Note — this is a long read, so if you want, you can download this article and read it offline here [https://payhip.com/b/YVGf].What’s the best way to understand Flexbox? Learn the fundamentals, then build lots of stuff. And that’s exactly what we’re going to do in this article. A few things to note * This article was written with intermediate developers in mind, and assumes you already know a bit about Flexbox. But… * If you know some CSS, but don’t know Flexbox at all, I wrote a comprehen
This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.
Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial
It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.
Below topics are explained in this Machine Learning course for beginners:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03
Machine learning problems can generally be divided into three types. Classification and regression, which are known as supervised learning, and unsupervised learning which in the context of machine learning applications often refers to clustering.
Machine learning problems can generally be divided into three types. Classification and regression, which are known as supervised learning, and unsupervised learning which in the context of machine learning applications often refers to clustering.
In the following article, I am going to give a brief introduction to each of these three problems and will include a walkthrough in the popular python library scikit-learn.
Before I start I’ll give a brief explanation for the meaning behind the terms supervised and unsupervised learning.
Supervised Learning: In supervised learning, you have a known set of inputs (features) and a known set of outputs (labels). Traditionally these are known as X and y. The goal of the algorithm is to learn the mapping function that maps the input to the output. So that when given new examples of X the machine can correctly predict the corresponding y labels.
Unsupervised Learning: In unsupervised learning, you only have a set of inputs (X) and no corresponding labels (y). The goal of the algorithm is to find previously unknown patterns in the data. Quite often these algorithms are used to find meaningful clusters of similar samples of X so in effect finding the categories intrinsic to the data.
In classification, the outputs (y) are categories. These can be binary, for example, if we were classifying spam email vs not spam email. They can also be multiple categories such as classifying species of flowers, this is known as multiclass classification.
Let’s walk through a simple example of classification using scikit-learn. If you don’t already have this installed it can be installed either via pip or conda as outlined here.
Scikit-learn has a number of datasets that can be directly accessed via the library. For ease in this article, I will be using these example datasets throughout. To illustrate classification I will use the wine dataset which is a multiclass classification problem. In the dataset, the inputs (X) consist of 13 features relating to various properties of each wine type. The known outputs (y) are wine types which in the dataset have been given a number 0, 1 or 2.
The imports I am using for all the code in this article are shown below.
import pandas as pd
import numpy as np
from sklearn.datasets import load_wine
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.metrics import f1_score
from sklearn.metrics import mean_squared_error
from math import sqrt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn import linear_model
from sklearn.linear_model import ElasticNetCV
from sklearn.svm import SVR
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
from yellowbrick.cluster import SilhouetteVisualizer
In the below code I am downloading the data and converting to a pandas data frame.
wine = load_wine()
wine_df = pd.DataFrame(wine.data, columns=wine.feature_names)
wine_df['TARGET'] = pd.Series(wine.target)
The next stage in a supervised learning problem is to split the data into test and train sets. The train set can be used by the algorithm to learn the mapping between inputs and outputs, and then the reserved test set can be used to evaluate how well the model has learned this mapping. In the below code I am using the scikit-learn model_selection function train_test_split
to do this.
X_w = wine_df.drop(['TARGET'], axis=1)
y_w = wine_df['TARGET']
X_train_w, X_test_w, y_train_w, y_test_w = train_test_split(X_w, y_w, test_size=0.2)
In the next step, we need to choose the algorithm that will be best suited to learn the mapping in your chosen dataset. In scikit-learn there are many different algorithms to choose from, all of which use different functions and methods to learn the mapping, you can view the full list here.
To determine the best model I am running the following code. I am training the model using a selection of algorithms and obtaining the F1-score for each one. The F1 score is a good indicator of the overall accuracy of a classifier. I have written a detailed description of the various metrics that can be used to evaluate a classifier here.
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="rbf", C=0.025, probability=True),
NuSVC(probability=True),
DecisionTreeClassifier(),
RandomForestClassifier(),
AdaBoostClassifier(),
GradientBoostingClassifier()
]
for classifier in classifiers:
model = classifier
model.fit(X_train_w, y_train_w)
y_pred_w = model.predict(X_test_w)
print(classifier)
print("model score: %.3f" % f1_score(y_test_w, y_pred_w, average='weighted'))
A perfect F1 score would be 1.0, therefore, the closer the number is to 1.0 the better the model performance. The results above suggest that the Random Forest Classifier is the best model for this dataset.
In regression, the outputs (y) are continuous values rather than categories. An example of regression would be predicting how many sales a store may make next month, or what the future price of your house might be.
Again to illustrate regression I will use a dataset from scikit-learn known as the boston housing dataset. This consists of 13 features (X) which are various properties of a house such as the number of rooms, the age and crime rate for the location. The output (y) is the price of the house.
I am loading the data using the code below and splitting it into test and train sets using the same method I used for the wine dataset.
boston = load_boston()
boston_df = pd.DataFrame(boston.data, columns=boston.feature_names)
boston_df['TARGET'] = pd.Series(boston.target)
X_b = boston_df.drop(['TARGET'], axis=1)
y_b = boston_df['TARGET']
X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(X_b, y_b, test_size=0.2)
We can use this cheat sheet to see the available algorithms suited to regression problems in scikit-learn. We will use similar code to the classification problem to loop through a selection and print out the scores for each.
There are a number of different metrics used to evaluate regression models. These are all essentially error metrics and measure the difference between the actual and predicted values achieved by the model. I have used the root mean squared error (RMSE). For this metric, the closer to zero the value is the better the performance of the model. This article gives a really good explanation of error metrics for regression problems.
regressors = [
linear_model.Lasso(alpha=0.1),
linear_model.LinearRegression(),
ElasticNetCV(alphas=None, copy_X=True, cv=5, eps=0.001, fit_intercept=True,
l1_ratio=0.5, max_iter=1000, n_alphas=100, n_jobs=None,
normalize=False, positive=False, precompute='auto', random_state=0,
selection='cyclic', tol=0.0001, verbose=0),
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,
gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,
tol=0.001, verbose=False),
linear_model.Ridge(alpha=.5)
]
for regressor in regressors:
model = regressor
model.fit(X_train_b, y_train_b)
y_pred_b = model.predict(X_test_b)
print(regressor)
print("mean squared error: %.3f" % sqrt(mean_squared_error(y_test_b, y_pred_b)))
The RMSE score suggests that either the linear regression and ridge regression algorithms perform best for this dataset.
There are a number of different types of unsupervised learning but for simplicity here I am going to focus on the clustering methods. There are many different algorithms for clustering all of which use slightly different techniques to find clusters of inputs.
Probably one of the most widely used methods is Kmeans. This algorithm performs an iterative process whereby a specified number of randomly generated means are initiated. A distance metric, Euclidean distance is calculated for each data point from the centroids, thus creating clusters of similar values. The centroid of each cluster then becomes the new mean and this process is repeated until the optimum result has been achieved.
Let’s use the wine dataset we used in the classification task, with the y labels removed, and see how well the k-means algorithm can identify the wine types from the inputs.
As we are only using the inputs for this model I am splitting the data into test and train using a slightly different method.
np.random.seed(0)
msk = np.random.rand(len(X_w)) < 0.8
train_w = X_w[msk]
test_w = X_w[~msk]
As Kmeans is reliant on the distance metric to determine the clusters it is usually necessary to perform feature scaling (ensuring that all features have the same scale) before training the model. In the below code I am using the MinMaxScaler to scale the features so that all values fall between 0 and 1.
x = train_w.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
X_scaled = pd.DataFrame(x_scaled,columns=train_w.columns)
With K-means you have to specify the number of clusters the algorithm should use. So one of the first steps is to identify the optimum number of clusters. This is achieved by iterating through a number of values of k and plotting the results on a chart. This is known as the Elbow method as it typically produces a plot with a curve that looks a little like the curve of your elbow. The yellowbrick library (which is a great library for visualising scikit-learn models and can be pip installed) has a really nice plot for this. The code below produces this visualisation.
model = KMeans()
visualizer = KElbowVisualizer(model, k=(1,8))
visualizer.fit(X_scaled)
visualizer.show()
Ordinarily, we wouldn’t already know how many categories we have in a dataset where we are using a clustering technique. However, in this case, we know that there are three wine types in the data — the curve has correctly selected three as the optimum number of clusters to use in the model.
The next step is to initialise the K-means algorithm and fit the model to the training data and evaluate how effectively the algorithm has clustered the data.
One method used for this is known as the silhouette score. This measures the consistency of values within the clusters. Or in other words how similar to each other the values in each cluster are, and how much separation there is between the clusters. The silhouette score is calculated for each value and will range from -1 to +1. These values are then plotted to form a silhouette plot. Again yellowbrick provides a simple way to construct this type of plot. The code below creates this visualisation for the wine dataset.
model = KMeans(3, random_state=42)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(X_scaled)
visualizer.show()
A silhouette plot can be interpreted in the following way:
The plot for the wine data set above shows that cluster 0 may not be as consistent as the others due to most data points being below the average score and a few data points having a score below 0.
Silhouette scores can be particularly useful in comparing one algorithm against another or different values of k.
In this post, I wanted to give a brief introduction to each of the three types of machine learning. There are many other steps involved in all of these processes including feature engineering, data processing and hyperparameter optimisation to determine both the best data preprocessing techniques and the best models to use.
Thanks for reading!
This beginner’s guide explains the concepts of deep learning and computer vision. Also get insights into 5 interesting applications of deep learning for computer vision.
This beginner’s guide explains the concepts of deep learning and computer vision. Also get insights into 5 interesting applications of deep learning for computer vision.
Deep learning and computer vision are trends at the forefront of computational, engineering, and statistical innovation. You’ve probably heard a lot about these trends if you follow technology blogs and news reports, however, it’s easy to get lost in the terminology without proper explanations.
This beginner’s guide explains the concepts of deep learning and computer vision. You’ll also get insights into five interesting applications of deep learning for computer vision.
What Is Deep Learning?To truly understand deep learning, the following definitions are important:
Bearing these definitions in mind, deep learning is a subset of machine learning in which machines use deep neural network architecture and algorithms to learn tasks autonomously.
What distinguishes deep learning is that its networks contain many hidden layers. This extra complexity empowers machines to learn from unstructured, unlabeled data as well as labeled and categorized data.
Note that none of these concepts are particularly new — rapid advances in computing power and technology enables the models to be fed with large volumes of data. The more data available, the more proficient the models become at learning tasks.
Speech recognition, image recognition, natural language processing (NLP), and computer vision are some of the areas deep learning has improved dramatically.
Many technology companies now specialize in providing platforms for training deep learning models in computer vision and other areas. Such companies have also facilitated further innovation in these artificial intelligence branches.
What Is Computer Vision?Computer vision is a scientific field spanning multiple disciplines that is concerned with getting computers to extract high-level meaning from images and videos.
The list of applications of computer vision is extensive; some of the most interesting include:
Deep learning has several uses in helping to achieve computer vision and overcoming its challenges — here are five of them.
Probably the computer vision capability familiar to most people is facial recognition, which is a common feature in today’s smartphones and cameras. Modern facial recognition systems at large enterprises are powered by deep learning networks and algorithms.
Facebook’s DeepFace identifies human faces in digital images using a nine-layer neural network. The system has 97 percent accuracy, which is famously better than the FBI’s facial recognition system. Google also developed its own highly accurate facial recognition system named FaceNet.
Classification with localization means identifying objects of a certain class in images and videos and highlighting their location, typically by drawing a box around the object. This particular computer vision use case is more challenging than simple object classification, which assigns labels to entire images (e.g. cat, bird, dog).
Classification with localization is particularly helpful in the medical field because healthcare organizations can train neural networks to rapidly identify cancerous regions of the body based on x-rays and other diagnostic medical images.
An extension of object classification and localization is object detection, in which the model can identify many objects of different types in images.
Semantic segmentation is a more advanced form of image classification and localization made possible by neural networks. With semantic segmentation, a model can classify and locate all of the pixels in an image or video. See the gif below to view semantic segmentation in action.
*Image source: *https://nikolasent.github.io/proj/proj4
The most exciting potential use for this computer vision function is real-time semantic segmentation used by self-driving cars. Identifying and localizing objects accurately can improve the safety and reliability of autonomous vehicles.
Colorization is the process of converting grayscale images to full-color images. The excitement of this use case comes from its aesthetic appeal. Colorization with deep learning can give new context and vibrancy to old black and white movies and photos. Check out this article for some impressive examples of image colorization using deep learning.
Technology giant Nvidia sent the Internet into a frenzy in 2018 when it announced a new technique that can reconstruct corrupted images. Wear and tear on old printed photographs can lead to holes, blurring, and other damage to the image. Digital images can get damaged and lose some of their pixels due to corrupt memory cards.
The technique uses deep learning to fill in the missing parts of images. According to the research paper, the deep learning model used by Nvidia can “robustly handle holes of any shape, size, location, or distance from the image borders”.
ConclusionYou’ve read about just a small sample of a wide range of exciting uses and applications of deep learning for computer vision. You’ve also got a beginner’s guide to understanding deep learning and computer vision.