Artificial Intelligence Course
Welcome to the AI Training Program at NIATT - National Institute of Advanced Technical Training.
Welcome to the AI Training Program at NIATT - National Institute of Advanced Technical Training. Designed for visionaries, innovators, and learners, this program bridges the gap between foundational AI concepts and real-world applications. Whether you’re a beginner or a professional, our hands-on approach and industry-relevant curriculum prepare you to thrive in the AI revolution.
- Who Should Enroll?
- Course Highlights
- Key Learning Outcomes
- Course Syllabus
- Interactive Learning: Engage in live projects, coding challenges, and case studies.
- Expert Guidance: Learn from seasoned AI professionals and researchers.
- Future-Ready Skills: Stay ahead with the latest AI tools and technologies.
- Flexible Learning Modes: Opt for classroom or online sessions at your convenience.
- Career Launchpad: Benefit from robust placement assistance and career coaching.
- Comprehensive Curriculum: From basics to advanced AI topics.
- Real-World Projects: Apply concepts to solve real-life challenges.
- Certifications: Globally recognized credentials for career advancement.
- Placement Support: Resume building, mock interviews, and job referrals.
Our course focuses on experiential learning through live projects such as:
- Predictive analytics for business decision-making.
- Real-time object detection for security systems.
- AI-powered sentiment analysis for social media insights.
Module 1: Introduction to AI and Machine Learning
- Understanding Artificial Intelligence: Concepts and History
- Real-World Applications: AI in healthcare, finance, and more
- Basics of Machine Learning: Types, tools, and processes
- Tools and Platforms: Python, Google Colab
Module 2: Supervised and Unsupervised Learning
- Linear and Logistic Regression
- Clustering Techniques: K-means, DBSCAN
- Decision Trees, Random Forest, and Gradient Boosting
- Practical Project: Predicting sales trends using ML
Module 3: Deep Learning and Neural Networks
- Fundamentals of Neural Networks: Perceptron, Backpropagation
- Advanced Architectures: CNNs, RNNs, and LSTMs
- Frameworks: TensorFlow and PyTorch in action
- Hands-On Project: Building an image recognition model
Module 4: Natural Language Processing (NLP)
- Fundamentals of NLP: Tokenization, Stemming, and Lemmatization
- Sentiment Analysis and Language Translation
- Tools: NLTK, SpaCy, Hugging Face Transformers
- Capstone Project: Developing an AI-powered Chatbot
Module 5: Computer Vision
- Image Preprocessing and Augmentation
- Object Detection with YOLO and OpenCV
- Real-Time Face Recognition Systems
- Project: Automated vehicle detection system
Module 6: AI Ethics and Future Trends
- Ethical Challenges in AI: Bias, fairness, and transparency
- The Role of AI in Industry 4.0
- Exploring Quantum AI and Edge AI
- Case Study: Implementing ethical AI in business
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