How I’d Learn Machine Learning in 2024 (If I Were Starting Over)

Summary

In this video, Smitha discusses an updated roadmap for learning machine learning in 2024, focusing on new industry trends like generative AI. She emphasizes the importance of understanding key concepts like Transformer architecture, optimization, and deep learning, while also recommending resources for hands-on experience and project building. Smitha suggests starting with foundational courses and gradually advancing skills in areas like PyTorch and natural language processing. She highlights the growing demand for AI professionals, particularly in generative AI, and provides a comprehensive learning path to help viewers excel in the evolving AI/ML landscape.

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Q & A

What is the main focus of the video by Smitha?

The main focus of the video is how to effectively learn machine learning in 2024, considering the evolving landscape of AI and ML, including the rise of generative AI.

How has the investment in generative AI changed from 2022 to 2024 according to the Stanford State of AI report?

Generative AI investment skyrocketed between 2022 and 2024, growing nearly eightfold to reach $25.2 billion by 2024.

Why is it important to understand the current landscape of AI/ML in 2024?

It is important because the AI/ML landscape has seen significant growth, especially with the rise of generative AI, and understanding the trends will help learners focus on relevant skills and job opportunities.

What is the first step Smitha recommends for someone starting to learn machine learning?

The first step is to take a foundational machine learning course that explains the basics effectively. Smitha recommends the School of Machine Learning’s 'Introduction to Machine Learning in Python' course.

Which ML concepts are essential to understand after completing a foundational course?

Key concepts include Transformer architecture (important for NLP and multimodal models), gradient descent and optimization, mathematics for machine learning, deep learning, and model evaluation metrics.

Why does Smitha suggest learning Transformer architecture in 2024?

Transformer architecture is essential for working with advanced AI models like BERT and GPT, which are widely used in natural language processing and other modern AI applications.

What is Smitha's advice on learning mathematics for machine learning?

Smitha suggests that learners do not need to master mathematics for ML at the beginning. Instead, they can learn it gradually as they progress, using resources such as GitHub repositories dedicated to ML mathematics.

What are the options Smitha suggests for gaining hands-on experience in machine learning?

She suggests collaborating with other learners, building your own project, or pursuing apprenticeships. Additionally, using public datasets from resources like GitHub can help learners build practical ML projects.

What is the suggested tool for deep learning in 2024, according to Smitha?

Smitha suggests learning PyTorch, as it has become more widely used in the industry compared to TensorFlow, especially in deep learning applications.

Why is understanding cloud deployment important for machine learning roles?

Cloud deployment is crucial because many tech companies and startups use cloud providers like Microsoft Azure, AWS, or Google Cloud to deploy machine learning models. Having basic knowledge of ML pipelines and cloud deployment is essential for job readiness.