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A Complete Roadmap for Machine Learning- 4 Steps to Get a Job

Machine Learning

I think there are many people who want to learn machine learning and use it in their business, but don’t know where to start. Therefore, this time, I will explain in detail the roadmap from acquiring machine learning to connecting it to a job.

For those who say, “I can’t master it by self-study,” I also introduce a learning school, so I hope you can refer to it.

Table of Contents

  • 3 Reasons to Learn Machine Learning with Python
    • ① Relatively easy grammar
    • ② Abundant libraries and frameworks
    • ③ Abundant fields that can be utilized
  • Skill set required for AI engineers
    • (1) Knowledge and skills of versatile programming language
    • ② Mathematical knowledge
    • ③ Knowledge of machine learning and deep learning
  • From beginner to engineer in 4 steps
  • Step 0: Goal setting, career goal setting
    • Machine learning engineer
    • Data scientist
    • Product manager
    • Machine learning consultant
  • Step 1: Acquire basic knowledge
    • Learn from books
    • Learn at school
    • Learn on the website
  • Step 2: Learn Data Processing
    • Learn from books
    • Learn at school
    • Learn on website
  • Step 3: Learn About Implementation
    • Learn from books
    • Learn at school
    • Learn on website
  • Step 4: Prove yourself and get a job
    • Work as an intern
    • Qualify
    • Show on scout site
    • Build a portfolio
  • List of 6 books introduced in this article
  • List of schools introduced in this article
  • Future Prospects and Trends of Machine Learning Engineers
  • Summary

3 Reasons to Learn Machine Learning with Python

To learn machine learning, you must first learn programming. I recommend Python as a language that can be learned relatively quickly and without waste.

  1. Relatively easy grammar
  2. Many libraries and frameworks
  3. Abundant fields that can be utilized

There are three good reasons why you should learn Python.

① Relatively easy grammar

Compared to other languages ​​used for development, Python has simple grammatical rules and a small amount of code to write, so it can be said that it is a simple and easy language to start with.

② Abundant libraries and frameworks

Python is famous for its rich library and frameworks used when programming. Libraries according to the purpose are available, so you can easily and efficiently program.

③ Abundant fields that can be utilized

Many people think that learning a language that can be used in a variety of fields is good. Another attractive point of Python is that it can be used in a wide variety of fields, such as web services, game production, and application development.

Skill set required for AI engineers

By understanding what skills you lack, you can approach your goals more efficiently.

There are three main skills that AI engineers need.

  1. Knowledge and skills of versatile programming language
  2. Mathematical knowledge
  3. Knowledge of machine learning and deep learning

We will explain each below.

(1) Knowledge and skills of versatile programming language

Versatile programming language skills such as Python are required. Specifically, knowledge of useful libraries and frameworks for AI development, and skills such as database accumulation and analysis.

② Mathematical knowledge

Knowledge of mathematics is often useful in understanding AI. In particular, there will be a big difference in the progress of understanding with and without knowledge of mathematics, such as data analysis using AI.

③ Knowledge of machine learning and deep learning

You should understand the concepts and basic knowledge of machine learning and deep learning.

Whether or not it is possible to instantly judge “how to introduce AI” is considered to be the criterion for whether or not it can be used as an immediate force. It can be said that a wealth of knowledge is essential in order to become an AI engineer who can speedily put AI to practical use.

From beginner to engineer in 4 steps

I will introduce the path from studying machine learning from scratch to becoming an engineer in 4 steps.

  • Step 0: Goal setting, career goal setting
  • Step 1: Acquire basic knowledge
  • Step 2: Learn Data Processing
  • Step 3: Learn About Implementation
  • Step 4: Prove yourself and get a job

If you decide the order and study firmly, you can accumulate knowledge and experience of machine learning, and you should be able to do it.

Step 0: Goal setting, career goal setting

First of all, let’s set a goal point and a career goal. By deciding the goal point first, you will be able to decide the direction and make an accurate choice.

  1. Machine learning engineer
  2. Data scientist
  3. Product manager
  4. Machine learning consultant

Here are four jobs you can get into by mastering machine learning.

Machine learning engineer

A machine learning engineer is a job that specializes in machine learning, not involved in general AI development work.

The main work contents are service development, devising the system necessary for it, data analysis, model development, infrastructure construction and operation.

Machine learning engineers can be said to be a highly demanded and promising profession due to concerns about a shortage of human resources.

Data scientist

A data scientist is a job that supports decision makers so that they can make rational decisions based on data in various decision-making situations.

Statistical analysis, IT skills, and extensive knowledge of business and market trends are required. Their familiarity with both the business and IT industries makes this a highly sought-after profession.

Product manager

A product manager is a person with overall responsibility and final decision-making authority over a company’s product. This is a relatively new position and plays a key role in increasing customer satisfaction and maximizing the company’s profits.

In addition to product management, my main job is to supervise various departments such as sales, research and development, finance, and legal affairs that are involved in selling products, and to make policy decisions. It is a skill that requires comprehension, thinking, thinking, and communication skills.

Another appealing point about being a product manager is that you can change careers from any position.

Machine learning consultant

A machine learning consultant is a job that solves problems faced by companies based on AI technology centered on deep learning.

In addition to machine learning knowledge and data analysis skills, a wide range of knowledge such as specialized knowledge in the business industry is required. Communication skills are also important, as consultants are the main job content.

This job is recommended for those who are good at capturing the trends of the times.

Step 1: Acquire basic knowledge

Once you set your goals, it’s time to actually learn. First of all, it is important to acquire the basic knowledge and grasp the whole picture.

  1. Learn from books
  2. Learn at school
  3. Learn on the website

Introducing recommended books, schools, and websites for acquiring basic knowledge.

learn from books

The advantage of learning from books is that the information is comprehensive and the content is highly reliable.

Here are three books we recommend.

This book is for beginners who can understand without basic knowledge of AI. A wide range of explanations are provided, from basic knowledge of AI and machine learning to strategy planning and execution that is incorporated into business. It is recommended because you can acquire all the knowledge you need to know as a project leader in one book.

This book carefully explains the history of trial and error that artificial intelligence research has gone through. It details the future of artificial intelligence research and possible problems.

Information engineering, electronic engineering, brain science, the web, and philosophy are also included, and it is recommended because you can understand “what AI can do now, what it cannot do, and what it will be able to do in the future” in this one book.

Learn at school

If you find it difficult to study on your own, we recommend studying at a school.

Win school is recommended for beginners because more than 90% of learners start from inexperienced or beginners. It is also attractive that you can choose a course that matches the skill you want to learn.

Learn on the website

It is also recommended to study on the website because it is easy to work on.

Progete is a programming learning site for super-beginners where you can enjoy learning like a game. It’s a wonderful service that makes it easy to maintain motivation because you can learn like a game, and it’s hard to get frustrated.

Step 2: Learn Data Processing

Now that you have mastered the basics, let’s move on to learning about data processing.

Data processing is the process of examining and shaping the data before analyzing it using AI.

Here are some recommended books, schools, and websites for learning data processing.

Learn from books

Here are some recommended books for middle school students. This book covers the basics of doing data analysis. It is easy to learn because you can learn while actually moving your hands.

Learn at school

We will introduce recommended schools for intermediate level students.

Tech Academy is one of the largest programming schools in Japan, and you can receive one-on-one guidance from carefully selected instructors who are active engineers.

Because it is one-on-one instruction, it is difficult to get frustrated when the level goes up. Highly recommended for those who are serious about learning.

Learn on website

Here are some recommended websites for intermediate level students.

PyQ is a website where you can learn up to the practical level (data analysis, machine learning, etc.).

Python’s execution environment is ready, so you can write code immediately and it’s easy to learn.

Step 3: Learn About Implementation

After learning about data processing, let’s finally learn about analysis using AI.

Here are some recommended books, schools, and websites for learning about implementation.

Learn from books

Here are some books for advanced learners.

In this book, we will focus on natural language processing and time series data processing, and use deep learning to solve various problems. You can master cutting-edge technology at the implementation level.

Many illustrations are used, so it is easy to understand and is recommended.

This book comprehensively explains the theory, mathematical background, and Python coding practice for each concept of machine learning.

Some people said that it was good that it covered not only the programming part but also the theoretical part.

Learn at school

We will introduce recommended schools for advanced students.

Tech boost is characterized by the fact that you can choose the course period according to the learning time of the day, and the fee is cheaper than other schools. I think that I can learn practical things because I can receive one-on-one guidance from active engineers eight times a month.

Learn on website

Udemy has courses in various categories such as development, IT/software, data science, Excel, management, and marketing, as well as programming languages ​​(such as Python).

You can repeatedly learn from practical level web development, data analysis, and machine learning with videos.

Step 4: Prove yourself and get a job

If you complete up to step 3, you have enough ability to use it in practice, so the next point is how to connect it to a job.

Here are 4 ways to connect to the job I’m going to introduce this time.

  1. Work as an intern
  2. Qualify
  3. Show on scout site
  4. Build a portfolio

Work as an intern

Internships are recommended for students who want to learn machine learning and connect it to their future jobs. There are not only internships for experienced people, but also internships where you can gain experience while studying from inexperienced.

If you find it difficult to find an intern on your own, here are some recommended internships.

The following two interns can work while learning through training even if they have no experience.

Dip Co., Ltd.

Required skill nothing special
Main development environment Python , Machine Learning /Natural Language Processing
Job Description ・ Machine learning application development and natural sentence generation AI used by 1500 sales people・Make what you want freely in an atmosphere like a development circle or laboratory
Work location Sumitomo Realty & Development Roppongi Grand Tower 31F, 3-2-1 Roppongi, Minato-ku, Tokyo
Salary 1100 yen ~
working conditions Basic remote work

Avinton Japan Co., Ltd.

Required skill ・People with high aspirations and ambition・ People who catch up quickly

・People who want to work using English (some want to learn in the future)

Main development environment ・Foundation of infrastructure (server, network), construction of development environment・Docker/VMWare/AWS/SQL

・Python /Numpy/OpenCV

・HTML/ CSS /JavaScript

・React/Express/Node.js/Websockets

・AI/machine learning lectures (image classification, transfer learning, etc.) Pandas/Jupyter

・ AI / machine learning lectures (image classification, transfer learning, etc.) Pandas / Jupyter etc. We decide the contents according to the trends at that time.

Job Description ・Web, system development and infrastructure construction centered on open systems.・In-house application development and operation.
Work location Waynes & Issey Yokohama Building 4F, 7-150 Hanasaki-cho, Nishi-ku, Yokohama-shi, Kanagawa
Salary No data
working conditions No data

The following two interns are for experienced candidates. If you have taken the above steps and have some knowledge of machine learning, you can try it.

EXIDEA Co., Ltd.

Required skill ・No development experience, but enrolled in an information engineering department・Experience in laboratories, etc. in the case of data science
Main development environment ・Development language: Python , Shell Script, SQL, PHP, Node.js, TypeScript, Java・Infrastructure: AWS (Lambda, Glue, Athena, QuickSight, etc.), Zabbix

・Source code management: GitHub

・Tools: Redmine, Lucidchart, Chatwork, Slack

Job Description ・Data science・Analysis platform development

・DevOps

Work location 1-4-18 Honjo, Sumida-ku, Tokyo
Salary No data
working conditions Able to work from 6 hours a day, 3 days a week on weekdays

Studio Ousia Co., Ltd.

Required skill ・Those who are familiar with natural language processing and machine learning
Main development environment ・Development language: Python・Infrastructure: AWS

・Library

Machine learning /NLP system

・PyTorch

・LightGBM

・Scikit-learn

・hyperopt

・NumPy

・SciPy

・Gensim

・marisa-trie

・mw parser from hell

・Joblib

System/Web system

・Flask

・Ansible

・Cython

・Fabric

・Gevent

・MessagePack

・SQL Alchemy

・Alembic

Job Description ・Development of question answering engine “QA Engine”・Development of Entity Linking engine “Semantic Kernel”
Work location 4th floor, Otemachi Building, 1-6-1 Otemachi, Chiyoda-ku, Tokyo
Salary No data
working conditions Basic remote work

In the internship, you can learn practical skills to work in a team that you cannot learn in self-study or programming school.

In addition, you can grow significantly by working hard with excellent engineers and students.

All students are encouraged to actively participate.

Qualify

Qualifications are effective as proof of your abilities when looking for a job or changing jobs. Credentials can also support careers within the workplace, making it easier to transfer your machine learning skills to a job.

Now let’s talk about what kind of qualifications you should have.

test name G test
organizer Japan Deep Learning Association
Held frequency 3 times a year
site https://www.jdla.org/certificate/general/
Exam fee ・ General: 12,000 yen (excluding tax)・Student: 5,000 yen (excluding tax)
Overview There are about 220 questions, and the pass rate is about 60% to 70%.

The G test is a test designed to develop human resources who can use deep learning, and is recommended for engineers and those who use AI in business.

test name E-qualification
organizer Japan Deep Learning Association
Held frequency twice a year
site https://www.jdla.org/certificate/engineer/#certificate_No04
Exam fee ・ General: 33,000 yen (excluding tax)・Student: 22,000 yen (excluding tax)

・Member: 27,500 yen (excluding tax)

Exam outline There are about 100 questions, and the pass rate is about 60% to 70%.

The E qualification is a qualification for engineers. It is given to people who understand the theory of deep learning and have the ability to implement it, so it is a proof of advanced skills related to machine learning.

test name AI implementation test
organizer Study-AI Co., Ltd.
Held frequency as needed
site https://kentei.ai/
Exam fee ・Class A: 3,500 yen (tax included)・B grade: 2,000 yen (excluding tax)

・S class: 5,000 yen (tax included)

Exam outline The passing rate of A grade is about 75%, and it is recommended for JDLA exam preparation because it covers the exam range.

The AI ​​implementation test is a test that certifies mathematics that can express AI and programming skills that can be implemented.

There are 3 types: Class B for those who plan using AI, Class A for implementation that is slightly more difficult than Class B, and Class S for practical applications such as natural language processing and model implementation. You can take what you have.

In the process of studying for qualifications, it is recommended because you can study more comprehensively, organize your knowledge, and learn things you did not know.

Show on scout site

Another way is to show your abilities on scout sites.

In order to prove knowledge, skills, and experience related to machine learning on job change sites for engineers, the qualifications mentioned above will be effective.

However, if you show your ability on the scout site, it is often the case that experienced people are the target.

Build a portfolio

When you hear the word “portfolio,” you probably think of a designer creating a portfolio, but recently, an increasing number of companies are asking engineers to submit their portfolios as well.

An engineer’s portfolio, like a designer’s, is a testament to your abilities and achievements.

You will also need a portfolio if you want to show your ability on the scout site mentioned above.

List of 6 books introduced in this article

book title Reference price step
The easiest machine learning project textbook 1980 yen Step 1: Acquire basic knowledge
Will artificial intelligence surpass humans? 1540 yen Step 1: Acquire basic knowledge
Introduction to data analysis with Python 2nd edition – Data processing using NumPy and pandas 4180 yen Step 2: Learn Data Processing
Deep-Learning from Scratch-Natural Language Processing- 3960 yen Step 3: Learn About Implementation
[3rd Edition] Python Machine Learning Programming Theory and Practice by a Master Data Scientist 4400 yen Step 3: Learn About Implementation

 

List of schools introduced in this article

school name step
Win School Step 1: Acquire basic knowledge
tech academy Step 2: Learn Data Processing
tech boost Step 3: Learn About Implementation

Future Prospects and Trends of Machine Learning Engineers

I’m sure many of you reading this are wondering about the future of machine learning engineers.

AI-related industries are currently in high demand, as are machine learning engineers. Since the penetration rate of machine learning algorithms is still low, the demand for machine learning engineers is expected to increase in the future.

In the future, the shortage of advanced IT human resources in companies will tend to accelerate further, so the future of machine learning engineers can be said to be bright.

Summary

This time, I explained in detail the roadmap for learning machine learning. If you are even slightly interested in machine learning, please refer to the 4 steps.

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