Project 01
Image Classification Using CNN
Purpose
Develop a Deep Learning model that automatically identifies and classifies images into different categories.
Business Output
An image classification model that can recognize and categorize images.
Job-ready learning
Basic
Deep Learning.
Neural networks and modern deep-learning techniques for advanced AI applications.
Learner rating
4.8/5
Course duration
2 Months
Learning modules
13 modules
Learning mode
Online/ Offline
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Program overview
Deep Learning is a 2 Months Artificial Intelligence program built around hands-on practice, guided assignments, interview preparation, and placement support. Start with Neural Networks, practise with Python, TensorFlow, Keras, and connect your learning through applied business scenarios. Build a portfolio that explains your approach and prepare for roles such as Deep Learning Associate and Computer Vision Trainee.
2 Months structured training program
Hands-on practice with Python, TensorFlow, Keras
Live projects and portfolio-ready assignments
Certification guidance and placement assistance
A structured learning journey
Deep Learning Curriculum
Explore the curriculum, build your understanding one module at a time, and put each new skill into practice.
01Introduction to Deep LearningModule 1 · 1 topics
- What is Deep Learning? Introduction to Artificial Intelligence and Machine Learning Difference Between Machine Learning and Deep Learning Applications of Deep Learning Deep Learning Workflow Real-World Industry Use Cases
02Python Programming for Deep LearningModule 2 · 1 topics
- Python Fundamentals Variables, Data Types and Operators Conditional Statements and Loops Functions and Object-Oriented Programming NumPy for Numerical Computing Pandas for Data Handling Introduction to Jupyter Notebook and Google Colab
03Mathematics and Statistics for Deep LearningModule 3 · 1 topics
- Linear Algebra Fundamentals Vectors and Matrices Matrix Operations Probability and Statistics Derivatives and Calculus Basics Gradient and Gradient Descent Mathematical Foundations of Neural Networks
04Neural Network FundamentalsModule 4 · 1 topics
- Introduction to Artificial Neural Networks (ANN) Biological Neurons vs Artificial Neurons Perceptron Model Neural Network Architecture Input, Hidden and Output Layers Weights and Biases Forward Propagation
05Activation Functions and OptimizationModule 5 · 1 topics
- Linear Activation Function Sigmoid and Tanh Functions ReLU and Leaky ReLU Softmax Activation Function Loss Functions Gradient Descent Stochastic Gradient Descent Adam and RMSprop Optimizers
06Training Deep Neural NetworksModule 6 · 1 topics
- Backpropagation Algorithm Chain Rule and Weight Updates Epochs, Batch Size and Iterations Learning Rate Optimization Overfitting and Underfitting Dropout and Regularization Batch Normalization Model Performance Evaluation
07Deep Learning FrameworksModule 7 · 1 topics
- Introduction to TensorFlow Introduction to Keras Introduction to PyTorch Building Neural Networks Model Training and Validation Saving and Loading Models Comparing Deep Learning Frameworks
08Convolutional Neural Networks (CNN)Module 8 · 1 topics
- Introduction to Computer Vision CNN Architecture Convolution and Filters Padding and Stride Pooling Layers Flattening and Fully Connected Layers Image Classification Transfer Learning Fundamentals
09Recurrent Neural Networks (RNN)Module 9 · 1 topics
- Introduction to Sequential Data RNN Architecture Forward and Backward Propagation Through Time Vanishing and Exploding Gradients Long Short-Term Memory (LSTM) Gated Recurrent Units (GRU) Time Series Prediction
10Natural Language Processing with Deep LearningModule 10 · 1 topics
- Introduction to NLP Text Preprocessing Tokenization and Embeddings Word2Vec and GloVe Fundamentals Sequence Modelling RNN and LSTM for Text Processing Text Classification and Sentiment Analysis
11Advanced Deep LearningModule 11 · 1 topics
- Autoencoders Generative Adversarial Networks (GANs) Attention Mechanism Transformer Architecture Fundamentals Transfer Learning and Fine-Tuning Introduction to Large Language Models (LLMs)
12Deep Learning Model DeploymentModule 12 · 1 topics
- Model Saving and Serialization Model Deployment Fundamentals Building APIs Using Flask or FastAPI Creating AI Applications Using Streamlit Model Integration Model Monitoring Basics Introduction to MLOps
13Practical Projects and Career PreparationModule 13 · 1 topics
- End-to-End Deep Learning Workflow Real-World Industry Case Studies Hands-On Deep Learning Projects Model Evaluation and Optimization Git and GitHub Portfolio Resume Preparation Technical Interview Preparation Career Guidance
Find out how this curriculum fits your career goals.
Tools & technologies
Explore industry-relevant tools through hands-on learning to master practical, in-demand skills.
Python, Jupyter Notebook, Google Colab
NumPy, Pandas, Matplotlib
ensorFlow, Keras, PyTorch
OpenCV
NLTK, Hugging Face Transformers
Streamlit, Flask, FastAPI
Git, GitHub
Build practical skills with industry-relevant learning, expert guidance, and hands-on projects designed for modern teams.
Get the complete curriculum, tools, projects, and career support details in one brochure.
Build something that matters
6 Industry Projects
Every student completes 6 real-world projects — one per month — building a professional portfolio demonstrated during placement interviews.
Project 02
Handwritten Digit Recognition System
Purpose
Build a Deep Learning model that recognizes handwritten digits from images
Business Output
A trained model that accurately recognizes handwritten digits from 0 to 9
Project 03
Sentiment Analysis Using LSTM
Purpose
Develop a Deep Learning model that identifies whether a customer review expresses positive or negative sentiment.
Business Output
A sentiment analysis model that classifies customer reviews as positive or negative.
Project 04
Time Series Forecasting Using LSTM
Purpose
Develop a Deep Learning model that predicts future values using historical time-series data.
Business Output
A forecasting model that predicts future trends based on historical data.
Portfolio tip: document the problem, your approach, and the result so you can walk an interviewer through your decisions.
Who should enroll
- Machine learning learners advancing to deep learning
- Developers entering AI model development
- Engineering graduates
- Data professionals interested in neural networks
Learning modes
- Classroom training at Thane center
- Live online interactive sessions
- Recorded sessions for missed classes
- Free demo class before enrollment
Prerequisites
- Basic Python and machine learning knowledge recommended
- Basic linear algebra is helpful
- Own laptop for lab work
95% of our students successfully get placed after completing our programs.
Learn more about how we have been impacting thousands of careers.
- Apply Deep Learning skills to practical business scenarios
- Use Python, TensorFlow, Keras with confidence
- Build project work that can be shown in interviews
- Prepare for relevant certifications and job roles
Recognition that travels with you
Globally recognized certifications
Deep Learning Certificate
Certification pathway
Stand out with proof of practical skills.
Build a credible portfolio of skills with structured preparation, practical projects, and certification guidance from experienced mentors.
Beyond the classroom
Career Assistance Program
Soft skill session
Resume building
Aptitude training
LinkedIn profile building
Mock interview
Job assistance
Placement Assistance
Dedicated placement support with industry connections and recruiter interactions.
Dedicated placement cell with job referrals
Resume & LinkedIn profile optimization
Mock interviews with industry experts
Interview call scheduling support
Career counseling until you get placed
Access your complete learning experience through our all-in-one learning portal.
SkillUpAll-in-one
01Live Mentor Sessions
02AI-Powered Practice
03Progress Tracking
04Career support
One learning hub for your complete career journey
Your next opportunity starts here
Limited seats. High demand. Enroll today.
Industry-ready courseFilling fastGet industry-ready with Wraystech Academy
- Enroll for one course and get access to the complete learning journey
- In-depth curriculum with live mentor support
- Integrated projects and industry-recognized certifications
- Practice on real-world cases and career-ready assignments
- Dedicated placement guidance and interview preparation
CAREER OUTCOME
Offer letter preview
Congratulations!
Your practical skills, projects, and career preparation can help you approach interviews with clarity and confidence.
Role-ready preparation
Portfolio, interview practice, and mentor feedback.
Career counsellor
READY
A little more clarity
Frequently asked questions
Answers to common questions about learning, projects, and getting started.
What is Deep Learning?
Deep Learning is a specialized branch of Machine Learning that uses artificial neural networks with multiple layers to learn complex patterns from large amounts of data. It is widely used in image recognition, speech processing, NLP and AI applications.
What will I learn in the Deep Learning course?
You will learn Python programming, neural networks, activation functions, backpropagation, optimization techniques, CNN, RNN, LSTM, NLP, TensorFlow, Keras, PyTorch and model deployment.
Who can join the Deep Learning course?
Students, graduates, working professionals, software developers, data analysts and individuals interested in Artificial Intelligence and Machine Learning can join this course.
Do I need programming knowledge to learn Deep Learning?
Basic programming knowledge is helpful. Python is commonly used for Deep Learning, and learners will cover Python fundamentals before progressing to advanced concepts.
What are the prerequisites for learning Deep Learning?
Basic knowledge of Python, mathematics, statistics and Machine Learning is beneficial. However, learners can build these foundations through structured training and practice.
Which programming language is used in Deep Learning?
Python is the most commonly used programming language for Deep Learning because it offers powerful libraries and frameworks such as TensorFlow, Keras and PyTorch.
Which tools and technologies are covered in the Deep Learning course?
The course covers Python, Jupyter Notebook, Google Colab, NumPy, Pandas, TensorFlow, Keras, PyTorch, OpenCV, NLTK, Hugging Face Transformers and Streamlit.
What is the difference between Machine Learning and Deep Learning?
Machine Learning uses algorithms to identify patterns and make predictions from data. Deep Learning uses multi-layered neural networks to automatically learn complex features and patterns, often from large datasets.
What are CNN, RNN and LSTM in Deep Learning?
CNN (Convolutional Neural Network) is commonly used for image processing and computer vision. RNN (Recurrent Neural Network) works with sequential data, while LSTM (Long Short-Term Memory) is a type of RNN designed to capture longer-term dependencies.
Will I work on practical Deep Learning projects?
Yes. Students work on hands-on projects such as Image Classification Using CNN, Handwritten Digit Recognition, Sentiment Analysis Using LSTM and Time Series Forecasting Using LSTM
How long does it take to complete the Deep Learning course?
The course generally takes around 4–6 months, depending on the training schedule, learning pace and practical project requirements.
What career opportunities are available after completing the Deep Learning course?
Learners can explore opportunities such as Deep Learning Engineer, AI Developer, Machine Learning Engineer, Computer Vision Engineer and NLP Engineer, depending on their skills, qualifications and experience.
Is Deep Learning difficult to learn?
Deep Learning involves programming, mathematics and neural network concepts, which may seem challenging initially. With structured learning, regular coding practice and practical projects, learners can gradually develop their skills.
Will I receive a certificate after completing the Deep Learning course?
Certificate availability and eligibility depend on the institute's course completion and assessment requirements. Contact Raystech Academy for details.
Does Raystech Academy provide placement assistance after the Deep Learning course?
Raystech Academy provides career guidance and placement assistance to help students prepare for job opportunities through resume preparation, technical interview practice and career support.
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