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Advanced AI & Deep Learning Training

Artificial Intelligence & Deep Learning Program

Learn Neural Networks, Deep Learning & AI Applications with Real-Time Projects

Build strong expertise in neural networks, deep learning architectures, and practical AI applications through real-time projects. Gain hands-on skills for advanced AI and deep learning career paths.

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Batch Details & Schedule

Choose a learning format and schedule that fits your lifestyle

Next Batch Starts

18th May 2026

Session Time

07:20 AM to 08:20 AM

Course Duration

45 days, 70 hours

Download Complete Curriculum

Course Features & Highlights

Everything you need to become a successful AI & ML professional

Live Projects

Work on real-world projects from day one with industry use cases

Expert Trainers

Learn from industry professionals with 10+ years of experience

100% Placement Support

Dedicated placement cell with assistance until you get hired

Industry Certification

Get certified and boost your resume with recognized credentials

LMS Access

Access to learning materials and recorded sessions

Mock Interviews

Weekly mock interviews to prepare you for real job scenarios

Resume Building

Professional resume preparation and LinkedIn profile optimization

Soft Skills Training

Communication, aptitude, and personality development sessions

Complete Course Curriculum

Comprehensive modules covering every aspect of Artificial Intelligence & Deep Learning

  • Module 1: Fundamentals of Artificial Intelligence
    • Introduction to AI
      • Artificial Intelligence Fundamentals
      • Types of AI
      • Real-World AI Applications
      • AI in Modern Industries
    • Python for AI
      • Python Basics
      • Data Types & Functions
      • NumPy & Pandas
      • Data Handling Techniques
    • Mathematics for Deep Learning
      • Statistics Fundamentals
      • Probability Concepts
      • Linear Algebra Basics
      • Data Visualization
  • Module 2: Data Preprocessing & Feature Engineering
    • Data Preprocessing
      • Data Cleaning Techniques
      • Handling Missing Values
      • Data Transformation
      • Normalization & Standardization
    • Exploratory Data Analysis (EDA)
      • Data Visualization
      • Correlation Analysis
      • Pattern Identification
    • Feature Engineering
      • Feature Selection
      • Feature Extraction
      • Dimensionality Reduction
  • Module 3: Fundamentals of Deep Learning
    • Introduction to Deep Learning
      • Deep Learning Concepts
      • Neural Networks Overview
      • Artificial Neural Networks (ANN)
    • Neural Network Components
      • Neurons & Layers
      • Activation Functions
      • Forward Propagation
      • Backpropagation
    • Optimization Techniques
      • Gradient Descent
      • Loss Functions
      • Optimizers
      • Hyperparameter Tuning
  • Module 4: Deep Learning Architectures
    • Convolutional Neural Networks (CNN)
      • CNN Architecture
      • Image Processing Concepts
      • Feature Maps & Pooling
      • Image Classification
    • Recurrent Neural Networks (RNN)
      • Sequence Modeling
      • Types of RNNs
      • Applications of RNNs
    • LSTM & GRU Networks
      • Long Short-Term Memory (LSTM)
      • Gated Recurrent Units (GRU)
      • Sequential Data Processing
  • Module 5: Advanced Deep Learning
    • Hands-On Coding Sessions
      • Model Building & Training
      • Deep Learning Pipeline Development
      • Model Evaluation
    • Model Deployment
      • API Integration
      • FastAPI Basics
      • AI Model Deployment Concepts
    • Interview Preparation
      • Deep Learning Interview Questions
      • Mock Interviews
      • Resume Building Support
  • Module 6: Real-World AI & Deep Learning Projects
    • Computer Vision Projects
      • Face Recognition
      • Object Detection
      • Image Classification
    • NLP & AI Projects
      • Sentiment Analysis
      • Text Classification
      • AI Chatbots
    • Deep Learning Applications
      • Recommendation Systems
      • Fraud Detection
      • Predictive Analytics
  • Module 7: Practical Implementation & Deployment
    • Transfer Learning
      • Pretrained Models
      • Fine-Tuning Techniques
      • Model Optimization
    • Transformers & Attention Models
      • Attention Mechanisms
      • Transformer Architecture
      • BERT Basics
      • GPT Models Introduction
    • Generative AI Concepts
      • Text Generation
      • Image Generation
      • AI Content Creation

Prerequisites & Eligibility

Course Prerequisites
Basic understanding of programming concepts and Python fundamentals is beneficial
Dedication to complete hands-on labs, case studies, and capstone projects
Commitment for the program duration (45 days, 70 hours)

Certification

Get industry-recognized certification that validates your skills and boosts your career prospects

  • Industry-recognized AI & Deep Learning course completion certificate from Avowal Data Systems
  • Digital certificate with a unique verification ID for easy authenticity checks
  • Covers neural networks, CNNs, RNNs, LSTMs, transformers, transfer learning, and real-world deep learning applications
  • Shareable on LinkedIn, resumes, portfolios, and other professional platforms
  • Valid proof of practical AI & deep learning skill development for employers, recruiters, and hiring teams
  • Includes project-based learning recognition and full course syllabus coverage
Frequent Questions

Find answers to common questions about our AI & Deep Learning training program

  • What will I learn in this AI & Deep Learning course?

    This course covers AI fundamentals, neural networks, CNNs, RNNs, LSTMs, transformers, transfer learning, model optimization, deployment concepts, and real-world deep learning applications across multiple industries.

  • Is prior programming knowledge required for an AI & Deep Learning course?

    Basic programming knowledge is helpful, especially Python fundamentals, but the course starts from core concepts and gradually progresses into advanced deep learning architectures and practical AI implementation.

  • Does this AI & Deep Learning course include hands-on projects and practical sessions?

    Yes, the course includes hands-on coding sessions, model building, training, deployment practice, and project work in areas like computer vision, NLP, recommendation systems, and predictive analytics.

  • Will I learn deep learning frameworks like TensorFlow and PyTorch?

    Yes. The program covers practical deep learning workflows using popular frameworks and tools for model building, training, optimization, experimentation, and deployment.

  • What career opportunities are available after completing AI & Deep Learning training?

    After completing AI & Deep Learning training, learners can pursue roles such as Deep Learning Engineer, AI Engineer, Machine Learning Engineer, Computer Vision Engineer, NLP Engineer, Data Scientist, and AI Research Associate.