Create Your Own Sophisticated Model with Neural Networks

Create Your Own Sophisticated Model with Neural Networks
Create Your Own Sophisticated Model with Neural Networks
English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 24m | 360 MB

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.

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

01 The Course Overview
02 Decision Trees – Classification and Visualization
03 Tuning a Decision Tree
04 Using Decision Trees for Regression
05 Reducing Overfitting with Cross-Validation
06 Implementing Random Forest Regression
07 Bagging Regression with Nearest Neighbors
08 Tuning Gradient Boosting Trees and AdaBoost Regressor
09 Defining a Stacking Aggregator with scikit-learn
10 Using LDA for Classification
11 Using QDA and SGD for Classification
12 Classifying Documents with Naive Bayes
13 Label Propagation with Semi-Supervised Learning
14 Perceptron Classifier
15 Multilayer Perceptron
16 Stacking with a Neural Network
17 Creating a Simple Estimator