Artificial Intelligence Basics for AI Engineers

Explore the birth of artificial intelligence, key milestones, early theories, breakthroughs, and how AI evolved from research concepts to real world systems.

Understand different AI types, behavioral models, reactive and adaptive intelligence, and how machines learn to make decisions.

Learn ethical AI principles, responsible usage, safety guidelines, bias mitigation, and rules for building trustworthy artificial intelligence systems.

Understand the AI pipeline from data collection and preprocessing to model training, evaluation, deployment, and real world decision making.

Understand the hierarchy of artificial intelligence, machine learning, and deep learning, including key differences, relationships, and practical use cases.

Learn BFS, DFS, and A* search techniques to solve AI problems efficiently using informed and uninformed search strategies.

Explore optimization techniques used in AI to improve solutions, reduce cost, enhance performance, and solve complex problems efficiently.

Understand classification and clustering concepts, supervised and unsupervised learning, and how AI models identify patterns in data.

Learn neural network basics, including neurons, layers, activation functions, and how models learn from data to make predictions.

Explore perceptrons and deep learning models including ANN, CNN, and RNN, understanding architectures, learning processes, and real world applications.

Set up Python and install essential libraries like NumPy and Pandas to start building AI and data science projects.

Learn supervised, unsupervised, and reinforcement learning paradigms and how each is used to solve different machine learning problems.

Understand how to train, evaluate, and validate machine learning models using real datasets and performance metrics.

Learn techniques to optimize machine learning models, improve performance, reduce overfitting, and fine tune parameters for better accuracy.

Learn generative AI fundamentals, how models create text and images, and where generative systems are used in real applications.

Understand large language models, how LLMs work, their architecture, training process, and practical applications in modern AI systems.

Learn how multimodal AI combines text, image, and audio data to build intelligent systems with richer understanding and responses.

Understand retrieval augmented generation, how RAG enhances AI responses using external knowledge sources and real time data.

Learn transformer architecture, attention mechanisms, and why transformers power modern language models and advanced AI applications.

Explore agentic AI concepts, autonomous systems, decision making agents, and how AI systems plan, act, and adapt independently.