Starting Machine Learning Journey At Different Career Levels

With the recent trend of Generative AI and Large Language Models (LLMs), everyone is considering joining the field of Machine Learning as it will be the future. Whether one is a college student, a top-tech manager, or even a leader in the tech industry, everyone can be seen talking about AI and studying it by following the same approach. But, is it true that at any career level, one needs to follow the same pathway of learning ML?

Even before that, does everyone need to know the ML to survive the recent recession in the tech industry? This was one of the most discussed topics among people working in the software industry. It is always worthwhile to be updated with the recent changes in the tech industry, but it is not always true to start doing hands-on or solving all the problems using AI. This blog will discuss how learners at different career levels should prepare themselves for starting the ML journey with step-wise strategies.

College Students

Machine Learning is not limited to any specific branch these days. It can solve tasks in the construction industry, machine manufacturing, and even pharmaceuticals. Hence, every college student, irrespective of their branches, should know the basics of ML to think of out-of-the-box solutions to some long-unsolved problems or replace traditional approaches to make the system more robust.

During college days, students can start investing a small amount of time in this order:

• Step 1: Start with Python programming language.
• Step 2: Learn the Basics of Data Structures and Algorithms
• Step 3: Learn the Basics of Machine Learning
• Step 4: Start playing with open-source datasets
• Step 5: Exploratory Data Analysis
• Step 6: Machine Learning Techniques, libraries, and frameworks
• Step 7: ML Algorithms along with Big Project, including deployment.

If you want detailed step-wise guidance for starting the machine learning journey, please read this blog: How to start ML journey step-by-step guidance.

Software Interns

Internships in tech firms are an opportunity to express the industry potential and excel in the field. During their internship, many students or professionals focus on solving the given problem statement via one of the methods, mostly traditional. But in actuality, they can start solving the given problem statement and keep learning and thinking if another way exists to solve it.

My ML journey also started during my internship at KPIT Technologies, where I used the Learning by doing philosophy. It is the best approach to strengthen any tech stack in the computer science domain. The learning strategy changes slightly, and we follow a parallel approach to Learning.

Interns can keep solving the given industry problem statement and simultaneously searching if some solution involving AI or ML exists. If they find some, it's a good hint that a possibility of integration exists. They can start learning ML basics and check where those theoretical aspects benefit them in the AI integration.

Steps to follow for interns to start their ML journey:

• Step 1: Search for existing AI solutions and how they can be utilized to solve the given industry problem statement.
• Step 2: Start with the basics of ML if you already know the basics of programming and DSA, and think about where exactly they can find the use of theory in solving the given problem statement using AI.
• Step 3: Finding the complete end-to-end solution using AI is not always true. Even if AI can be used to solve only a part of the complete problem statement, move towards integrating that.

Usually, these out-of-the-box solutions help you grab attention and pre-placement offers in the respective firms. Learning by doing strategy is also mentioned in the blog here.

Early Freshers

Freshers in any industry can think of learning ML and AI as their companions in solving challenging problems. During early career, most employees try to deliver whatever is assigned to them. However, with the knowledge of AI and ML, they can propose additional solutions to their employer or suggest alternative methods to a part of the solution and achieve robust performance.

This way of approaching any problem statement will enhance the collaboration opportunities, and they can even be capable of working in the core AI ML field. The best way to learn ML for freshers would be to start from the basics of Machine Learning like:

• What is ML, and where exactly is it used?
• What are the various applications of ML in the tech industry?
• How can ML be used in the domains they are currently working in, and how can AI solve their traditional problems?

Based on these questions, they can attend specific research paper conferences and seminars and participate in Hackathons after doing hands-on with basic ML projects falling in their domains. Examples of some popular applications of Machine Learning across broader technical domains are:

• ML in agriculture: Predicting: Soil fertility prediction
• ML in medical science: Cancer Classification
• ML for security: Credit card fraud detection

Experienced Professionals

After a lengthy discussion with many industry professionals, we discovered that most are willing to switch careers into AI. Still, they do not have time to invest in preparing for interviews and reading Machine Learning by giving extra time.

It is not true that to use AI, you always need to be directly present in firms that only focus on AI products like LLMs in Amazon Alexa, Google's DeepMind, or Tesla's Neuralink. They need to focus on applying the existing AI techniques to solve their general problems. Even before that, they first need to know whether the problem domain they are working on requires AI support.

Professionals need to consider AI a supporting technique to solve existing problems quickly. AI itself is nothing, but AI + X is a complete solution where X can be any business domain, like medical science, software engineering, rocket science, space programs, or anything else.

Experienced professionals can start using AI or integrating AI solutions into their existing problems and, in parallel, start reading the basics of Machine Learning. This is opposite to what we saw in the case of college students, but for experienced professionals,

Tech Managers

Technical managers are responsible for completing any system's pipeline; hence, they are overloaded with deliverables. However, with the tight schedule, the scope of innovation becomes limited. In such cases, tech managers need to consider which sub-modules in the pipeline can be solved via newer approaches, including AI. This can improve the robustness of the overall system. Usually, the team follows the instructions of tech managers, which adds to the responsibility of tech managers to provide initial insights on the solution.

But to understand and propose solutions, tech managers should be aware of the applications of Machine learning and AI specifically used in each of the modules in the pipeline. Technical managers should know more than the basics of ML, as when the team members are stuck somewhere, they need to guide them regarding some additional improvements like:

1. Applying feature engineering for better insights
2. Introducing regularizers to reduce overfitting
3. Designing key evaluation metrics to test the performance of the developed model.
4. What possible algorithms will be best suited for the given problem statement?
5. How to solve the challenges

Tech Leaders, including CEOs and CTOs

Tech Leaders attend many different seminars and business meetings where they listen to several projects and research papers. Leaders usually participate in these meetings to understand the business use case and find potential integrations into their platforms. Similarly, researchers showcase the potential of their products in a full-fledged manner with a lot of insights and technicalities.

Leaders should be aware of the terminologies, techniques, and potential business use cases in such cases. Leaders should know the fundamentals like

• What is Machine Learning, and how is it different from AI?
• What data infrastructure is present in the firm, and how can they utilize the existing data to perform Business Analytics?
• The knowledge about the classification of ML models on the basis of Input data: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
• The knowledge about the classification of ML models on the basis of the nature of the problem statement: Classification, Regression, and Clustering.
• Whether they need to spend money on making data labeled or not?
• How do various ML algorithms work from a theoretical angle? Leaders may not be doing directly hands-on, but they will be able to suggest possible directions to explore.

Conclusion

Machine Learning is one of the most demanding topics in the software industry, and people at all career stages want to learn it. The learning journey of Machine Learning can vary as per the individual requirements at various career stages, which is discussed in this blog. It outlines how a beginner at the career stages of 1. A student, 2. Intern 3. Early fresher, 4. Experienced professions, 5. Tech managers, and 6. Tech leaders can utilize the best way of Learning and learn this technology faster.

Enjoy Learning!

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