In this practical course, we are going to focus on supervised machine learning and how to apply it in Python programming language. Supervised machine learning is a branch of artificial intelligence whose goal is to create predictive models starting from a dataset. With the proper optimization of the models, it is possible to create mathematical representations of our data in order to extract the information that is hidden inside our database and use it for making inferences and predictions.
A very powerful use of supervised machine learning is the calculation of feature importance, which makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common approach for calculating feature importance is the SHAP technique.
In the realm of cutting-edge technology, machine learning stands at the forefront, revolutionizing industries and transforming the way we interact with the world. From personalized recommendations to autonomous vehicles, machine learning empowers computers to learn from vast amounts of data and make intelligent decisions. If you’ve ever been captivated by the idea of building intelligent systems, understanding the prerequisites for machine learning is your essential first step.
Embarking on a journey into machine learning requires a solid foundation in several key areas. As with any endeavor, building upon a sturdy groundwork paves the way for success. Let us unveil the prerequisites that will equip you with the skills to unravel the mysteries of machine learning and harness its potential to shape the future.
- Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more
- Machine learning Concept and Different types of Machine Learning
- Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..
- Feature engineering
- Python Basics
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