A one-stop solution to learning complex models with Neural Networks and understanding the basics of Natural Language Processing

Scikit-learn has evolved as a robust library for Machine Learning applications in Python with support for a wide range of Supervised and Unsupervised Learning Algorithms.

With this course you will learn the Decision Tree algorithms and Ensemble Models to build Random Forest, Regression Analysis. You will focus on Decision Trees and Ensemble Algorithms. Moving forward, you learn to use scikit-learn to classify text and Multiclass with scikit-learn. You will explore various algorithms for classification. You will also look at Naive Bayes model and Label Propagation. Finally, you'll use Neural Networks using different Classifiers and create your own Simple Estimator.

Style and Approach
This course consists of practical scikit-learn videos that target novices as well as intermediate users. It explores technical issues in depth, covers additional protocols, and supplies many real-life examples so that you are able to implement scikit-learn in your daily life.

What You Will Learn
Tuning a decision tree
Bagging regression with nearest neighbors
Tuning an AdaBoost regressor
Using SGD for classification
Exploring the Perceptron classifier
Stack with a neural network

Table of Contents
TREE ALGORITHMS
ENSEMBLE METHODS
TEXT AND MULTICLASS CLASSIFICATION WITH SCIKIT-LEARN
NEURAL NETWORKS