Introduction to Machine Learning & Face Detection in Python

Learn the most up to date techniques in data mining from regression to neural networks
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2,355 students enrolled Bestselling in Machine Learning
Instructed by Holczer Balazs IT & Software / Other
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  • Lectures 54
  • Length 5.5 hours
  • Skill Level Intermediate Level
  • Languages English, captions
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 9/2015 English Closed captions available

Course Description

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.

What are the requirements?

  • Basic python

What am I going to get from this course?

  • Solving regression problems
  • Solving classification problems
  • Using neural networks
  • The most up to date machine learning techniques used by firms such as Google or Facebook
  • Face detection with OpenCV

What is the target audience?

  • This course is meant for newbies who are not familiar with machine learning or students looking for a quick refresher

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Introduction
Preview
01:33
Introduction to machine learning
Preview
05:47
Section 2: Regression
Linear regression introduction
Preview
06:51
Linear regression example
10:42
Logistic regression introduction
12:59
Cross validation
04:05
Logistic regression example I - sigmoid function
14:24
Logistic regression example II
05:29
Logistic regression example III - credit scoring
11:49
Section 3: K-Nearest Neighbor Classifier
K-nearest neighbor introduction
09:13
K-nearest neighbor introduction - normalize data
02:35
K-nearest neighbor example I
02:35
K-nearest neighbor example II
02:16
Section 4: Naive Bayes Classifier
Naive Bayes introduction
07:50
Naive Bayes example I
07:05
Naive Bayes example II - text clustering
19:29
Section 5: Support Vector Machine (SVM)
Support vector machine introduction
15:07
Support vector machine example I
03:44
Support vector machine example II - character recognition
11:58
Section 6: Tree Based Algorithms
Decision trees introduction
09:36
Decision trees example I
01:39
Decision trees example II - iris data
14:55
Pruning and bagging
03:32
Random forests introduction
02:42
Boosting
02:47
Random forests example I
02:56
Random forests example II - enhance decision trees
02:26
Section 7: Clustering
Principal component anlysis introduction
02:50
Principal component analysis example
04:32
K-means clustering introduction
07:43
K-means clustering example
05:55
DBSCAN introduction
05:56
Hierarchical clustering introduction
05:54
Hierarchical clustering example
05:16
Section 8: Neural Networks
Neural network introduction
11:03
Feedfordward neural networks
07:42
Training a neural network
08:36
Error calculation
03:12
Gradients calculation
08:35
Backpropagation
03:18
Applications of neural networks
05:56
Deep learning
02:41
Neural network example I - XOR problem
08:06
Neural network example II - face recognition
16:17
Section 9: Face Detection
Face detection introduction
07:21
Installing OpenCV
03:09
CascadeClassifier
08:48
CascadeClassifier parameters
04:07
Tuning the parameters
03:38
Section 10: Outro
Final words
01:19
Section 11: Source Code & Data
Source code & CSV files
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Instructor Biography

Holczer Balazs, Software Engineer

Hi!

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

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