Don't Learn Machine Learning, Instead learn this!

Deepchand O A

8 Sept 202406:20

Summary

TLDRDeep Chen discusses the challenges of pursuing machine learning in 2024, highlighting its complexity and the need for a solid foundation in math, statistics, and programming. He notes the current job market's preference for experienced individuals over freshers due to the economic slowdown. Chen suggests that generative AI, which focuses on prompt engineering with large language models (LLMs), is a more accessible field for freshers. He advises learning backend and frontend development to complement basic machine learning knowledge for roles in generative AI, positioning oneself as an AI/ML engineer. For those not interested in full-stack development, alternative careers in data science are recommended.

Takeaways

🧠 Machine Learning is complex and requires a solid foundation in mathematics, statistics, and programming.
📚 Learning ML is not a quick process; it demands years of practice and deep understanding of algorithms.
🌐 The job market for ML and data science roles has tightened due to economic slowdown, favoring experienced candidates.
🔍 Despite learning ML, freshers often lack the industry experience compared to those with 2-3 years of experience.
💼 Companies are increasingly seeking experienced individuals for ML and data science roles, making it difficult for freshers to break in.
🚀 The market is shifting towards Generative AI, which is less about deep ML knowledge and more about prompt engineering with LLMs.
💼 For those with ML experience, transitioning to Generative AI roles might be easier, as it requires less in-depth ML knowledge.
🛠️ If aiming for Generative AI roles, having a basic understanding of ML and DL, along with backend and frontend development skills, is beneficial.
🎓 For those deeply interested in ML research, pursuing higher education like a master's or PhD is recommended.
🔄 Other career options in the data science industry for freshers include data engineering, MLOps engineering, AI product management, and data analysis.

Q & A

Why does the speaker suggest that machine learning might not be a good choice for some people in 2024?
-The speaker suggests that machine learning might not be a good choice due to its complexity, the need for a solid foundation in mathematics, statistics, and programming, and the current economic slowdown that has led to fewer job opportunities for freshers in the field.
What are the challenges faced by beginners when learning machine learning according to the script?
-Beginners face challenges such as the difficulty of learning the subject, the need for a solid foundation in related fields, and the complexity of topics that can be overwhelming, leading many to give up.
How has the job market for machine learning and data science roles changed according to the speaker?
-The job market has become more competitive and less welcoming to freshers due to the economic slowdown, with companies preferring experienced candidates and a shift towards roles that involve generative AI.
What is the role of generative AI in the current AI job market as per the speaker?
-Generative AI is becoming a more prominent role in the AI job market, as it involves working with large language models (LLMs) and prompt engineering, which is less technically demanding compared to traditional machine learning roles.
Why does the speaker recommend generative AI roles for freshers over traditional machine learning roles?
-The speaker recommends generative AI roles because they are less demanding in terms of deep machine learning knowledge, focus more on prompt engineering and API usage, and are in higher demand due to the current market shift towards AI integration.
What advice does the speaker give to those who have learned machine learning and deep learning but are not sure about their career path?
-The speaker advises those with knowledge in machine learning and deep learning to consider roles in generative AI, which may require some machine learning background but also involve full-stack development skills.
What are some alternative career paths in the AI field that the speaker suggests for freshers?
-The speaker suggests alternative careers such as data engineer, MLOps engineer, AI product manager, or data analyst, which may not require as deep machine learning expertise as traditional machine learning roles.
What skills does the speaker recommend learning for someone interested in generative AI roles?
-For generative AI roles, the speaker recommends learning backend development with frameworks like Django, FastAPI, or Flask for Python, and front-end development with HTML, CSS, and frameworks like React, Angular, or Vue.js.
How does the speaker describe the future of AI products in relation to generative AI?
-The speaker describes the future of AI products as being heavily based on generative AI, with many apps integrating chatbots and companies automating tasks using AI, indicating a growing demand for generative AI skills.
What does the speaker suggest for those who are passionate about deep machine learning concepts and research?
-For those passionate about deep machine learning and research, the speaker suggests pursuing higher education like a master's or a PhD to contribute significantly to the industry.

Outlines

00:00

🤖 The Complexity of Machine Learning in 2024

Deep Chen discusses the challenges of learning machine learning in 2024, particularly for freshers. He highlights the complexity of the subject, which requires a solid foundation in mathematics, statistics, and programming. The video emphasizes that mastering machine learning is not a quick process and that the job market has shifted, making it difficult for beginners to find roles in machine learning and data science due to an economic slowdown and a preference for experienced professionals. The speaker suggests that generative AI, which involves prompt engineering with large language models (LLMs), is a more accessible field for newcomers, as it does not require in-depth machine learning expertise.

05:02

🎓 Alternative Career Paths in AI for Fresh Graduates

The speaker advises fresh graduates to consider generative AI roles over traditional machine learning or data science positions due to the current market trends. They recommend learning backend development, such as Django, FastAPI, Flask for Python, or Express JS for JavaScript, and front-end technologies like HTML, CSS, and frameworks like React, Angular, or Vue.js. This skill set can lead to roles as an AI/ML engineer, which may still involve some machine learning tasks but are less demanding than traditional data science roles. For those not interested in full-stack development, alternative careers like data engineer, MLOps engineer, AI product manager, or data analyst are suggested. The speaker also offers to provide a full roadmap for generative AI in the comments section and encourages viewers to engage with the content.

Mindmap

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that provides systems the ability to learn from data, identify patterns, and make decisions with minimal human intervention. In the video, the speaker suggests that due to its complexity and the current job market conditions, machine learning might not be the best choice for beginners in 2024. The script mentions that mastering machine learning requires a solid foundation in mathematics, statistics, and programming, and years of practice.

💡Complexity

Complexity, in the context of the video, refers to the difficulty level associated with learning and mastering machine learning. The speaker points out that machine learning is not something that can be learned overnight and that it involves complex topics that can be overwhelming for beginners. This concept is used to highlight the challenges faced by freshers trying to break into the field of AI and machine learning.

💡Economic Slowdown

Economic slowdown is mentioned as a factor affecting the job market for machine learning and data science roles. The script explains that due to the economic conditions, companies have become more cautious about hiring fresh graduates for these roles, which has made it tougher for beginners to find jobs in the field.

💡Generative AI

Generative AI is a term used in the video to describe a shift in the AI job market towards roles that involve working with large language models (LLMs) and prompt engineering. The speaker suggests that generative AI roles might be more accessible for freshers compared to traditional machine learning roles, as they often require less in-depth machine learning knowledge and more focus on API usage and backend development.

💡Prompt Engineering

Prompt engineering is a skill mentioned in the video that involves crafting input prompts to guide large language models (LLMs) to generate desired outputs. This is a key aspect of generative AI roles, where the ability to effectively prompt an LLM is more important than deep machine learning expertise.

💡Full Stack Development

Full stack development refers to the ability to work on both the front-end and back-end aspects of web and software development. The video suggests that freshers might want to consider learning full stack development if they are interested in AI roles, as it can provide a broader skill set that is in demand in the job market.

💡Data Science

Data science is a field that involves the analysis and interpretation of complex digital data from various sources, using various techniques and tools. In the video, the speaker discusses how the demand for pure data science roles has decreased, and how freshers might want to consider other AI-related roles or fields.

💡Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers to model and understand complex patterns in data. The script mentions deep learning in the context of the skills that might be useful for generative AI roles, even if the role itself does not require deep expertise in machine learning algorithms.

💡Frameworks

Frameworks in the video refer to the software structures or systems that developers use to build applications or websites. Examples mentioned include Django, Flask, Express JS, React, Angular, and Vue.js. These frameworks are part of the skill set that the speaker suggests freshers might want to learn to transition into AI roles.

💡AI Product Manager

An AI product manager is a role mentioned in the video as an alternative career path for those interested in AI but not necessarily in machine learning or data science. This role involves managing the development of AI products, requiring a combination of technical knowledge and business acumen.

Highlights

Machine learning in 2024 is not a good choice for freshers due to its complexity and the current job market.

Machine learning requires a strong foundation in mathematics, statistics, and programming, making it difficult for beginners to master quickly.

Many learners give up on machine learning because of the overwhelming complexity of its topics.

In 2024, companies are hiring fewer freshers for machine learning and data science roles due to an economic slowdown and preference for experienced candidates.

Even internships in machine learning and data science don't guarantee job opportunities in the current market.

The job market has shifted away from pure machine learning and data science roles towards generative AI, especially for freshers.

Generative AI roles are less technically demanding than machine learning, focusing more on working with large language models (LLMs) and prompt engineering.

Big tech companies are leading the LLM race, making it easier for smaller companies to build AI products using LLMs.

Freshers can pivot to roles in generative AI by learning full-stack development and integrating LLM APIs.

Generative AI positions often require knowledge of backend technologies like Django, FastAPI, or Express.js, and frontend development skills using React, Angular, or Vue.js.

If a fresher prefers not to pursue full-stack development, they can explore alternative AI roles like data engineer, MLOps engineer, AI product manager, or data analyst.

Generative AI is becoming the dominant field in AI, with increasing demand for professionals to build and integrate chatbots and automation tools.

For freshers wanting to stay in AI without deep machine learning expertise, generative AI is the most accessible and future-proof career path.

A research-focused career in machine learning might be better suited for those who are deeply interested in complex algorithms and can pursue advanced degrees like a master's or PhD.

The speaker emphasizes that the future of AI will revolve around generative AI products, and freshers should align their skillsets to meet this demand.