| Course Name | Duration | Classes | Total Fee | Mode of Training | Class Timing |
|---|---|---|---|---|---|
|
Artificial Intelligence
Course |
2 Months |
40 |
60,000 PKR |
Online & Face-to-Face |
To be decided mutually with students |
Artificial Intelligence Training Certification Course
Technocation provides an excellent faculty and qualified developers as there is a remarkable prospect in this field. One can make his/her Career with the help of both Artificial Intelligence Training and establish an identity and get guidance in Rawalpindi.
Therefore, we aim to shape inspiring students with in-depth training to meet the requirements of the IT industry and build substantial grounds in Artificial Intelligence Training by exhibiting students with various projects. Technocation also bestows the Best Artificial Intelligence Training Course in Rawalpindi, Islamabad.
We guide people from every background to change their lives via our career-oriented short-term courses in Rawalpindi. Our evening and online course primarily focus on school, college, university students, and full/part-time employees.
Advantages of Learning Artificial Intelligence
- Career Opportunities: Opens doors to high-demand jobs in tech and beyond.
- Innovation: Drives creativity in solving complex problems.
- Automation: Enables smarter and more efficient systems.
- Future Skills: Prepares for the technology of tomorrow.
- Versatility: Applicable across industries like healthcare, finance, and entertainment.
- Problem-Solving: Enhances decision-making with data-driven insights.
- Global Impact: Contributes to advancements in sustainability, security, and more.
Artificial Intelligence Training Certification Course Outline
This advanced-level course on Artificial Intelligence (AI) is designed for professionals and researchers who want to deepen their knowledge and skills in AI. The course covers advanced concepts in machine learning, deep learning, reinforcement learning, natural language processing, AI ethics, and AI deployment strategies. By the end of this course, participants will have a comprehensive understanding of state-of-the-art AI techniques and their applications.
Module 1:Machine Learning Techniques
- Ensemble Methods
- Bagging, Boosting, Stacking
- Advanced Optimization Algorithms
- Adam, RMSProp, SGD Variants
- Model Evaluation and Selection
- Cross-validation techniques
- Hyperparameter tuning (Grid Search, Random Search, Bayesian Optimization)
Module 2:Deep Learning Architectures
- Convolutional Neural Networks (CNNs)
- Advanced CNN Architectures (ResNet, Inception, EfficientNet)
- Recurrent Neural Networks (RNNs)
- LSTMs and GRUs
- Transformer Models (BERT, GPT, Vision Transformers)
- Generative Models
- GANs (Generative Adversarial Networks)
- Variational Autoencoders (VAEs)
Module 3:Reinforcement Learning (RL)
- Fundamentals of RL
- Model-Free vs Model-Based RL
- Advanced RL Algorithms
- Deep Q-Learning
- Policy Gradient Methods
- Proximal Policy Optimization (PPO) and Actor-Critic Methods
- Applications of RL in Robotics and Games
Module 4:Natural Language Processing (NLP)
- Advanced Text Representations
- Word Embeddings (GloVe, fastText)
- Contextual Embeddings (ELMo, BERT, GPT)
- Sequence-to-Sequence Models
- Machine Translation
- Summarization
- Large Language Models (LLMs)
- Fine-tuning and Prompt Engineering
- Applications in Chatbots and Content Generation
Module 5:Computer Vision
- Advanced Image Processing Techniques
- Object Detection and Segmentation
- YOLO, Faster R-CNN, Mask R-CNN
- Video Analysis and Action Recognition
- Weekly Assignments
- Midterm and Final Projects
- Certification upon successful completion of all modules and projects.
Module 6:AI Ethics and Governance
- Bias and Fairness in AI
- Interpretability and Explainability
- Privacy-Preserving Machine Learning
- Ethical AI Frameworks and Governance Policies
- Weekly office hours
- Discussion forums for peer-to-peer and instructor interaction
Module 7:AI in Production
- Deployment Strategies
- Model Optimization and Quantization
- Serving Models with TensorFlow Serving, TorchServe, etc.
- Monitoring and Maintaining AI Systems
- AI Infrastructure and MLOps
- Data Pipelines
- CI/CD for Machine Learning
Module 8:Specialized Topics (Optional)
- Federated Learning
- Edge AI
- AI for Healthcare, Finance, and Autonomous Systems
- Neuromorphic Computing
- AI practitioners and researchers
- Data scientists looking to specialize in AI
- Professionals in industries adopting AI solutions
Module 9: AI Course Features
- Hands-on Projects:
- Image Captioning with Attention Mechanisms
- Developing a Reinforcement Learning Agent for a Game
- Fine-tuning a Transformer Model for Text Summarization
- Research Paper Discussions:
- Key papers in AI like “Attention is All You Need” and “GANs”
- Tools and Frameworks:
- TensorFlow, PyTorch, Hugging Face Transformers
- OpenAI Gym, Unity ML-Agents
Experience and Inspiring Trainers:
Real-time Practice and Projects:
Start with introductory texts like Artificial Intelligence: A Guide to Intelligent Systems by Michael Negnevitsky or Deep Learning by Ian Goodfellow.
Prerequisites for Artificial Intelligence Course:
- Strong foundation in AI and machine learning basics.
- Proficiency in programming languages such as Python.
- Familiarity with linear algebra, calculus, probability, and statistics.
- Linear Algebra: Matrix operations, eigenvalues, eigenvectors.
- Probability and Statistics: Random variables, distributions, Bayes’ theorem.
- Calculus: Partial derivatives, gradients, chain rule (useful for machine learning).
Certificate:
Course Material:
Support and Careers Advice:
“Our Student Success is Our Mission”.