AIキャリア構築！— Andrew Ngの『How to Build a Career in AI』を読み解く

🚀 『How to Build a Career in AI』を読み終えましたが、その内容は非常に画期的です！ 🤖✨ 
[How to Build a Career in Al](https://info.deeplearning.ai/how-to-build-a-career-in-ai-book)

本書は、AIの基本的なスキルを学ぶ段階から、それを実践的なプロジェクトに応用し、最終的に理想の仕事を手に入れるまでの過程を巧みにガイドしています。継続的な学習の重要性、戦略的なプロジェクト選択、そして魅力的なポートフォリオの構築を強調しています。業界の役割や要件を理解するために、情報収集インタビューの重要性が強調されています。

こちらが私の読書メモです。
# How to build a career in AI

### Chapter 1: Three Steps to Career Growth.

![Screenshot 2024-07-18 at 12.14.18.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3495932/9e02cf24-2563-03b0-1ec0-0be84e82bb70.png)

### Chapter 2: Learning Technical Skills for a Promising AI Career.

- Foundational machine learning skills
models such as linear regression, logistic regression, neural networks, decision trees, clustering, and anomaly detection
core concepts behind how and why machine learning works, such as bias/variance, cost functions, regularization, optimization algorithms, and error analysis
- Deep learning
basics of neural networks, practical skills for making them work, such as hyperparameter tuning, convolutional networks, sequence models, and transformers.
- Math relevant to machine learning 
linear algebra (vectors, matrices, and various manipulations of them) 
probability
statistics (including discrete and continuous probability, standard probability distributions, basic rules such as independence and Bayes’ rule, and hypothesis testing)
exploratory data analysis (EDA) — using visualizations and other methods to systematically explore a dataset, particularly useful in data-centric AI development, where analyzing errors and gaining insights can really help drive progress!
calculus
- Software development
programming fundamentals
data structures (especially those that relate to machine learning, such as data frames)
algorithms (including those related to databases and data manipulation)
software design
Python
key libraries such as TensorFlow or PyTorch, and scikit-learn.

A good course — in which a body of material has been organized into a coherent and logical form — is often the most time-efficient way to master a meaningful body of knowledge.

The best way to build a new habit is to start small and succeed, rather than start too big and fail.

### Chapter 3: Should You Learn Math to Get a Job in AI?

It is useful to ask what you need to know to make the decisions required for the work you want to do. 

### Chapter 4: Scoping Successful AI Projects.

Five steps to help you scope projects:

Step 1

Identify a business problem (not an AI problem). I like to find a domain expert and ask, “What are the top three things that you wish worked better? Why aren’t they working yet?”

Step 2

Brainstorm AI solutions.

Step 3

Assess the feasibility and value of potential solutions.

Step 4

Determine milestones. This includes both machine learning metrics (such as accuracy) and business metrics (such as those related to user engagement, revenue, and so on.). Unfortunately, not every business problem can be reduced to optimizing test set accuracy!

Step 5

Budget for resources.

### Chapter 5: Finding Projects that Complement Your Career Goals.

A few ways to generate project ideas:

- Join existing projects.
- Keep reading and talking to people.
- Focus on an application area.
- Develop a side hustle.

Given a few project ideas, A quick checklist of factors to consider which one one should jump into:

- Will the project help you grow technically?
- Do you have good teammates to work with?
- Can it be a stepping stone?
- Avoid analysis paralysis.

Two distinct styles to build projects and how to go about it:

- Ready, Aim, Fire tends to be superior when the cost of execution is high and a study can shed light on how useful or valuable a project could be.
When committing to a direction means making a costly investment or entering a one- way door (meaning a decision that’s hard to reverse), it’s often worth spending more time in advance to make sure it really is a good idea.
- Ready, Fire, Aim tends to be better if you can execute at low cost and, in doing so, determine whether the direction is feasible and discover tweaks that will make it work. 
After agreeing upon a project direction, when it comes to building a machine learning model that’s part of the product, I have a bias toward Ready, Fire, Aim. Building models is an iterative process.

### Chapter 6: Building a Portfolio of Projects that Shows Skill Progression.

Don’t worry about starting too small. 

Communication is key.

Leadership isn’t just for managers.

### Chapter 7: A Simple Framework for Starting Your AI Job Search.

If you’re considering a role switch, a startup can be an easier place to do it than a big company.

If you’re switching either roles or industries, one of the most underused tools for becoming more familiar with a new role and/or industry is the informational interview.

Overcoming uncertainty:

![Screenshot 2024-07-18 at 12.15.51.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3495932/193347fc-063b-6347-29f5-15ef6a2cd4b4.png)

### Chapter 8: Using Informational Interviews to Find the Right Job.

An informational interview involves finding someone in a company or role you’d like to know more about and informally interviewing them about their work.
An informational interview can help you sort out what the AI people in a particular company actually do.
An informational interview can be invaluable for learning what happens and what skills are needed to do the job well in the case that gradually many people will be taking on an AI job for the first time.

Prepare for informational interviews by researching the interviewee and company in advance.
You might ask thoughtful questions like:
✓ What do you do in a typical week or day?

✓ What are the most important tasks in this role?

✓ What skills are most important for success?

✓ How does your team work together to accomplish its goals?

✓ What is the hiring process?

✓ Considering candidates who stood out in the past, what enabled them to shine?

If you can reach out to someone who’s already in your network — perhaps a friend who made the transition ahead of you or someone who attended the same school as you. Meetups such as [Pie & AI](https://www.deeplearning.ai/events/) can also help you build your network.

Finally, be polite and professional, and thank the people you’ve interviewed. And when you get a chance, please pay it forward as well and help someone coming up after you.

### Chapter 9: Finding the Right AI Job for You.

- Pay attention to the fundamentals. 
A compelling resume, portfolio of technical projects, and a strong interview performance will unlock doors. 
Update your resume and make sure it clearly presents your education and experience relevant to the role you want. 
Customize your communications with each company to explain why you’re a good fit. 
Before an interview, ask the recruiter what to expect. Take time to review and practice answers to common interview questions, brush up key skills, and study technical materials to make sure they are fresh in your mind. 
Take notes to help you remember what was said.
- Proceed respectfully and responsibly. 
Approach interviews and offer negotiations with a win- win mindset.
- Choose who to work with.
- Get help from your community.

### Chapter 10: Keys to Building a Career in AI.

- Teamwork
- Networking
- Job search
- Personal discipline
- Altruism

### Chapter 11: Overcoming Imposter Syndrome.

Don’t discourage you or anyone else from growing in AI.