Introduction to Machine Learning & Face Detection in Python
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Introduction to Machine Learning & Face Detection in Python

Learn the most up to date techniques in data mining from regression to neural networks, TensorFlow
Bestselling
4.4 (163 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
2,760 students enrolled
Created by Holczer Balazs
Last updated 3/2017
English
Current price: $10 Original price: $35 Discount: 71% off
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Includes:
  • 7.5 hours on-demand video
  • 7 Articles
  • 4 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
  • TensorFlow
View Curriculum
Requirements
  • Basic python
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.

Who is the target audience?
  • This course is meant for newbies who are not familiar with machine learning or students looking for a quick refresher
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Curriculum For This Course
Expand All 83 Lectures Collapse All 83 Lectures 07:25:03
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Introduction
4 Lectures 11:42


Installing Anaconda
02:08

Datasets we will use
00:56
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Regression
10 Lectures 01:15:41

Linear regression with gradient descent
08:41

Linear regression example I
08:17

Linear regression example II
03:56

Logistic regression introduction
10:15

Logistic regression introduction II - illustration
04:30

Cross validation
03:29

Logistic regression example I - sigmoid function
11:22

Logistic regression example II
05:48

Logistic regression example III - credit scoring
09:59
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K-Nearest Neighbor Classifier
4 Lectures 24:16
K-nearest neighbor introduction
11:23

K-nearest neighbor introduction - normalize data
03:40

K-nearest neighbor example I - simple problem
04:55

K-nearest neighbor example II - credit scoring
04:18
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Naive Bayes Classifier
4 Lectures 34:04
Naive Bayes introduction
09:03

Naive Bayes example I - simple example
02:40

Naive Bayes example II - credit scoring
02:52

Naive Bayes example III - text clustering
19:29
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Support Vector Machine (SVM)
6 Lectures 45:00
Support vector machine introduction I - linear case
08:50

Support vector machine introduction II - non-linear case
07:18

Support vector machine introduction III - kernels
04:23

Support vector machine example I - simple
04:07

Support vector machine example II - iris dataset
08:24

Support vector machine example III- character recognition
11:58
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Tree Based Algorithms
9 Lectures 37:56
Decision trees introduction
09:03

Decision trees example I
02:20

Decision trees example II - iris data
06:11

Pruning and bagging
05:07

Random forests introduction
03:43

Boosting
02:55

Random forests example I - simple example
02:18

Random forests example II - credit scoring
03:14

Random forests example III - iris dataset
03:05
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Clustering
8 Lectures 40:46
Principal component anlysis introduction
03:47

Principal component analysis example
04:32

K-means clustering introduction I
06:10

K-means clustering introduction II
04:03

K-means clustering example
05:55

DBSCAN introduction
04:56

Hierarchical clustering introduction
06:07

Hierarchical clustering example
05:16
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Neural Networks
29 Lectures 02:30:32
---------- NEURAL NETWORKS INTRODUCTION ----------
00:01

Axons and neurons in the human brain
08:22

Modeling human brain
07:24

Learning paradigms
02:58

Artificial neurons - the model
06:57

Artificial neurons - activation functions
06:16

Artificial neurons - an example
05:00

Neural networks - the big picture
04:33

Applications of neural networks
02:12

---------- BACKPROPAGATION ----------
00:01

Feedforward neural networks
08:10

Optimization - cost function
10:40

Simplified feedforward network
08:07

Feedforward neural network topology
06:04

The learning algorithm
05:17

Error calculation
06:06

Gradient calculation I - output layer
08:21

Gradient calculation II - hidden layer
03:49

Backpropagation
05:18

Backpropagation II
01:59

Applications of neural networks I - character recognition
04:06

Applications of neural networks II - stock market forecast
04:10

Deep learning
04:11

--------- IMPLEMENTATION --------------
00:01

Building networks
06:06

Building networks II
05:53

Handling datasets
03:20

Neural network example I - XOR problem
07:46

Neural network example II - iris dataset
07:24
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Face Detection
5 Lectures 24:55
Face detection introduction
04:00

Installing OpenCV
04:22

CascadeClassifier
08:48

CascadeClassifier parameters
04:07

Tuning the parameters
03:38
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Source Code & Data
4 Lectures 00:10
Source code
00:02

Data
00:02

Slides
00:01

Coupon codes - get any of my courses for a discounted price
00:04
About the Instructor
Holczer Balazs
4.4 Average rating
2,400 Reviews
24,737 Students
21 Courses
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.

Take a look at my website and join my email list if you are interested in these topics!