Python for Machine Learning

In this article, we will learn about Python for machine learning. It is one of today’s trending topics as it is a subset of artificial intelligence. Machine Learning will have lots of applications in the future.

What is machine learning?

Machine learning is a technique where we build a model or engine and provide it with lots and lots of data. Our model then finds and learns patterns in the data so that the next time we give it new inputs, it can predict the outcome of the given output. So the more input data we provide, the more accurate the predictions will be.

Arthur Samuel, an American pioneer, defined machine learning as “a field of study that gives computers the ability to learn without being explicitly programmed.”

Tom Mitchel posed a learning problem as follows: a computer program is said to learn from experience E with respect to some task T and some performance measure P if its performance on T, as measured by P, improves with experience E.

Machine learning in action

Machine learning projects involve many steps. So let us take a look at them.

1. Import the data 

The first step is to import data. The data is often in the form of a CSV file. For example, we have a database with lots and lots of data. We export this data and store it in an organised CSV file.

2. Clean the data

This task involves removing duplicates and noises from the data. We want to avoid giving duplicates to the model for learning as the model will learn bad patterns and produce the wrong results.

We should ensure the data is clean by removing irrelevant and duplicated data and converting text-based into numerical values.

3. Split data into training and testing sets

To ensure our model produces the right results, we reserve a part of the dataset for training and a part for testing.

4. Create a model

It involves selecting an algorithm to analyse the data. There are various machine learning algorithms available. Each algorithm has pros and cons based on accuracy and performance. The algorithm depends on input data and the problem we are trying to solve.

5. Train the model

We give the model training data, and the model looks for patterns in the training data.

6. Make predictions

After training, we will program the model to make predictions. But, unfortunately, the predictions are not always accurate.

7. Evaluate and improve

In case of inaccurate predictions, we need to go back to our model and improve the model by selecting a different algorithm or giving more training data.

Python Libraries and tools for Machine Learning

1. Numpy

Numpy provides a multi-dimensional array and is a popular library.

2. Pandas

Pandas is a data analysis library that provides a concept known as a ‘data frame.’ A data frame is a data structure with two dimensions, like an excel spreadsheet. We have data in rows and columns.

3. Matplotlib

The third library is matplotlib. It is a two-dimensional plotting library for creating graphs and plots.

4. Scikitlearn

It is a popular machine-learning library. It is because it provides all standard algorithms, such as decision trees, neural networks, etc.

Need for machine learning

Humans are by far the most intelligent species with the ability to think and make independent choices. The idea is to replicate the same notion in machines to give them the ability to think and make decisions or accurate predictions. It gives rise to machine learning.

Organisations rely heavily on artificial intelligence, deep learning and machine learning to process vast amounts of information and predict outcomes. The machine makes decisions based on the data provided. Machine learning offers to solve real-world problems efficiently at a large scale.

Why and when to make machines learn?

There are several reasons to use machine learning. Some of them are as follows:

1. Lack of human expertise

Machines can make decisions when there is no human expert to guide the decision-making process. A trained machine makes well-informed choices in unknown territories or spatial planets.

2. Dynamic scenarios

Dynamic scenarios keep changing over a period of time, and we need to translate these changes into programming tasks and, thus, machine learning to perform such tasks. Examples include voice and speech recognition.

3. Difficulty in translating expertise into computational tasks

Machine learning helps us to translate human expertise to perform computational tasks easily.

Machine learning model

Machine learning models are classified as unsupervised or supervised.

1. Supervised learning

It involves a series of functions that map an input to output based on a series of input-output pairs. For instance, if we have a dataset with age and shoe sizes, we can use a machine learning model that can help us predict the shoe size based on age.

Supervised learning is further divided into regression and classification.

a. Regression Model

We use independent predictors to find a target value in the regression model. Then, we can use it to find the relationship that exists between a dependent variable and an independent variable.
In the regression model, the output continues.

Types of regression model:

Linear regression: it simply means finding a line that fits the data.

Decision tree

As its name suggests, a decision tree is in the form of a tree, and each node represents a decision. And the lines are the outcomes of the decisions.

Random forests

It is an ensemble learning technique that builds off decision trees and involves creating multiple decision trees using bootstrap datasets of original data. Then, it randomly selects a subset of variables at each step of the decision tree.

The model then chooses the mode of all the decision trees. Then it reduces the risk of error in choosing an individual tree by relying on the “majority wins” model.

Neural network

The human mind is the inspiration for this popular machine-learning model. Similar to the neurons in our brains, the circle represents nodes. The blue circle represents an input layer, the black circle represents a hidden layer, and the green circles are the output layer.

Each node in the hidden layer contains a function that the input goes through, finally learning to give out the output in the green circles.

Classification

Classification gives discrete output values. Some common classification models include logistic regression. It is similar to linear regression, but we use it to model the probability of a finite number of outcomes, typically two.
The output values can only be between 0 and 1

Support vector machine

It is a supervised machine learning technique that aims to find a hyperplane in n-dimensional space that can distinctly classify the data points.

Naive Bayes

It is a classifier that acts as a probabilistic machine-learning model for classification tasks. The main essence of the classifier is primarily based on the Bayes theorem.

Decision tree random forest and neural network

These models follow a similar logic as the previously explained ones, and the only point of difference is the output here is discrete.

2. Unsupervised learning

Unlike supervised learning, these models are used to draw inferences and find patterns with input data without references to the label outcome.
Two methods under unsupervised learning include dimensionality reduction and clustering.

Clustering

Clustering involves the grouping of data points. It is frequently used for customer segmentation in data mining, fraud detection and document classification.
Common clustering techniques include K-means, Hierarchical, Mean shift, and density-based clustering.

Dimensionality reduction

It is the process used to reduce the feature set’s dimension. Simply put, it reduces the number of features. Most dimensionality reduction techniques can be divided into feature elimination or feature extraction. Principal component analysis (PCA) is a popular method.

Challenges in machine learning

Even though machine learning is rapidly taking over the world, it still has a long way to go, as there are still several challenges in machine learning. Let’s look at some of them below:

1. Quality of data

Machine learning requires good-quality data. Moreover, we face data preprocessing and image extraction issues in the case of low-quality data.

2. Time-consuming task

Data acquisition, feature extraction and retrieval are time-consuming tasks.

3. Lack of specialist person

There are only a few expert resources available for the job.

4. Cannot formulate business propositions

The technology has yet to mature, so there is no clear objective for formulating business propositions.

5. Issues of overfitting and underfitting

We are unable to represent the dataset that is underfitting and overfitting.

6. Curse of dimensionality

There are too many features in data points.

7. Difficulty in deployment

Due to its extreme complications, deploying ML in real life is difficult.

Applications of machine learning

• Sentiment analysis
• Emotion analysis
• Fraud detection
• Customer analysis
• Fraud prevention
• Stock market prediction
• Error detection and prevention
• Speech recognition
• Speech synthesis
• Weather forecasting and prediction
• Object recognition

Who should learn machine learning?

Anyone thinking of establishing a career in machine learning must be well-versed in the algorithms and learning techniques of machine learning. As machine learning is a growing field with many opportunities and huge scope, it gives you a chance to work at some of the world’s best companies.

Prerequisites to get the best of the machine learning tutorial

Before we get to machine learning tutorials, every learner needs to understand linear algebra, statistics and basic mathematics concepts.

Most learners make the mistake of jumping directly into machine learning with no knowledge of mathematics and find it harder to understand basic ML concepts. It is essential to know that mathematics is the foundation of ML.

A basic understanding of Python syntax can help one easily understand the topics.

How to become a machine learning engineer?

To learn machine learning, you must select a road map based on your knowledge of the topics beforehand. If you are a beginner, start by covering basic concepts, and if you already understand the concepts, then begin by creating small projects such as speech recognition, image detection, etc.
Machine learning tutorials and certifications are the most trusted methods to become an expert on the topic.

Machine learning career path

If you want to kickstart a machine learning career, many companies are actively hiring machine learning experts. Companies whose main products include data science actively hire such experts.

Cloud-based companies also hire machine learning engineers. These companies allow users to upload their data to the cloud, which performs analytics. Since big data is rapidly growing, there will always be a demand for machine learning.

Conclusion

So in this article, we covered the basics of how to begin the journey in machine learning. We hope you liked the explanation.