AI Engineer Roadmap – How to Learn AI in 2025

Covers essential mathematics for machine learning, deep learning, and large language models, providing the skills needed to thrive as an AI engineer in 2025.

Provides a step-by-step roadmap to becoming a world-class AI engineer, including understanding the broader ecosystem of AI and data science, exploring real-world transformative impact, focusing efforts, and becoming job-ready.

Explores the transformative impact of AI across various industries like healthcare, finance, retail, e-commerce, entertainment, and autonomous vehicles, highlighting the critical role of AI in changing industries globally.

Explains the significance of linear algebra in AI, covering key concepts like vectors, matrices, linear systems, transformations, determinants, and matrix operations essential for understanding deep learning models.

Discusses the role of statistics in AI, including topics like probability distributions, sampling, hypothesis testing, regression, and evaluation metrics crucial for modeling and analyzing data in AI applications.

Details the essentials of traditional machine learning, covering concepts like classification, regression, decision trees, model evaluation, and understanding different models to make informed decisions in AI engineering.

Explores different cutting-edge tools and concepts in deep learning, focusing on neural networks, optimization algorithms, backpropagation, gradient problems, regularization techniques, and understanding the architecture behind deep learning models.

Emphasizes the importance of learning advanced Python skills for AI applications, including data manipulation, visualization, loading diverse data types, and working with deep learning frameworks like TensorFlow.

Introduces generative AI concepts and large language models, discussing the evolution of AI technology, understanding Transformers, pre-training models, fine-tuning, prompt engineering, and downstream tasks for large language models.

Addresses ethical AI principles, bias, privacy, data security, regulations, and governance in AI applications, emphasizing the importance of ethical practices in the development and deployment of AI technology.