Machine Learning Engineer Course

A full-time course to become a Machine Learning Engineer

A program designed to help you get a job as a machine learning engineer

DIVE INTO CODE's Machine Learning Engineering Course aims to help you become an independent and self-driven machine learning engineer in 4 months.

In our program, you will not just learn knowledge, but you will learn the skills that you really need, working backwards from the practical world. Let's aim to become a machine learning engineer who can work in the real world with our educational program.

How to Become a Machine Learning Engineer

  • Introduction
  • Curriculum
  • 1. Admission to the school

    Only students who pass the pre-test and interview can enroll in the course.

  • 2. Curriculum

    • Preliminary study

      This is the study before starting the main course.

    • Main course

      Students will study for more than 600 hours in 3 terms.

    • Employment Term

      We will prepare your portfolio, introduce partners, and hold job fairs.

What will you learn?

  • Preliminary study

    A solid foundation before starting the program

    • Mathematics
    • -Linear Algebra
    • -Calculus

    • Data Science Tools
    • -Jupyter Notebook
    • -Pandas
    • -Numpy
    • -Matplotlib(Seaborn)
    • -Kaggle EDA
    • -Machine Learning Overview
    • -Preprocessing
    • -Open dataset exercises
    • -Sklearn
    • I now have a better idea of what programming is all about.

      In the pre-learning period, students learn a wide range of topics from Python and mathematics to machine learning. Unlike the main program, the curriculum is input-oriented.

  • Term1


    In Term1, you will mainly learn about machine learning algorithms and their applications. By implementing algorithms from scratch, you will learn how to understand the algorithms properly. You will also learn practical skills through Kaggle exercises.

    • Machine Learning
    • -Supervised Learning
    • -Unsupervised Learning
    • Knowledge around machine learning
    • -Kaggle
    • I was able to master the algorithms in a challenging environment.

      The only people who can enter the Machine Learning Engineering course are those who have a strong will to get a job, can invest all their time for four months, and have a good background in programming and mathematics. You will learn in a classroom with such a high level of enthusiasm.

    • Now you can think like an engineer.

      In the Machine Learning Engineering course, the curriculum focuses on thinking like an engineer, not just getting knowledge. This is because the work you will do tomorrow is not like a textbook with written procedures!

  • Term2


    In Term2, you will learn basic algorithms such as CNN, DNN, RNN, etc., and areas where deep learning is often used such as basic image recognition / basic natural language processing. At the end of Term3, students will be able to read and implement Kaggle exercises and papers as in Term2.

    • DL
    • -DNN
    • -CNN
    • -RNN

    • Applying DL
    • -Introduction to Image Recognition (DL)
    • -Introduction to Natural Language Processing (DL)
    • DL Peripheral Knowledge
    • -Cloud (AWS)
    • -Learning on GPU
    • -Framework
    • -Thesis reimplementation
    • I can now get a bronze medal on Kaggle.

      You will be able to solve practical tasks and use cloud computing to perform calculations. You will be able to learn how to handle large amounts of data.

    • Understand about the operation and maintenance of machine learning projects.

      Learn the best practices in machine learning projects so that you can enter the machine learning project scene without any problems.

  • Term3


    In Term3, you will conduct an engineering project based on what you have learned so far. In this project, you will learn about new technologies and what you need to think about when incorporating machine learning into your system, and experience what it is like to be a real engineer.

    • -SQL
    • -Docker
    • -Understanding of machine learning applications (API/DB, system design)
    • Got the peripheral knowledge needed to be a machine learning engineer.

      Learn the peripheral knowledge of machine learning such as Docker/API building/DB/SQL/BigQuery/Web.

    • Learn to read and understand papers without problems

      While working on a project, you may have to read and implement papers to incorporate new technologies. When you reach this level, you will be able to catch up with new technologies and concepts by yourself.

  • A more detailed curriculum can be downloaded here.

Daily Schedule

  • The program is a two-day cycle called "Sprint" that is repeated over and over.

    • Opening Session

      Explanation of the theory behind the assignment and response to questions from the students.

    • Kaggle Presentation of the Day

      Students will be assigned to present the results of their research in the data analysis competition, with the aim of catching up on Kaggle trends in a collaborative manner.

    • Working on assignments in groups

      Students solve problems by consulting with each other. The aim is to develop communication and problem-solving skills that are required after employment.

    • End of Session

      Students will be assigned to share the progress and results of their assignments. This will be followed by a general discussion and Q&A session.

    • After the Sprint, we will have a self-study session to prepare for the Sprint that starts the next day. We plan to have about 6 hours of self-study time in 2 days.

Day 1 of Sprint
10:00-11:00 Start of Session
Explanation of Sprint tasks and theory
11:00-12:00 Group work on assignments
12:00-13:00 Lunch and free time
13:00-14:00 Group work on assignments
14:00-14:30 Kaggle presentation of the day
14:30-17:30 Group work on assignments
17:30-18:30 End of Session
Presentation, Q & A, Discussion
18:30-22:00 Self-study
Textbook study for this Sprint
Day 2 of Sprint
10:00-10:30 Start of Session
Hints and questions
10:30-12:00 Group work on assignments
12:00-13:00 Lunch and free time
13:00-14:00 Work on assignments for each group
14:00-14:30 Kaggle presentation of the day
14:30-17:30 Group work on assignments
17:30-18:30 End of Session
Presentation, Q & A, Discussion
18:30-19:00 Reflection by KPT
19:00-22:00 Self-study
Textbook study for the next Sprint

Mon〜Fri 10:00~19:00


  • Representative mentor

    Hiroyoshi Noro

    MBA Engineer Lecturer. I have experienced four career changes in different industries and occupations. With the spirit "I can do everything if I commit myself.” At the age of 29 years old, I succeeded the employment examination of a famous development company in Japan “Works Applications company” While experiencing entrepreneurship, I realized a lack of engineers in the industry. I founded "DIVE INTO CODE" to give the opportunity to those who challenge to become professional engineers so that they can seize the opportunities in the field. "We will do our best to start your engineer's career."

  • Mentor

    Mouhamed DIOP

    Telecoms & Networks | Web and ML Engineer Lecturer. I have experienced in Telecommunications and Networking as well as teaching in Machine Learning and Web Engineering. Since very young I have always believed in the following statement: “Each One Teach One, and at the end of the day, we will all learn something”. At the age of 15 years old, I traveled to the United States of America as an exchange student in order to open the eyes of Americans about the real Africa that they don’t know. I literally followed the same path (Teaching & Raising Awareness) in IT as I am now trying to make sure other people have the chance to become engineers like me in the best way possible through DIVE INTO CODE. “Let’s Walk Hand in Hand on this amazing journey of becoming an Engineer.”


Here are some examples of what students have developed in their own projects.

  • Development of a super-resolution smartphone application

    When you upload a photo on this app, it will make the image of the photo higher quality on your mobile app. This app runs on the smartphone, not in the cloud. We also pay attention to the speed of operation, and aim for a speed at which users can enhance the image quality without stress.

    Paper implementation, CNN, super-resolution, Android, Tensorflow, TensorflofLite, edge computing, Azure

  • Development of an interactive chatbot

    We have developed an interactive chatbot that works in Japanese. Data collection, pre-processing, paper implementation, RNN, LineAPI, Azure

  • Development of an alert system to detect fraudulent items

    Based on the image of a service that receives 30,000 items a day, we developed a fraudulent item detection algorithm and its system implementation. Data collection, pre-processing, paper implementation, CNN, multimodal learning, Azure, AzureBatchAI, Flask

  • Development of an alert system to detect fraudulent items

    Development of a model using a neural network that takes static image data as input and outputs a Japanese description (caption) of the image.

    Features of the Machine Learning Engineering Course

    • Learn machine learning and deep learning

      You will not only be able to implement cutting-edge machine learning and deep learning algorithms in libraries.
      We will implement those algorithms from scratch and learn the proper fundamentals.

    • Project-Based Curriculum

      Simply reading a book leaves you wondering if you can actually use it. In DIVE INTO CODE, the curriculum is designed to deal with unknown problems such as ToyProblem, mock projects, Kaggle, and application development including paper implementation.

    • Soft skills

      Students acquire not only the knowledge of machine learning, but also the thinking and skills of an engineer. We focus on developing communication skills and problem-solving skills to solve unknown problems.

How to enter the school

  • Trial
  • Exam
  • Interview
  • Curriculum
  • Briefing Session

    We will talk about careers, work as an engineer, and DIVE INTO CODE.

  • Pre-test

    We will select applicants based on their attitude and motivation to tackle unknown problems.

  • Interview

    If you are interested in joining our school, we will set up an interview for you to start learning again. After the interview, we use the results of the pre-test and interview to determine the degree of matching and commitment to the course.

  • Enrollment

    After confirming payment, we will invite you to the curriculum and tools necessary for studying.

You can also take the pre-test without attending the briefing session.

"Take the pre-test"

In order to pass the pre-test, you need to be able to complete the following material

※The above materials do not guarantee that you will pass the test.

Pre-test exam application