and certification needed to advance your career in artificial intelligence and deep learning.