- Instructor: Sophie Poyet
- Lectures: 19
- Students: 670
- Duration: 10 weeks
Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.
Audience
This course by Academy Europe will be useful for graduates, postgraduates, and research students who either have an interest in this Machine Learning subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner.
Prerequisites
The reader must have basic knowledge about Machine Learning. He/she should also be aware about Python, NumPy, Scipy, Matplotlib. If you are new to any of these concepts, we recommend you take up tutorials concerning these topics, before you dig further into this tutorial.
Curriculum
- 19 Sections
- 19 Lessons
- 10 Weeks
- Scikit Learn - Introduction1
- Scikit Learn - Modelling Process1
- Scikit Learn - Data Representation1
- Scikit Learn - Estimator API1
- Scikit Learn - Conventions1
- Scikit Learn - Linear Modeling1
- Scikit Learn - Extended Linear Modeling1
- Scikit Learn - Stochastic Gradient Descent1
- Scikit Learn - Support Vector Machines1
- Scikit Learn - Anomaly Detection1
- Scikit Learn - K-Nearest Neighbors (KNN)1
- Scikit Learn - KNN Learning1
- Scikit Learn - Classification with Naïve Bayes1
- Scikit Learn - Decision Trees1
- Scikit Learn - Randomized Decision Trees1
- Scikit Learn - Boosting Methods1
- Scikit Learn - Clustering Methods1
- Scikit Learn - Clustering Performance Evaluation1
- Scikit Learn - Dimensionality Reduction using PCA1
