Learn from the most popular areas of Machine Learning to the most useful algorithms in 5 days
Get one step ahead by understanding the most useful methodologies in business
A unique introductory course to better understand “Machine Learning”, one of the biggest fields of the AI era
Makes it easy to understand both theoretical and practical approaches of Machine Learning and Artificial Intelligence technologies that affect our lives the most
Made from resources from the best schools in the world and the best books in the field
Why Take This Course?
This course is for anyone who wants to take the first step into the world of Artificial Intelligence and Data Science by learning the fundamentals of Machine Learning.
In this course which Python programming language will be used, after the basic theoretical overview of Machine Learning, approaches to Regression and Classification problems which are the problem types of Supervised Learning, increasing the performance of these approach techniques will be covered and real-life projects will be developed. Afterwards, with Decision Trees, the deeper concepts will be covered. In addition to Decision Trees, the Ensemble Learning method, where we can combine different models that are frequently used in real life and create new models, will be discussed. On the last day of our course, the clustering method in Unsupervised Learning will be discussed and many concepts of Machine Learning will be learned to the full within 5 days.
Finally, we support your development process by giving you quiz assignments. When the project is finished, you will have 2 end-to-end projects related to Regression and Classification. Thus, you will not only leave what you have learned in theory, but you will also be able to improve yourself practically. In this way, you will be able to add your projects to platforms such as GitHub and Kaggle and expand your portfolio.
HUB
You are invited to join our Introduction to Machine Learning Hub and use this space to discuss topics related to the course, share interesting and relevant material and links, ask questions and engage with peers.
All Our Programs Include
A joint certificate issued by Global AI Hub for each successful learner
Additional access to active mentoring by Global AI Hub experts
The certificate you will earn in this training is valid for privileged membership applications under theCoreRelation Program
Sponsored by
Thanks to the Swiss-based AI Business School and the «10million.AI» project this course is free
It is part of the national education campaigns aiming at educating more than 10 million learners for free on AI and other digital technologies
ROC(Receiver Operating Characteristics) & AUC (Area Under the Curve)
Commonly Used Classification Algorithms
K-Nearest Neighbors
Support Vector Machine
Decision Trees
Project 2: Prediction of Cancer with the Breast Cancer Dataset – Classification Project
MODULE 4 – DECISION TREES
What are Decision Trees?
Decision Trees Application
How Are Decision Trees Calculated?
Decision Trees Advantages
Information Gain
Entropy
Gini Index
Visualization of Decision Trees
Bagging
Boosting
XGBoost
MODULE 5 – UNSUPERVISED LEARNING
What is Unsupervised Learning?
Why Use Unsupervised Learning?
Unsupervised Learning Algorithms
Visualization and Dimension Reduction
Principal Component Analysis (PCA)
t-SNE
What is Clustering?
Clustering Types
Affinity Propagation
Hierarchical Cluster Analysis (HCA)
Density-based Spatial Clustering (DBSCAN)
Centroid-based
K-Means Clustering
Elbow Method
Mini-Batch K-Means
EPILOGUE
Practical Use Of What Has Been Learned
Further Projects
What’s Next?
Learning activities
The course includes a series of lessons that lead you through the content in small, bite-sized learning blocks. Each lesson includes exciting video sessions followed by thought-provoking assessment questions.
Video sessions have to be marked as complete and can be accessed freely after the completion of each lesson.
Assessment questions are graded for the calculation of certification progress.
Each day has a “Materials” section to help you revise the topics that are seen that day.
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