What would be the right path to learn AI?

    Start with the basics of machine learning:
        Learn the core concepts of supervised, unsupervised, and reinforcement learning.
        Understand the key algorithms like linear regression, logistic regression, decision trees, random forests, etc.
        Get familiar with common evaluation metrics like accuracy, precision, recall, F1-score.

    Dive into deep learning:
        Learn the fundamentals of neural networks, including architectures like feedforward, convolutional, and recurrent neural networks.
        Understand the training process, loss functions, activation functions, and optimization techniques.
        Explore popular deep learning frameworks like TensorFlow, PyTorch, or Keras.

    Learn about data preprocessing and feature engineering:
        Understand how to handle missing data, outliers, and noisy data.
        Explore techniques for feature selection, dimensionality reduction, and data normalization.

    Practice implementing ML/DL models:
        Work through tutorials and build simple projects using the concepts you've learned.
        Try applying ML/DL to real-world datasets and problems in your domain.
        Participate in online coding challenges and competitions like Kaggle.

    Expand your knowledge:
        Learn about advanced topics like transfer learning, generative models, and reinforcement learning.
        Stay up-to-date with the latest research and trends in the AI/ML field.
        Consider taking online courses or pursuing a relevant certification.

    Develop practical skills:
        Learn how to deploy and monitor ML/DL models in production.
        Understand the challenges of scaling and optimizing AI systems.
        Familiarize yourself with tools for data visualization, model interpretation, and MLOps.