Machine learning
Introduction to machine learning
Computers that figure out what to do without
talking have been the long awaited thing.
The idea of being able to drive, identifying
pedestrians and potholes, and reacting quickly and efficiently to changes in
the environment to get them safely to their destinations—this is machine
learning.
How It Works Let's start with analyzing business
data.
ML is a kind of AI that can understand and learn
from large amounts of data. Take Twitter as an example. According to
Internet Live Stats, Twitter users send about 500 million tweets every day,
which is equivalent to about 200 billion tweets per year. It is humanly
impossible to analyze, classify, classify, learn and predict anything with that
number of tweets.
Machine learning requires a significant amount
of work for businesses to obtain valuable information. To get the most out
of ML, you need to have clean data and know what questions you have. You
can then choose the model and algorithm that best suits your business. ML
is not a simple or easy process. The success of ML requires constant
effort.
ML has a life cycle.
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understand. Reasons to turn to and learn from ML
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Data collection and cleanup. It has the required amount of data and is
clean enough to provide insight.
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function selection. It involves determining the data that needs to be
provided to ML to build an ML model. Depending on the type of algorithm,
there are different methods available for selecting features. For example,
let's say you are using a decision tree algorithm. In this case, the
analyst or modeling tool can apply an "interesting score", a column
from the database, to determine whether that data should be used to build the
model.
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Select model. Select files (models) that are trained to process and
find specific items in your data. The model is given algorithms to work
with, and by testing the data, you can combine the two to draw conclusions.
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training and tuning. The conclusion is that depending on the model,
the question can be answered.
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You need to evaluate your models and algorithms to see if they are ready
for use, or if you need to go back a few steps and refine your models,
features, algorithms or data to achieve your goals,
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Deploy the trained model to production.
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Review the output of an existing model in production
What is machine learning used for? machine learning applications
Machine learning is a way for businesses to
understand data and learn from it. There are many sub-fields available to
businesses. Depending on the use case, whether it's increasing revenue,
providing search capabilities, integrating voice commands into products, or
self-driving cars.
machine learning subfield
ML is used a lot in
business today, and its use will grow even more. Subfields of ML include
social media and product recommendations, image recognition, health checkups,
language translation, speech recognition, data mining, and more.
Social media platforms
like Facebook, Instagram, or LinkedIn are also using ML to suggest pages to
follow or groups to join, based on posts you like. Get historical data
about posts others liked or similar to yours and suggest or add to your
feed.
Ecommerce sites can
also use ML to recommend products based on previous purchases, your searches,
and the behavior of other users similar to you.
An important use of ML
today is image recognition. Social media platforms encouraged tagging
people in photos based on ML. Police could use it to find suspects in
photos or videos. A plethora of cameras installed at airports, shops and
doorbells can help you figure out who committed the crime or where the offender
went.
Medical checkups also
make good use of ML. After an event like a heart attack, you can go back
and see warning signs that you might have overlooked. Systems used by
hospitals can learn to see connections from inputs (behaviours,
test results, or symptoms) to outputs (such as a heart attack) given their past medical records. The
doctor can then enter future notes and test results into the system, allowing
the machine to detect heart attack symptoms much more reliably than humans.
Language translation of
web pages or apps for mobile platforms is another example of ML. Some apps
perform more advanced tasks than others depending on the ML models, techniques,
and algorithms they use.
Today, everyday use of ML is in banks and credit
cards. ML can detect quickly, but it takes a long time for humans to
discover. Transactions that are heavily inspected and labeled (false or
not) allow ML to learn how to identify fraud in a single future
transaction. A
great ML for this is data mining.
data mining
Data mining is a type
of ML that analyzes data to make predictions or discover patterns within big
data. The term is a bit misleading because it doesn't require anyone, be
it a malicious actor or an employee. Instead, the process involves
discovering patterns in the data that will be needed to make decisions in the
future.
Consider, for example,
a credit card company. Your bank may be aware of suspicious activity on
your card. How could a bank detect such activity so quickly and send an
almost instantaneous alert? What makes this fraud prevention possible is
continuous data mining. As of early 2020, there were over 1.1 trillion
cards issued in the United States alone. The number of transactions on
that card generates a wealth of data for mining, pattern detection, and
learning to identify future suspicious transactions.
deep learning
Deep learning is a
specific type of ML based on neural networks. Neural networks are
responsible for mimicking how neurons in the human brain function to make
certain decisions or to understand something. For example, a 6-year-old
would be able to tell her mother apart from a crosswalk by looking at her face,
because a 6-year-old would quickly analyze many details such as her hair color,
facial features, scars, and more, all in the blink of an eye. can. Machine
learning replicates this in the form of deep learning.
A
neural network consists of 3-5 layers (input layer, 1-3 hidden layers, output
layer). Hidden ones make decisions one by one towards the output layer or
conclusion. what hair color? what eye color? Are there any
scars? As it grows into hundreds of layers, this is called deep learning.
Types of Machine Learning
There are basically four types of machine
learning algorithms: supervised, semi-supervised, unsupervised, and
reinforcement. ML experts estimate that about 70% of ML algorithms in use
today are supervised. Work with known or labeled datasets, such as pictures
of dogs and cats. As both types of animals are known, administrators can
label photos before providing them to the algorithm.
Unsupervised ML algorithms learn from unknown
datasets. Take the TikTok video as an example. There are so many
videos with so many topics that it is impossible to train the algorithm in a
supervised way. The data is not yet labeled.
A semi-supervised ML
algorithm is initially trained on a small, known and labeled dataset. It
is then applied to a larger, unlabeled data set to continue training.
Enhanced ML algorithms
are not initially trained. They learn by trial and error on the
go. Think of a robot that learns to navigate a pile of rocks. Each
time you fall, you learn what doesn't work and change your behavior until you
succeed. Think about dog training and the use of treats to teach different
commands. With positive reinforcement, the dog continues to carry out
commands and changes behaviors that do not display a favorable
response.
Supervised and unsupervised machine learning
supervised machine learning
Find patterns using known, established, and
classified data sets. Expand on previous ideas for photos of dogs and
cats. You can have huge data sets with thousands of different animals in
millions of photos. Because the animal types are known, they can be
grouped, labeled, and passed to a supervised ML algorithm to learn how to
understand them.
The supervisory algorithm now compares the input
to the output and the picture to the label of the animal type. It will
eventually learn to recognize certain kinds of animals in new pictures it
encounters.
unsupervised machine learning
Unsupervised ML algorithms are like spam filters
today. Initially, administrators could program a spam filter to understand
spam by searching for specific words in emails. But that's not possible
anymore, and it works fine without supervision in ML. An unsupervised ML
algorithm is fed with unlabeled emails looking for patterns. When these
patterns are found, you learn what spam looks like and identify it in
production.
machine learning technology
ML technology solves problems. Choose a
specific ML technique based on the problem you are facing. Here are 6 common ones.
regression technique
Regression can be used to predict home market
prices in Minnesota for December or to determine the optimal selling price for
household goods. According to regression, even if the price fluctuates, it
always returns to the average price. You can plot the price over time on a
graph and find the average over time. If the red line continues to rise
above the chart, it is possible to predict the future.
classification
As expected, you can find good customers (who
always come back and spend a lot of money) or, as expected, customers to start
shopping elsewhere. If you can look back over time and find predictors for
each customer segment, you can apply them to your current customers and predict
which group they will fit into. This allows you to market more effectively
and convert potentially leaving customers into excellent returning
customers.
clustering
Unlike classification
techniques, clustering is unsupervised ML. In clustering, the system finds
a way to group data that it doesn't know how to group.
Google uses clustering
for generalization, data compression, and privacy for products such as YouTube
videos, Play apps, and music tracks.
Anomaly detection
Anomaly detection is
used to find outliers, such as finding black sheep in a herd. Given the
vast amount of data, these anomalies are impossible for humans to
detect. But if, for example, a data scientist has provided system medical
billing data from many hospitals, anomaly detection finds a way to group
claims. You can discover a set of outliers that turn out to be where the
fraud is taking place.
Shopping Cart Analysis
The logic of shopping
cart analysis makes it possible to predict the future. A simple example -
if a customer puts ground beef, tomatoes, and tacos in a basket, you can
predict that they will add cheese and sour cream. These predictions can
help online shoppers make valuable offers on items they might have forgotten,
or help group products in stores, generating additional sales.
Two MIT professors used
this approach to discover “pioneers of failure .” As a result,
some customers like a product that has failed. When they find this, they know
whether to keep selling the product and what marketing to do to increase sales
to the right customers. You can decide to apply.
time series data
Time series data is
typically collected through a fitness monitor on the wrist. It can collect
heartbeats per minute, how many steps we take per minute or hour, and some even
measure oxygen saturation over time. With this data, you can predict when
someone will run in the future. Because of time-based data on vibration
levels, dB noise levels, and pressures, you can also collect data about
machines and predict failures.
machine learning algorithms
If ML needs to learn from data, how do you
design algorithms to learn and find statistically significant data? ML
algorithms support the process of supervised, unsupervised, or augmented ML.
Let's look at some of the most common specific
algorithms. Here are the top 5 currently in use.
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A linear regression algorithm establishes a relationship by fitting the
independent and dependent variables to a graph and plotting a straight line for
the mean or trend. Merriam-Webster defines regression as a function that
produces the mean value of a random variable, given that one or more
independent variables have specified values. This definition also
applies to logistic regression.
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Logistic (aka logit) regression, like linear regression, fits a variable to
a graph, but the lines are not linear. This line is a sigmoid
function.
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Decision trees are very commonly used algorithms within supervised
ML. Used to classify data based on categorical and continuous variables.
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Support Vector Machine draws a hyperplane based on the two nearest data
points. This encloses the class to separate the data. Classify data
based on N-dimensional space. N represents the number
of different features it has.
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Naive Bayes calculates the probability of a particular outcome. This
is very effective and outperforms more sophisticated classification
models. A naive Bayesian classifier model will understand that a given
feature is not related to the presence of any other particular ffeature.
machine learning model
After combining ML types (supervised,
unsupervised, etc.), techniques and algorithms, the result is a trained
file. This file can now be fed with new data and can recognize patterns
and make predictions or decisions as needed for the business, manager or
customer.
Best language for machine learning
A machine learning language is a way to write
instructions for a system to learn. Each language has a community of users
for support to learn from or guide others. Libraries are included with
each language for machine learning use.
Here are the top 10 according to our 2019 GitHub
Top 10 survey . .
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Python
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C++
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JavaScript
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Java
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C#
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Julia
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Shell
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R
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TypeScript
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Scala - the language used to interact with big data
Python machine learning
Python is the most common ML language, so I'll
go into more detail here.
Python is an
open source object-oriented language named after Monty Python . Because it is interpreted, it is converted
to bytecode before execution in the Python virtual machine.
There are a number of features that make Python
a favorite for ML.
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A powerful set of packages currently available. There are certain ML
packages like numpy, scipy and panda.
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Prototypes can be created quickly and easily.
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There are a variety of tools that enable collaboration.
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As data scientists move from extracting to modeling to updating ML
solutions, Python may continue to be the language of choice. Data
scientists don't have to change languages as they move through their
lifecycles.
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