Scikit-learn, one of the foremost and often used Machine Learning software libraries particularly used for the Python Language. Machine Learning with scikit-learn Training primarily focuses on learning and leading real-world problems occuring in the ML Applications. Training includes the implementation of k-nearest neighbors, random forest, logistic regression and artificial neural networks ML Models. Scikit-learn installation can be done easily using various download managers like pip on different Operating Systems like Windows, Ubuntu, MacOS, Anaconda. In this training, the developers can develop various examples using k-Nearest Neighbors with features extractions on standardized terminologies. In addition to it, Naive Bayes introductory information is included in the ML with scikit-learn training. On the completion of the training, the aspirants will grab the opportunity to understand the complete scenario for scikit-learn to be used for Machine Learning Platforms.
- Understanding the core concept like bias and variance
- Feature extraction from categorical variables, images and text
- Understanding Documents and images using logistic regression methods
- Discovering hidden data structure in data using K-means clustering
- Evaluation of performance of ML systems in common tasks
- Integration of Computer Science and statistics for building smart and efficient models