Python 机器学习

Machine Learning with Python
– Basics
We are living in the ‘age of data’ that is enriched with better computational power and
more storage resources,. This data or information is increasing day by day, but the real
challenge is to make sense of all the data. Businesses & organizations are trying to deal
with it by building intelligent systems using the concepts and methodologies from Data
science, Data Mining and Machine learning. Among them, machine learning is the most
exciting field of computer science. It would not be wrong if we call machine learning the
application and science of algorithms that provides sense to the data.
What is Machine Learning?
Machine Learning (ML) is that field of computer science with the help of which computer
systems can provide sense to data in much the same way as human beings do.
In simple words, ML is a type of artificial intelligence that extract patterns out of raw data
by using an algorithm or method. The main focus of ML is to allow computer systems learn
from experience without being explicitly programmed or human intervention.
Need for Machine Learning
Human beings, at this moment, are the most intelligent and advanced species on earth
because they can think, evaluate and solve complex problems. On the other side, AI is still
in its initial stage and haven’t surpassed human intelligence in many aspects. Then the
question is that what is the need to make machine learn? The most suitable reason for
doing this is, “to make decisions, based on data, with efficiency and scale”.
Lately, organizations are investing heavily in newer technologies like Artificial Intelligence,
Machine Learning and Deep Learning to get the key information from data to perform
several real-world tasks and solve problems. We can call it data-driven decisions taken by
machines, particularly to automate the process. These data-driven decisions can be used,
instead of using programing logic, in the problems that cannot be programmed inherently.
The fact is that we can’t do without human intelligence, but other aspect is that we all
need to solve real-world problems with efficiency at a huge scale. That is why the need for
machine learning arises.
Why & When to Make Machines Learn?
We have already discussed the need for machine learning, but another question arises
that in what scenarios we must make the machine learn? There can be several
circumstances where we need machines to take data-driven decisions with efficiency and
at a huge scale. The followings are some of such circumstances where making machines
learn would be more effective:
Lack of human expertise
The very first scenario in which we want a machine to learn and take data-driven decisions,
can be the domain where there is a lack of human expertise. The examples can be
navigations in unknown territories or spatial planets.
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Dynamic scenarios
There are some scenarios which are dynamic in nature i.e. they keep changing over time.
In case of these scenarios and behaviors, we want a machine to learn and take data-driven
decisions. Some of the examples can be network connectivity and availability of
infrastructure in an organization.
Difficulty in translating expertise into computational tasks
There can be various domains in which humans have their expertise,; however, they are
unable to translate this expertise into computational tasks. In such circumstances we want
machine learning. The examples can be the domains of speech recognition, cognitive tasks
etc.
Machine Learning Model
Before discussing the machine learning model, we must need to understand the following
formal definition of ML given by professor Mitchell:
“A computer program is said to learn from experience E with respect to some class of
tasks T and performance measure P, if its performance at tasks in T, as measured by P,
improves with experience E.”
The above definition is basically focusing on three parameters, also the main components
of any learning algorithm, namely Task(T), Performance(P) and experience (E). In this
context, we can simplify this definition as:
ML is a field of AI consisting of learning algorithms that:
Improve their performance (P)
At executing some task (T)
Over time with experience (E)
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Based on the above, the following diagram represents a Machine Learning Model:
Task (T)
Performan
ce (P)
Experienc
e (E)
Let us discuss them more in detail now:
Task(T)
From the perspective of problem, we may define the task T as the real-world problem to
be solved. The problem can be anything like finding best house price in a specific location
or to find best marketing strategy etc. On the other hand, if we talk about machine
learning, the definition of task is different because it is difficult to solve ML based tasks by
conventional programming approach.
A task T is said to be a ML based task when it is based on the process and the system
must follow for operating on data points. The examples of ML based tasks are
Classification, Regression, Structured annotation, Clustering, Transcription etc.
Experience (E)
As name suggests, it is the knowledge gained from data points provided to the algorithm
or model. Once provided with the dataset, the model will run iteratively and will learn
some inherent pattern. The learning thus acquired is called experience(E). Making an
analogy with human learning, we can think of this situation as in which a human being is
learning or gaining some experience from various attributes like situation, relationships
etc. Supervised, unsupervised and reinforcement learning are some ways to learn or gain
experience. The experience gained by out ML model or algorithm will be used to solve the
task T.
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Performance (P)
An ML algorithm is supposed to perform task and gain experience with the passage of
time. The measure which tells whether ML algorithm is performing as per expectation or
not is its performance (P). P is basically a quantitative metric that tells how a model is
performing the task, T, using its experience, E. There are many metrics that help to
understand the ML performance, such as accuracy score, F1 score, confusion matrix,
precision, recall, sensitivity etc.
Challenges in Machines Learning
While Machine Learning is rapidly evolving, making significant strides with cybersecurity
and autonomous cars, this segment of AI as whole still has a long way to go. The reason
behind is that ML has not been able to overcome number of challenges. The challenges
that ML is facing currently are:
Quality of data: Having good-quality data for ML algorithms is one of the biggest
challenges. Use of low-quality data leads to the problems related to data preprocessing
and feature extraction.
Time-Consuming task: Another challenge faced by ML models is the consumption of
time especially for data acquisition, feature extraction and retrieval.
Lack of specialist persons: As ML technology is still in its infancy stage, availability of
expert resources is a tough job.
No clear objective for formulating business problems: Having no clear objective and
well-defined goal for business problems is another key challenge for ML because this
technology is not that mature yet.
Issue of overfitting & underfitting: If the model is overfitting or underfitting, it cannot
be represented well for the problem.
Curse of dimensionality: Another challenge ML model faces is too many features of data
points. This can be a real hindrance.
Difficulty in deployment: Complexity of the ML model makes it quite difficult to be
deployed in real life.
Applications of Machines Learning
Machine Learning is the most rapidly growing technology and according to researchers we
are in the golden year of AI and ML. It is used to solve many real-world complex problems
which cannot be solved with traditional approach. Following are some real-world
applications of ML:
Emotion analysis
Sentiment analysis
Error detection and prevention
Weather forecasting and prediction
Stock market analysis and forecasting
Speech synthesis
Speech recognition
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Customer segmentation
Object recognition
Fraud detection
Fraud prevention
Recommendation of products to customer in online shopping.