Machine Learning with Scikit-Learn

Course Overview

Master the core concepts of statistical learnings implemented in Scikit-learn to quickly extract features from any type of data. In a practice-as-you-learn approach, gain in-depth working knowledge of tools in Scikit learn library to implement classification, regression, clustering, dimensionality reduction and many more classic data science concepts.In cloud labs, practice preprocessing of data, model selection, classical regression, naive Bayes, SVM and much more.Be industry-ready to develop cutting-edge intelligent apps in the fastest possible time with our machine learning models with Scikit-Learn training course.

At the end of the training, participants will be able to:

  1. Install, configure and test Scikit-learn library to work with Python in Linux / Windows
  2. Gain a deep understanding of built-in Machine Learning models and usage
  3. Implement image recognition using SVM
  4. Classify text using Naive Bayes \
  5. Deploy a decision tree classifier Implement unsupervised learning with PCA
  6. Master advanced features such as feature extraction, feature selection, and model selection.
  7. Lead smart-app development projects that manipulate complex and large datasets

Pre-requisite

Required: Development experience in Python, Linux, Hands-on with Python libraries, Fair understanding of Machine Learning, Deep Learning Models

Duration

2 days

Course Outline

  1. Linux
  2. Windows
  3. Checking Installation
  1. Learning Models
  2. Supervised Learning
  3. Unsupervised Learning
  1. Image Recognition
  2. Training Support Vector Machines
  1. Text Classification
  2. Preprocessing data
  3. Training the classifier
  • Evaluating the performance
  1. Preprocessing the data
  2. Training a decision tree classifier
  3. Interpreting the decision tree
  4. Evaluating the performance
  1. Visualization
  2. Feature Selection
  • Alternative Clustering Methods
  1. Feature extraction
  2. Feature selection
  3. Model selection

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