AI and Machine Learning Roadmap for 2025

Hi everyone! In 2025, learning AI and ML is incredibly important due to the high demand in the industry. An average AI engineer in India earns around ₹10 lakh. If you're a skilled AI engineer placed in a top productbased company, your package could potentially reach ₹1 crore. This video provides a complete roadmap for learning Artificial Intelligence and Machine Learning in 2025, covering what topics to study, where to learn them, and the best resources available. By the end, you'll have complete clarity for your AI journey.

Step 1: Learning Mathematical Concepts

AI and ML heavily rely on mathematical concepts. To build good and serious AI working models, you cannot skip these mathematical concepts. Skipping them means you'll be copying and pasting code without truly understanding it. If there's an issue or bug, you won't be able to resolve it without the fundamental knowledge. These concepts form the foundation of your AI and ML journey.

Key Mathematical Concepts:

1. Linear Algebra

Topics include vectors, determinants, matrices, eigenvalues, and eigenvectors. Linear algebra helps in understanding machine learning models. It's used in image recognition and selfdriving cars. The best resource is the IIT Bombay NPTEL course.

2. Probability and Statistics

Covers mean, median, mode, standard deviation, and more. Used in recommendation systems. Khan Academy's YouTube channel is the recommended resource.

3. Calculus

Learn about derivatives, integrals, and gradients, which help in training and optimizing models. Check out the IIT Madras NPTEL course.

Step 2: Learning Python

While various programming languages can be used, Python is the most popular and recommended for AI/ML. It's wellsuited for data analysis and research.

Why Python?

Python boasts a large community, extensive libraries, and strong support for natural language processing and deep learning. It's easy to learn, readable, and integrates well with other languages. Focus on learning the fundamentals of Python necessary for AI/ML.

Essential Python Libraries

Learn libraries like NumPy, Pandas, Matplotlib, and Seaborn. These are essential for data computation, handling, and visualization. Krish Naik's YouTube channel is a great resource.

Step 3: Understanding Machine Learning Basics

Machine learning is the foundation of AI. Understanding its concepts is crucial for building intelligent applications. In simple terms, machine learning is a black box that takes input, performs operations using algorithms, and provides output. We train the algorithm on historical data (input and corresponding outputs) to predict future data.

It's a process of finding the best algorithms for a job based on previously known datasets and outcomes. It has two key components:

Data Manipulation

Cleaning and preprocessing input data for optimal model performance. NumPy and Pandas are vital for this.

Algorithms

Learning different machine learning algorithms like decision trees, linear regression, random forests, and clustering methods. Choose the algorithm that produces the best outputs for your use case. fast.ai's Machine Learning course is highly recommended.

Practical Projects

Implement projects to gain handson experience. Examples include spam detection and house price prediction. Find datasets on websites like Kaggle. Use Jupyter Notebook or Google Colab to implement your projects.

Step 4: Understanding Deep Learning

Deep learning is often the most interesting part of the AI/ML journey, allowing you to create AI models capable of complex tasks like image recognition and natural language processing. fast.ai's Deep Learning course is a great starting point to learn neural networks, perceptrons, activation functions, and backpropagation.

Deep learning improves and clarifies your understanding of machine learning basics. Different types of neural networks (CNN, RNN) are used for different purposes (image recognition, speech recognition, text generation). Implement projects based on these concepts.

Deep Learning Projects

Try face recognition and image classification projects. Implement models using TensorFlow and PyTorch. If you need to learn TensorFlow and PyTorch, try the deeplearning.ai course. Use pretrained models from Hugging Face, finetune them, and deploy your projects on platforms like GitHub and LinkedIn.

Step 5: Advanced AI Concepts

If you're interested in furthering your AI/ML journey, explore advanced concepts like Natural Language Processing (NLP), Computer Vision, and Reinforcement Learning. You can learn these concepts from the IIT Delhi NPTEL NLP course.

Conclusion

This roadmap helps you complete your AI journey in approximately 78 months. Be patient, as the concepts can be complex. Once you reach deep learning and neural networks, the concepts will become clearer, sparking your interest and enabling you to build better projects. Trust the roadmap and you will achieve great things in AI.

Hope you learned something from this video. Signing off! Take care, everyone, and I'll see you in the next video. Till then, keep preparing and be focused!