You're looking to build a career in Machine Learning. What's the best way to create a development plan?

1 Assess your current level

The first step in creating a development plan is to assess your current level of ML skills and knowledge. You can use various online resources, such as Coursera, Udemy, or Kaggle, to take courses, quizzes, or projects that test your proficiency in different aspects of ML, such as data analysis, programming, algorithms, mathematics, statistics, or domain knowledge. You can also ask for feedback from your peers, mentors, or managers, or look at job descriptions or portfolios of ML professionals that you admire or aspire to be. This will help you identify your strengths and weaknesses, as well as your gaps and areas for improvement.

3 Choose your learning resources and methods

The third step is to choose your learning resources and methods that will help you achieve your goals and objectives. There are many options available for learning ML, such as online courses, books, podcasts, blogs, videos, webinars, workshops, bootcamps, or conferences. You can also learn from other ML experts or practitioners, such as mentors, coaches, instructors, or colleagues. You can join online or offline communities, such as forums, groups, or clubs, where you can exchange ideas, ask questions, or collaborate on projects. You can also learn by doing, such as by working on real-world or simulated ML problems, datasets, or challenges. You should choose the resources and methods that match your learning style, preferences, and budget.

4 Create a learning schedule and track your progress

The fourth step is to create a learning schedule and track your progress. You should allocate a specific amount of time and energy for your learning activities, and stick to it as much as possible. You can use tools, such as calendars, planners, or apps, to organize your learning schedule and remind you of your deadlines and milestones. You should also track your progress regularly, and evaluate your performance and outcomes. You can use tools, such as journals, logs, or portfolios, to document your learning process and achievements. You should also celebrate your successes and reward yourself for your efforts.

5 Seek feedback and guidance

The fifth step is to seek feedback and guidance from others who can help you improve your ML skills and knowledge. You should be open to constructive criticism and suggestions, and learn from your mistakes and failures. You should also seek advice and support from people who have more experience or expertise in ML, such as mentors, coaches, instructors, or managers. You can ask them for tips, recommendations, or feedback on your learning goals, plans, or outcomes. You can also seek feedback and guidance from your peers, such as fellow learners, colleagues, or friends. You can share your learning experiences, challenges, or achievements with them, and ask for their opinions, insights, or feedback.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?