What do you do if you want to start a career in Machine Learning without a formal education?

Once you have gone through the basics of mathematics and programming, start with the Machine Learning course taught by Andrew Ng. It is one of the best courses to start your journey. Take your time and complete the course (try to complete the assignments by yourself). Upon completion of the course, you will have a solid grasp on the fundamentals of ML. You can then start solving the problems on Kaggle. Pick any problem and start exploring. Implement your baseline solution and keep improving it by using different techniques you have learnt earlier or learning currently. Once you are done with your code improvement, learn from the implementations that others have submitted. The more you practice, the more you will learn and explore.

The best way to start learning Machine Learning concepts is by building a strong foundation, Start by delving into fundamental courses on statistics and programming languages like Python, After this start with simple machine learning algorithms like Regression and Classification. There are various courses available online on platforms like Linkedin Learning, Coursera, and university-sponsored online modules offering comprehensive modules tailored to varying skill levels. However, To truly solidify theoretical knowledge one should engage in real-world projects and gain practical insights. Platforms like Kaggle provide an invaluable opportunity to apply your skills, collaborate with peers, and tackle diverse challenges.

Fundamentals are essential, If it comes to machine learning it starts from understanding the problem statement , Knowing your data , Cleaning or shaping data according to your model and lastly most important to know which model may make a best fit for your data.

Before delving into the intricate algorithms and frameworks of machine learning, it's essential to first master the foundational concepts and tools that support them. This entails understanding basic mathematical principles like linear algebra, calculus, and probability, alongside programming skills, particularly in Python, the predominant language in the field of ML. Numerous online resources, including Khan Academy, Coursera, and edX, offer courses, books, and tutorials covering these topics. Additionally, platforms like Kaggle or Google Colab provide opportunities to practice coding and data analysis collaboratively in an interactive setting.

Don't jump on the trend bandwagon and start straight from LLM, or GenAI when choosing a domain for your ML journey. These areas require a large amount of resources, knowledge and technical skills and in most cases not very friendly toward someone who does not have a formal background. Start from smaller domains that you are interested in such as Language Processing and recommendation Systems, and think of ways that might help you to serve your goal and background. By finding one that applies to your interests and background, you can leverage your own knowledge to ask the right questions and be able to absorb the subject much faster.

To embark on a career in machine learning without a formal education, it's crucial to begin by mastering the foundational concepts and tools that underpin this field. This involves gaining proficiency in fundamental mathematical principles such as linear algebra, calculus, and probability, alongside developing strong programming skills, particularly in Python, which serves as the lingua franca of machine learning.

Utilize online resources like tutorials, courses, and open-source projects to gain knowledge and experience. Build a strong portfolio showcasing your projects and contributions, and consider participating in competitions like Kaggle to demonstrate your abilities.

Choose a domain ML is a broad and diverse field that can be applied to many different domains, such as computer vision, natural language processing, recommender systems, or self-driving cars. Each domain has its own challenges, methods, and datasets that require specific knowledge and skills. To start a career in ML, you need to choose a domain that interests you and matches your goals and background. You can explore different domains by reading blogs, podcasts, or newsletters, such as Towards Data Science, DataCamp, or Machine Learning Mastery. You can also follow experts and influencers on social media, such as Twitter or LinkedIn, to stay updated on the latest trends and developments.

Creating an engaging portfolio to showcase machine learning skills and attract potential employers or clients is crucial. Start by replicating existing projects found online, such as on GitHub or Medium, and then enhance their performance by adding your own creative touch. Alternatively, delve into open-source or public datasets on platforms like Kaggle or UCI Machine Learning Repository and create your own projects from scratch. Document your projects meticulously, using tools like Jupyter Notebook or Markdown, and share them on platforms like GitHub or Portfolio to receive feedback and increase your visibility.

Networking and applying are crucial steps in starting a career in machine learning without a formal education. Networking involves building connections with professionals in the field, seeking mentorship, and actively engaging in communities where knowledge sharing and collaboration thrive.

It provides guidance on how to network and apply for jobs or projects in the machine learning field without a formal education. It includes networking tips such as joining online communities like Reddit, Stack Overflow, or Discord, as well as attending local events such as meetups, hackathons, or workshops. Additionally, it suggests reaching out to mentors, coaches, or peers for advice and referrals. The guide also offers advice on tailoring your resume, cover letter, and portfolio for each opportunity, highlighting relevant skills, experience, and achievements.

Network with professionals in the field, seek mentorship, and be proactive in seeking opportunities such as internships or freelance projects to gain practical experience and establish credibility in the industry.