Introduction: Artificial intelligence coding is a new literacy. It is hoped that in the future, people will be able to write artificial intelligence code as commonly as they can read and write now. Because code is the deepest form of communication between humans and machines, and artificial intelligence and data science are of great value to many professions.
Chapter 1: The three steps of career growth are learning basic skills, participating in projects, and finding a job. At each stage, a community that supports you should be established. Career development in the field of artificial intelligence has its uniqueness.
Chapter 2: Learning basic technical skills is very important, including basic machine learning skills, deep learning, related mathematics, software development, etc. A good course is an efficient way to master the knowledge system, and continuous learning is required.
Chapter 3: Mathematics is one of the basic skills of artificial intelligence, but it is necessary to distinguish priorities. Understanding the mathematical principles behind algorithms is helpful for debugging algorithms. Curiosity-driven learning should also be encouraged.
Chapter 4: Defining a successful artificial intelligence project requires five steps: determining business problems, proposing AI solutions, evaluating feasibility and value, determining milestones, and resource budgets. Project development is an iterative process.
Chapter 5: Find projects that meet your career goals. Start with small projects. You can join existing projects, keep reading and communicating, focus on application fields, and develop side businesses. When choosing a project, consider factors such as technical growth, teammates, and whether it can be a stepping stone. Avoid analysis paralysis.
Chapter 6: Building a project portfolio shows skill progress. Each project is a step in growth. Communication and leadership are crucial.
Chapter 7: The job search framework. When switching positions or industries, consider switching one first. For example, a financial analyst can first find a data science job in his own field or go to a technology company to be an analyst.
Chapter 8: Informational interviews can help you understand your target job. Research the interviewee and the company in advance. Ask questions about job content, key skills, teamwork, etc. Informational interviews are especially important in the field of artificial intelligence.
Chapter 9: Job search details include researching positions and companies, informational interviews, applying or being recommended, interviewing, and choosing an offer. Pay attention to resumes, interview performance, etc. Act with respect and responsibility. Choose good teammates and seek help from the community.
Chapter 10: The keys to building an artificial intelligence career include teamwork, interpersonal and communication skills, developing good habits, helping others, and building a community rather than simply socializing.
Chapter 11: Overcoming impostor syndrome. Many people have experienced it, and newcomers in the field of artificial intelligence may also have it. Realize that everyone faces technical challenges. Welcome others to join the community and find people who support you.
Final Thoughts: The days of thinking about life are limited. Is what we do worth one thirty-thousandth of our lives?
