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Introduction to Machine Learning: A Beginner’s Guide

Feb 24, 2023 | Blog

What is Machine Learning?

Artificial Intelligence (AI) is a field of computer science and engineering that focuses on creating machines that can perform tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making. AI systems can be trained to recognize patterns in data, make predictions, and take actions based on their observations and experiences.

There are several different approaches to AI, including rule-based systems, expert systems, and machine learning. Rule-based systems rely on pre-defined rules and logic to make decisions and perform tasks, while expert systems use the knowledge of human experts to make decisions. Machine learning is a subset of AI that involves training algorithms on large amounts of data, allowing them to learn patterns and make predictions or decisions based on that data.

Supervised, unsupervised, and reinforcement learning are the three main types of machine learning. Each type of learning has its own strengths and weaknesses and is suitable for different types of problems. Supervised learning is most commonly used for tasks such as classification and regression, unsupervised learning is useful for tasks such as clustering and anomaly detection, and reinforcement learning is suitable for tasks that involve decision-making and control, such as robotics and game playing.

How it works:

The process of training a machine learning model is iterative and often involves iterating through these steps multiple times to achieve the desired level of performance. It requires a combination of technical expertise, domain knowledge, and creativity to effectively train and deploy machine learning models. However, this process involves several steps:

Popular machine learning algorithms:

After talking about the process of training a machine learning model, it is important to explore the popular machine learning algorithms that are commonly used in various applications. In the following some examples of commonly used algorithms and their applications are listed.

  • Linear Regression: Linear regression is a type of supervised machine learning algorithm used for predicting a continuous output variable based on one or more input variables. It is a linear approach to modelling the relationship between the input and output variables. In other words, it tries to find the best linear relationship between the input variables and the output variable. Linear regression is commonly used in various fields, such as finance, economics, social sciences, and engineering, to predict outcomes such as sales, stock prices, housing prices, and more. It is a simple yet powerful algorithm that provides valuable insights into the relationships between variables.
  • Logistic Regression: Logistic regression is also a type of supervised machine learning algorithm used for classification problems. Unlike linear regression, which predicts a continuous output variable, logistic regression predicts the probability of an event occurring based on the values of input variables. This algorithm is popular for binary classification problems, where the output variable has only two possible values (e.g., true/false or yes/no). It can also be extended to handle multi-class classification problems, where the output variable can have more than two values. It is widely used in various fields, including healthcare, marketing, finance, and more.
  • Decision Trees: As a supervised machine learning algorithm, they work by recursively partitioning the input space into smaller subsets based on the values of input variables, in order to create a tree-like model of decisions and their possible consequences. Decision trees have several advantages, including their interpretability and ability to handle non-linear relationships between input variables and output variables. They can also handle both categorical and continuous input variables, and can be extended to handle multi-class classification and regression problems. However, decision trees are prone to overfitting, especially when the tree is too deep or too complex. Decision trees and their variations, such as random forests and gradient boosting, are widely used in various fields, including finance, healthcare, marketing, and more.
  • Random Forests: Random forests are an extension of decision trees in machine learning, and are commonly used for classification, regression, and other prediction tasks. They work by creating an ensemble of decision trees, where each tree is built using a randomly selected subset of the input features and a bootstrapped sample of the training data. During the training phase, each tree in the forest is grown independently, resulting in a collection of diverse trees. Random forests are widely used in various fields, including finance, healthcare, marketing, and more. They can also be used to predict outcomes such as customer behaviour, disease diagnosis, credit risk, and more.
  • Support Vector Machines (SVM): Support vector machines (SVMs) are a type of supervised machine learning algorithm used for classification and regression problems. They work by finding a hyperplane in a high-dimensional space that separates the data points into two or more classes based on their input features. SVMs have several advantages, including their ability to handle both linear and non-linear relationships between input features and output variables, and their effectiveness in high-dimensional spaces. They are also able to handle noisy data and are less prone to overfitting than other algorithms. They are widely used in various fields, including image recognition, bioinformatics, and more.
  • K-Nearest Neighbours (KNN): It works by finding the K closest data points in the training dataset to a given query point, and then predicting the class or value of the query point based on the majority or average of the K closest points. KNN is a simple algorithm that can be used for both classification and regression problems. It is non-parametric, meaning it does not make any assumptions about the underlying distribution of the data. However, it requires a large amount of memory to store the entire training dataset, and can be computationally expensive when the dataset is very large. KNN is widely used in various fields, including healthcare, finance, and more.

How to apply:

Python is a popular programming language for machine learning and data analysis due to its simplicity, readability, and vast array of libraries and tools available for machine learning.

Python’s clean syntax allows developers to write concise and maintainable code, making it easy to prototype, experiment, and iterate on machine learning models.

It also has a large and active community of developers who have built an extensive collection of powerful machine learning libraries and frameworks, including TensorFlow, PyTorch, scikit-learn, and Keras.

These libraries provide a range of capabilities for tasks such as data preprocessing, feature engineering, model training, and evaluation, making it easier to build complex machine learning systems with minimal coding.

Before starting with any machine learning algorithm, it is essential to import the necessary libraries.

NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, and Keras are some of the popular libraries used in machine learning.

For example, to import Scikit-Learn in Python, use the following code:

import sklearn

In the next step, we need to load the data into Python using libraries such as Pandas or NumPy. So we can use the following code to load a dataset using Pandas.

import pandas as pd
data = pd.read_csv('filename.csv')

Next, the imported data must be divided into a training and a testing set. The training set is used to train the machine learning algorithm, while the testing set is used to evaluate its performance. To do this we can use the following code.

from sklearn.model_selection import train_test_split
train_data, test_data, train_labels, test_labels = train_test_split(data, 
labels, test_size=0.2, random_state=42)

Once the data is split, the machine learning algorithm can be trained on the training set. Depending on the algorithm, use the appropriate method from the imported library to train the machine learning algorithm on the training data.

from sklearn.tree import DecisionTreeClassifier
dtclassifier = DecisionTreeClassifier(), train_labels)

Once the machine learning algorithm is trained on the training data, it needs to be evaluated on the testing data. Use evaluation metrics, such as accuracy, precision, recall, or F1-score, to evaluate the performance of the machine learning algorithm on the testing data.

from sklearn.metrics import accuracy_score
predictions = dtclassifier.predict(test_data)
accuracy = accuracy_score(test_labels, predictions)

Once the model has been trained and tested, deploy it in a production environment to make predictions on new data. This involves using the trained model to predict on new data and integrating it with other software systems as needed.

Common applications of machine learning:

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Machine learning has become an essential tool in a wide range of industries and applications which some common applications of it are listed below:

Image and speech recognition (such as identifying objects in photos, recognizing faces, or transcribing spoken language) – Natural language processing (e.g. chatbots, sentiment analysis, and language translation) – Fraud detection (e.g. credit card fraud detection, insurance fraud detection, and identity theft detection) – Recommendation systems (e.g. recommending products or services based on past behaviour or preferences)  – Autonomous vehicles to enable them to recognize objects, navigate roads, and make decisions; as well as Healthcare (such as predicting patient outcomes, identifying high-risk patients, and improving disease diagnosis and treatment).

In this blog, we have endeavoured to provide you with general information about machine learning. However, if you wish to delve deeper into this topic and seek more extensive knowledge, you can peruse the following links to gain useful insights.