Artificial Intelligence (AI) is revolutionizing industries by enabling machines to mimic human intelligence. This course provides a comprehensive understanding of AI concepts, techniques, and real-world applications. Learn how AI is transforming business, healthcare, finance, and more. Whether you’re a beginner or an experienced professional, this course equips you with the skills to build intelligent systems. Dive into machine learning, neural networks, and AI algorithms to develop cutting-edge solutions. Join us at Techbriq and become proficient in AI, opening doors to exciting career opportunities in one of the fastest-growing fields.
This AI course is designed to provide hands-on training in artificial intelligence, covering fundamental to advanced concepts. You will explore AI models, machine learning techniques, deep learning frameworks, and ethical AI considerations. Through practical exercises and real-world projects, you will gain the skills required to develop AI-powered applications. Our expert-led training ensures you understand AI’s core principles, from data preprocessing to model deployment. Whether you aim to become an AI engineer, data scientist, or AI researcher, this course will guide you through the essential skills needed to excel in the AI domain.
- Fundamentals of Artificial Intelligence and its applications.
- Machine learning algorithms and neural networks.
- AI model training, evaluation, and optimization.
- Deep learning techniques using TensorFlow and PyTorch.
- Ethical considerations and responsible AI practices.
- Real-world AI project implementation and deployment.
Upon completing this course, you will receive an industry-recognized certification from Techbriq. This certification validates your expertise in Artificial Intelligence, making you a competitive candidate in the job market and empowering you with in-demand AI skills.
- Overview of AI, ML, and Deep Learning
- History and evolution of AI
- AI in real-world applications
- Supervised vs. Unsupervised Learning
- Regression and Classification algorithms
- Model training and evaluation
- Introduction to neural networks
- Convolutional Neural Networks (CNNs)L
- Recurrent Neural Networks (RNNs) and LSTMs
- Model deployment strategies
- AI ethics and bias mitigation
- Future trends in AI
- MongoDB (Mongoose ORM) and SQL (Sequelize ORM) integration
- Performing queries, joins, and relationships
- Using Redis for caching
- Best practices for scalability and performance
- Load balancing and clustering in Node.js
- Deploying applications on Heroku, AWS, and DigitalOcean
- Monitoring and debugging in production
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