Artificial Intelligence Tutorial For Beginners | Edureka

This comprehensive course on Artificial Intelligence, presented by Edureka, is meticulously designed to guide learners from foundational concepts to advanced applications in the field of AI. Aimed primarily at beginners, it offers a structured pathway to understand the intricacies of artificial intelligence, machine learning, and deep learning, while also providing valuable insights for those seeking to enhance their knowledge. The course begins with an introduction to AI, exploring its definitions, historical context, and core principles that distinguish it from human intelligence. It delves into the various types of AI, such as narrow and general intelligence, setting the stage for a deeper dive into machine learning—the engine behind many modern AI systems. Through clear explanations, learners will grasp how machine learning algorithms, including supervised, unsupervised, and reinforcement learning, work to analyze data and make predictions, with practical examples illustrating their real-world impact.

As the course progresses, it transitions into deep learning, a subset of machine learning that has revolutionized areas like computer vision and natural language processing. Detailed tutorials cover neural networks, from basic artificial neural networks to more complex architectures like convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for sequential data. Hands-on sessions with popular frameworks such as TensorFlow and Keras empower students to build and train their own AI models using Python, bridging theory with practice. Beyond technical skills, the course addresses the broader implications of AI, including its applications across industries like healthcare, gaming, and business, as well as the ethical considerations and potential dangers associated with its deployment. It also offers career guidance, outlining step-by-step roadmaps to becoming an AI engineer, essential skills for 2025 and beyond, and project ideas to apply learning in practical scenarios.

Key Points Covered in This Course:
- Introduction to Artificial Intelligence: Core definitions, types, and historical evolution of AI.
- Machine Learning Fundamentals: Overview of ML algorithms, data training processes, and real-world use cases.
- Deep Learning and Neural Networks: In-depth exploration of neural network architectures, including CNNs and RNNs, with implementation guides.
- AI Algorithms and Techniques: Coverage of search algorithms like BFS and A*, optimization methods, and clustering techniques.
- Practical Tools and Frameworks: Hands-on tutorials with TensorFlow, Keras, and generative AI tools such as Midjourney.
- Applications and Ethics: Discussion on AI's impact in various sectors, ethical frameworks, and societal considerations.
- Career Development: Roadmaps for AI engineers, skill requirements, and future trends in the AI job market.