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

Data Analysis

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

Intermediate Python (coming soon)

Python is one of the cores for Machine learning and Data Science. The reason for this is that no matter how much the theory is known, without Python, these theories cannot be put into practice. With this course, you will have a deeper introduction to Python with some of the intermediate concepts of Python such as decorators, generators, comprehensions, better grasp the strengths of the language, and be better prepared for real-life scenarios from standard examples. At the same time, with the power of Object Oriented Programming you will learn with this course, you will be able to improve your Machine Learning & Data Science projects more effectively in terms of both readability and performance.

With this Course, you will gain the following competencies:

• Object Oriented Programming
• Decorators, Generators, Comprehensions

Introduction to Deep Learning

Deep Learning which is the most complex sub-topic of Machine Learning and the most popular field of AI offers a new approach to many problems that cannot be solved by standard algorithms. Deep Learning helps us to solve problems consisting of unstructured data such as image and language processing. Deep Learning algorithms solve more complex problems and perform better in Big Data compared to Conventional Machine Learning algorithms. In addition, it plays a key role in solving problems that Data Scientists cannot solve with basic Machine Learning methods. Autonomous vehicles, voice assistants, drones and computer vision are the most common Deep Learning topics used in the industry. Deep Learning, which has a great depth in the academic field, includes the subjects that have received the largest R&D investment by many global big-tech companies. In this Deep Learning course, we will show you how the most important AI projects are carried out and what the underlying mathematics are.

With this Course, you will gain the following competencies:

• Brief introduction to Deep Learning and Neural Networks, 
• Machine Learning Short Review, Fundamentals of Regression & Classification, 
• Optimization Techniques
• Digital Signal Processing and Computer Vision, 
• Natural Language Processing and Sentiment Analysis.

Advanced Deep Learning (coming soon)

Since Artificial Intelligence is a world in itself, it would not be wrong to say that Deep Learning, one of its most important sub-branches, is also a world in itself. With this course, you will learn how Deep Learning theories are applied in our real life and you also will prepare for real life projects. With each topic you will learn in this course you will achieve amazing things; With Transfer Learning you will get rid of model training time by using an accurate model that has been previously trained for hours or even days, with Tensorflow Keras  you will develop deep learning models easily, with Generative Adversarial Networks you will create an image of things that never existed with, with Convolutional Neural Network into practice you will develop a face recognition system, and with Natural Language Processing into practice  you will create a full performance chatbot with the help of Rasa. And while doing all this, you will be working with real data, not with the clean and organized data given in tutorials. After this course, you will be a Deep Learning guru!

With this Course, you will gain the following competencies:

• Neural Network Implementations with Tensorflow Keras, 
• Transfer Learning
• Generative Adverserial Networks

Workshop DL 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.