[100% Off] Ai Enginner 2026 Complete Course, Gen Ai, Deep, Machine, Llm
Master Machine Learning, Deep Learning, LLMs & AI Systems with hands-on, real-world projects
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.
Description
“This course contains the use of artificial intelligence”
Artificial Intelligence is no longer about experimenting with isolated models or learning algorithms in theory. In 2026, companies are hiring AI Engineers who can work across the entire stack, from data understanding and machine learning to deep learning systems and Generative AI applications. If your goal is to land an AI Engineer job in 2026, this course is built for you.
This course is a complete Full-Stack AI Engineer program that brings together Machine Learning, Deep Learning, and Generative AI into one structured, end-to-end learning path. Instead of fragmented knowledge, you will gain a unified understanding of how modern AI systems are designed, trained, optimized, and deployed in real-world environments. Every concept in this course is taught with a strong focus on practical application, engineering mindset, and production readiness.
You will begin by building a solid foundation in Python for AI, data manipulation, and exploratory data analysis, learning how to understand data before modeling it. You will then move into core machine learning concepts, where you will work with regression, classification, ensemble methods, and unsupervised learning, while understanding critical ideas such as bias–variance tradeoff, model evaluation, feature engineering, and hyperparameter tuning. These skills form the backbone of real AI systems and are essential for any AI Engineer role.
As the course progresses, you will transition into Deep Learning, where you will learn how neural networks actually work under the hood. You will understand forward propagation, backpropagation, gradient descent, activation functions, and loss functions, and then implement these ideas using PyTorch or TensorFlow. You will build deep neural networks, work with convolutional neural networks for computer vision, and apply sequence models such as RNNs, LSTMs, and GRUs for time-series and text-based problems. You will also learn deep learning engineering best practices, including regularization, monitoring training behavior, reproducibility, and model versioning.
The course then takes you into the most in-demand area of AI today: Generative AI and Large Language Models. You will gain a clear understanding of transformer architecture, self-attention, embeddings, tokenization, and context windows, so you know how LLMs actually work rather than treating them as black boxes. You will learn how to work with modern models such as GPT, Claude, Gemini, and open-source LLMs, and understand their capabilities, limitations, cost considerations, and safety concerns.
You will also develop strong skills in Prompt Engineering, learning how to design prompts that are reliable, controllable, and robust, while avoiding common failure modes such as hallucinations and prompt injection. Beyond prompting, you will build embedding-based semantic search systems, implement Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in real data, and design tool-calling and function-based LLM applications that interact with external systems.
Finally, you will explore Agentic AI systems, where models can plan, reason, use tools, and execute multi-step tasks. You will learn how modern AI agents are structured, how memory and state are managed, and how these systems are used in real products. You will also understand evaluation strategies, cost optimization, latency tradeoffs, security risks, and responsible AI practices, ensuring you can build systems that are not only powerful but also safe and scalable.
This course is designed for anyone serious about becoming an AI Engineer, including software engineers transitioning into AI, data professionals upgrading their skill set, and students preparing for AI-focused roles. No prior experience in machine learning or deep learning is required, as everything is taught from first principles to production-level understanding.
By the end of this course, you will not just understand AI concepts. You will be able to design, build, and reason about real AI systems with confidence. If your goal is to secure an AI Engineer role in 2026 and beyond, this course provides the skills, structure, and depth required to get there.
