Sir, I am 15 year old boy and I want to be a AI Engineer so please tell me the road map as how I made AI engineer after 10th what stream I have to choose for beginners a Ai engineer what thing I have to choose I choose in college and some tell me as Python language is best and easiest language for Ai engineer software I started to learn python.

[Pranay7898]

[AhmedShafique313]

Phase 1: College Foundations (Build Core Computing Basics)
Start with introductory courses to establish a strong base in programming and computer science fundamentals:

Basic Programming: Enroll in courses like GW-BASIC to learn essential coding concepts and syntax.
Computer Fundamentals: Study hardware components, software principles, and operating systems (e.g., Windows, Linux) to understand system-level interactions.
Phase 2: Programming Proficiency (Master Python and Core Concepts)
Transition to practical programming skills, focusing on a versatile language like Python:

Python Journey: Begin with beginner-level tutorials (e.g., variables, loops, functions), progress to intermediate topics (e.g., file handling, libraries like NumPy), and advance to expert-level applications (e.g., scripting, automation).
Object-Oriented Programming (OOP): Learn classes, inheritance, polymorphism, and encapsulation to design modular code.
Data Structures and Algorithms (DSA): Cover arrays, linked lists, trees, graphs, sorting/searching algorithms, and time/space complexity analysis.
Discrete Mathematics: Study sets, logic, graph theory, and combinatorics to support algorithmic thinking.
Phase 3: Mathematical Foundations (Essential for AI/ML)
Strengthen quantitative skills through targeted math courses, as they underpin AI models:

Statistics: Focus on descriptive/inferential stats, hypothesis testing, and distributions.
Linear Algebra: Explore vectors, matrices, eigenvalues, and transformations for data manipulation.
Probability: Delve into random variables, Bayes' theorem, and stochastic processes for uncertainty modeling.
Phase 4: Advanced AI Specialization (From ML to Cutting-Edge Applications)
Apply your foundations to AI domains, progressing from core techniques to specialized and practical implementations:

Machine Learning (ML): Start with supervised/unsupervised learning, regression, classification, and tools like scikit-learn.
Deep Learning (DL): Advance to neural networks, CNNs, RNNs, and frameworks like TensorFlow or PyTorch.
Artificial Intelligence (AI): Study search algorithms, knowledge representation, and ethical considerations.
Natural Language Processing (NLP): Cover tokenization, sentiment analysis, transformers, and libraries like Hugging Face.
Large Language Models (LLMs): Explore models like GPT, fine-tuning, prompt engineering, and deployment challenges.
AI Agents: Learn multi-agent systems, reinforcement learning, and autonomous decision-making.
Software Development Life Cycle (SDLC): Integrate AI into full development processes, including agile methodologies, testing, deployment (e.g., MLOps), and maintenance.