Learn how to create your Algorithms based on data science. Machine learning, deep learning and artificial intelligence
I make you a promise:
What you’ll learn
- Master Machine Learning on Python.
- Learn the basics of Python.
- Create robust Machine Learning models.
- Learn supervised and unsupervised machine learning.
- Make strong analysis.
- Handle powerful topics like Reinforcement Learning, NLP and Deep Learning.
- Learn to use different libraries for Data Analysis.
- Get in touch with Decision Trees.
- Linear Regression and Logistic Regression.
- Statistics and probability.
- Neural Networks.
Course Content
- Introduction to machine learning (Theory) –> 3 lectures • 15min.
- Mathematics in machine learning (Theory) –> 5 lectures • 25min.
- Python (Practice) –> 2 lectures • 12min.
- Machine learning libraries (Practice) –> 6 lectures • 54min.
- Supervised learning (Practice) –> 16 lectures • 2hr 30min.
- Unsupervised learning (Practice) –> 6 lectures • 45min.
- Project: Fake news detection –> 4 lectures • 54min.
Requirements
I make you a promise:
“If you can’t understand and program your own machine learning algorithm in 30 days after the React course, you get all your money back and get to keep all the course materials as my gift”.
In this course, you as a beginner will be guided through all relevant fields of Alorgythms and Artificial Intelligence in Python in a practice-oriented way, so that you can finally program your AI with Python 3.9 (the latest version) without errors. We’ll focus here on machine learning and deep Learning
Your lecturer in this course is Vivien.
She has been a Python and Java programmer for 7 years and works at a software company for cyber security. In this course, Vivien shares all she knows about what beginners need to know to get started successfully in machine learning and make as few mistakes as possible. Vivien’s goal in this course is to provide you with strong tools and a solid foundation in Python/Machine learning for your future programming experience. This is why the course also relies on theory to make the basic concepts of machine learning understandable in order to apply them later in this course.
The machine learning course covers everything you need to know as a beginner in Python and machine learning itself to successfully write your first programs and codes. The focus is on practice so that you can primarily use the functions of Python in real life to solve your problems and write your own machine-learning code, in addition to theoretical lections.
Vivien tries to design the course in such a way that even total beginners to Python can program their own algorithms. If you want to be on the safe side, check out the Python course first, as this is the basic programming language.
We’ll focus on all the important libraries, functions, and learning methods like supervised and unsupervised machine learning.
We also look at the foundations for further programming in areas such as deep learning, data science, and Python itself.
You want to learn machine learning from scratch and code your own individual program?
See you in the course then!