What You'll Learn
- Build and evaluate Machine Learning models using regression, classification, clustering, and ensemble techniques with proper validation and optimization.
- Design, train, and debug Deep Learning models including fully connected networks, CNNs, and sequence models (RNNs, LSTMs, GRUs) using PyTorch or TensorFlow.
- Understand and implement Transformer-based Large Language Models (LLMs), including attention, embeddings, tokenization, and fine-tuning concepts.
- Create production-ready Generative AI applications using prompt engineering, embeddings, semantic search, and Retrieval-Augmented Generation (RAG).
- Develop agentic AI systems that perform multi-step reasoning, tool calling, and task execution with memory and control mechanisms.
- Apply AI engineering best practices such as feature engineering, model optimization, reproducibility, cost control, evaluation, and performance tuning.
- Integrate AI models into real applications by designing full-stack architectures that connect backends, APIs, and user interfaces with AI systems
Requirements
- Basic Python programming knowledge
- Curiosity to understand how AI works under the hood
- No prior experience in Machine Learning, Deep Learning, or Generative AI is required — everything is explained from first principles to production.
Who This Course is For
- Aspiring AI Engineers
- Software Engineers transitioning into AI
- Data Analysts & Data Scientists upgrading to AI systems
- ML Engineers wanting Deep Learning and LLM expertise
- Students & professionals preparing for AI-focused roles
Your Instructor
Data Science Academy
Bridging knowledge to industry with Data & AI education
4.3 Instructor Rating
1,070 Reviews
50,911 Students
24 Courses
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