Buy and Download - Udemy Introduction to Machine Learning and Face Detection in Python

2015
Udemy
Holczer Balazs
04:16:19
English

Learn the most up to date techniques in data mining from regression to neural networks.
This course is about the fundamental concepts of machine learning, focusing on neural networks, SVM and decision trees. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very very good guess about stock prices movement in the market.
In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together.
The first chapter is about regression: very easy yet very powerful and widely used machine learning technique. We will talk about Naive Bayes classification and tree based algorithms such as decision trees and random forests. These are more sophisticated algorithms, sometimes works, sometimes not. The last chapters will be about SVM and Neural Networks: the most important approaches in machine learning.

├── 01 Introduction
│ ├── 001 Introduction.mp4
│ └── 002 Introduction to machine learning.mp4
├── 02 Regression
│ ├── 001 Linear regression introduction.mp4
│ ├── 002 Linear regression example.mp4
│ ├── 003 Logistic regression introduction.mp4
│ ├── 004 Cross validation.mp4
│ ├── 005 Logistic regression example I - sigmoid function.mp4
│ ├── 006 Logistic regression example II.mp4
│ └── 007 Logistic regression example III - credit scoring.mp4
├── 03 K-Nearest Neighbor Classifier
│ ├── 001 kNN introduction.mp4
│ ├── 002 kNN introduction - normalize data.mp4
│ ├── 003 kNN example I.mp4
│ └── 004 kNN example II.mp4
├── 04 Naive Bayes Classifier
│ ├── 001 Naive Bayes introduction.mp4
│ └── 002 Naive Bayes example.mp4
├── 05 Support Vevtor Machine SVM
│ ├── 001 Support vector machine introduction.mp4
│ ├── 002 Support vector machine example I.mp4
│ └── 003 Support vector machine example II - image recognition.mp4
├── 06 Tree Based Algorithms
│ ├── 001 Decision trees introduction.mp4
│ ├── 002 Decision trees example I.mp4
│ ├── 003 Decision trees example II - iris data.mp4
│ ├── 004 Pruning and bagging.mp4
│ ├── 005 Random forests introduction.mp4
│ ├── 006 Boosting.mp4
│ ├── 007 Random forests example I.mp4
│ └── 008 Random forests example II - enhance decision trees.mp4
├── 07 Clustering
│ ├── 001 Principal component anlysis introduction.mp4
│ ├── 002 K-means clustering introduction.mp4
│ ├── 003 K-means clustering example.mp4
│ ├── 004 DBSCAN introduction.mp4
│ ├── 005 Hierarchical clustering introduction.mp4
│ └── 006 Hierarchical clustering example.mp4
├── 08 Neural Networks
│ ├── 001 Neural network introduction.mp4
│ ├── 002 Simple example - logical AND table.mp4
│ ├── 003 Training a neural network.mp4
│ ├── 004 Complex neural networks.mp4
│ ├── 005 Applications of neural networks.mp4
│ ├── 006 Deep learning.mp4
│ └── 007 Neural network example - XOR problem.mp4
├── 09 Outro
│ └── 001 Final words.mp4

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