Machine Learning Expert Certificate

Learning Path: Machine Learning Expert Certificate

  • Introduction to AI, Robotics and Data 
  • Global Impact of AI 
  • Introduction to Python
  • ML Developer Toolkit 
    • Version Control Systems & Portfolio
    • Database for Data Scientists
  • Data Analysis
  • Introduction to Machine Learning 
  • Machine Learning Deployment 
  • Workshop ML Projects
  • Data Visualization

Introduction to AI, Robotics and Data 

Digital and AI technologies are conquering and fundamentally changing our world with an amazing speed. And they will increasingly have a very significant impact on all aspects of our life, our economy, our entire society.

From voice assistants and chatbots to self-driving cars and humanoid robots, these technologies improve efficiency and quality of life and open up totally new opportunities to create positive value. Therefore, we should all learn how to make good use of these new opportunities but also how to avoid the pitfalls, risks and threats which are also inevitably related to AI.

With this unique introductory course you will get: 

  • a comprehensive 360º overview of all relevant topics regarding AI
  • a basic understanding of AI  
  • an overview of the most relevant AI technologies


Global Impact of AI 

The "Global Impact of AI" course is specifically designed to help you explore the impact of Artificial Intelligence, AI, and digitalization on our society and all of us. It offers you a global perspective on the fascinating opportunities but also ethical aspects, risks and threats which are inevitably associated with AI and shares insights on the very different roles humans and machines will have in the long term.

With this introductory course you will discover:

  • AI-related risks and opportunities for our society and all of us 
  • Ethical challenges and AI regulation
  • The long-term roles of humans vs. machines 
  • The current state of global AI competition and collaboration


Introduction to Python

This course is for everyone who wants to take the first step to the Artificial Intelligence and Data Science world by learning one of the most popular programming languages from scratch.

By participating in this education series that we have created for you using real-world experiences, you will have taken the first step into the world of programming and artificial intelligence.

With this course, you will gain the following competencies:

  • Primitive Data Types and Data Collections
  • Loops and Conditional Statements,
  • Functional Programming, 
  • Exception Handling, 
  • Data Analysis & Manipulation


ML Developer Toolkit

Version Control Systems & Portfolio

In the age of Data Science, there are two other issues that developers must know besides machine learning and software language. Version control and personal portfolios. While version control systems ensure the follow-up of your software and the coordination with your teammates, which is inevitable in working life, your portfolio will be your most effective resume in the world of data science. In this course, you will gain these competencies:

  • How to follow your projects with Git Version Control System

  • GitHub website where you can share your work with the world, spend time with all the other developers, review the open-source projects of the most advanced technology companies worldwide, and showcase your work.

  • All the information you need to build your portfolio

As a data scientist there will be times when you will work alone or as a team. As the project you are developing grows, it will be very difficult to work on versions and manage your data. While you need to use version control systems such as Git to manage the version, keep your project up-to-date for everyone on the team, and increase the collaboration within the team, you also need to use databases to store and manage your data. To overcome this problem, you will learn the tools such as Git, Github, Sql, which are most commonly used in the field of Machine Learning which will increase your competencies in this field. You will store and manage your data using SQL and databases, and you will learn how to manage and control the version of your project on the Github platform using Git. Thanks to Github platform, you will be able to browse the most up-to-date open source projects and contribute them.

Database For Data Scientists

Much of the world's data is stored in databases, a structured system created to hold data in a digital environment controlled by various Database Management Systems. Relational Databases have become one of the most valuable assets of a data scientist, obliging individuals of the field to learn RDBMS usage of SQLite, MS SQL Server, and MySQL.

For beginners who wish to lay the foundations of their knowledge on relational databases, the self-paced course "Databases for Data Scientists" is tailor-made. In this course, you will gain these competencies:

  • Terminology of Relational Database

  • Essential Principles of Creation and Managing a Database

  •  Structured Query Language (SQL)

  • ACID & Normalization

  •  Combining SQL with Python via using SQLite

Data Analysis

Data is the most important element of machine learning projects and they are found in raw format in real life. With this course, you will learn how to analyze and process raw data. Data needs to be prepared for machine learning models. What we mean by the preparation of data covers topics such as cleaning, determining statistics, eliminating deficiencies and visualizing. You will have all of these competencies in the well-prepared data analysis course. By using the Python programming language, you will be able to provide very fast and high level analysis even on large data.

With this course, you will gain the following competencies:

  • Exploratory Data Analysis
  • Elements of Structured Data
  • Data Distribution, Correlations, Plotting and Visualization Techniques
  • Data Bias, Statisticals Tests, Data Manipulation with Pandas, 
  • Data Visualization with Seaborn


Introduction to Machine Learning

This course, which contains all the basic content you need to learn in the Introduction to Data Science, aims to evolve you into a good machine learning practitioner. Machine learning forms the core of data science, one of the most popular professions of our age. With this course, you will develop a high level of knowledge and deep understanding of data. It is anticipated that a good grasp of data, which is beginning to surround us, and ability to analyze will be the most important competence of the future. This education has been prepared carefully to evolve you for the upcoming data age.

With this course, you will gain the following competencies:

  • Linear Algebra and Probability
  • Data Preparation, Regularization
  • Supervised and Unsupervised Learning, 
  • Linear & Logistic Regression and Decision Algorithms
  • Data Modeling and Creating Machine Learning Models.


Machine Learning Deployment (coming soon)

One of the most important tasks that a machine learning engineer or data scientist must complete is to have a portfolio. By running your projects on simple web pages, you can easily show how wonderful projects you are doing, even to people who do not have a deep knowledge of the field of artificial intelligence. This course includes hands-on projects that explain how to implement a machine learning project you have created to a website.

Streamlit and Heroku are the most used free services today where you can implement projects on the web. You can be sure that turning your machine learning projects into exciting web projects will make you feel like an incredible data scientist.

With this course, you will gain the following competencies:

  • Deployment Techniques, 
  • Environmental Variables, 
  • Streamlit & Heroku Platform and Web Services.
  • SaaS and Paas Services, 
  • Object Oriented Programming,

Workshop ML Projects (coming soon)

When building your data scientist career, what works best in interviews is a good portfolio. You will have an interesting picture by making projects that will enrich your portfolio in machine learning workshops with projects.

Project 1: Data Scraping 

  • Techniques that allow you to collect data over the Internet and create customized data sets for yourself

Project 2: Data Visualization

  • A great project where you will learn 3 different datasets and 10 different data visualization techniques.

Project 3: Tabular Data 

  • A workshop on topics that will enable you to process large data and excel style data at incredibly high speeds.

Project 4: Time Series Analysis

  • A workshop that explains how analysis is done in time series and how you can use your time-dependent data in machine learning models, which will be most useful for you in real life.

Data Visualization

In the age of Artificial Intelligence, understanding, interpreting, and analyzing data has become one of the most critical competencies. Since you have learned the most required and sought-after programming language in the business world, Python, now you should take the next step to the Data Science world and learn how to interpret data for even non-technical people via visualizing the data. Since data is the prerequisite of all AI projects, you will learn how to create reports that explain themselves and interpret the big data via visualization and the most fundamental and popular visualization techniques in the real world with Python. In this course, you will gain these competencies:

  • Gather and preprocess data with Python

  • Choosing right visualization technique for different data and needs

  • Visualizing data with Matplotlib and Seaborn libraries

  • Interpret the visualized data

  • Create a PDF report by visualizing data

Once this course ends, you will have an end-to-end visualization project with real-world data which allows you to create a report to show your work to other people.