Introduction to Machine Learning: A Beginner's Guide

Nowadays, we hear a lot of these buzz words in the field of technology called Machine Learning, Deep Learning, Generative AI and so on. So, what's all that hype about? Artificial Intelligence is penetrating in almost every domain and it has become an absolute necessity in the modern world. We use AI in our daily life without even realizing that we are using it.

Artificial Intelligence is a technology which enables machines to think and behave like humans do. It is a broader term which encompasses Machine Learning, Deep Learning and their subfields.

What is Machine Learning?

Machine learning is a subfield of Artificial Intelligence which allows the machines to learn from data and make smart decisions or predictions without being explicitly programmed. So the first question that comes to mind is how do the machines learn. Obviously, there has to be a way to teach the machines so that they can learn. Machines are basically trained on massive amounts of data through which they learn and understand the patterns. They keep on learning and improving themselves as more data is fed into them. They can ultimately apply those learned patterns to make future predictions. The training is done using different algorithms which help in creating machine learning models. These models enable machines to perform intelligent tasks like predicting outcomes and classifying information.

Traditionally, we program a computer by giving a specific set of instructions to perform and accomplish a task. In machine learning, we provide the computer with data and a task to perform and it learns to accomplish the task using the data. Here's an example. Let's say we want the computer to recognize the images of birds. We won't provide it with the instructions of how a bird looks like. Instead we'll provide it with hundreds or thousands of images of birds and let the machine learning algorithm figure out the features and patterns that define a bird. As it gets more bird images to train itself, it gets better and can work very well on unseen images of birds.

Here is a bigger picture of what machine learning looks like. Data is given to the computer which learns the patterns in that data and creates a model. In machine learning, a model refers to the specific representation learned from data based on which predictions or decisions are made. That model is then used to make predictions using unseen data.

Applications of Machine Learning

Machine learning has a lot of applications in the real world.

• Recommendation Systems - Have you thought how you get those recommendations on any e-commerce website to buy so and so products. It's all based on machine learning where the system learns users' behaviors and buying patterns and recommends the products of their interest. Similarly, whenever you watch something on YouTube, it starts recommending videos from the same niche that might interest you.
• Disease Prediction - Machine learning can be used to learn patterns through historical data of patients with chronic diseases and then predict the possibility of diseases like heart failure, cancer, meningitis and many others in any patient with specific symptoms.
• Fraud Detection - Banks nowadays heavily rely on machine learning algorithms to predict fraudulent transactions and prevent them before hand to protect their customers from monetary losses.
• Image Recognition - Computer vision relies on deep learning algorithms for object detection in images, for example, face detection.
• Social Media - Social media platforms use machine learning algorithms to learn patterns from a person's social media usage, likes and dislikes and give personalized suggestions based on his/her own experience.
• Text Generation - Who hasn't heard of ChatGPT? It is a powerful conversational chatbot developed by OpenAI which generates meaningful automated conversations based on its machine learning capabilities.

Importance of Machine Learning in Today's World

It is a well-known saying that data is the new oil and AI is the new electricity. As electricity revolutionized transportation, health, and communication to name a few, machine learning is revolutionizing the modern world in each and every domain such as education, health, finance, business, and many more.

We have a lot of data around us and huge amounts of real-time data is being generated every second. Traditional methods are not sufficient to deal with such massive amounts of data. Machine learning algorithms can handle big data very easily, process it efficiently and uncover hidden insights and patterns from it. It helps to automate data analysis tasks so that humans can work on more complex and creative problems.

Prerequisites of Machine Learning

Data is at the core of any machine learning algorithm. The more data you have, your machine learning algorithm would be trained better and would give accurate predictions.

In the context of machine learning, we divide the data into two broad categories, labeled and unlabeled. When you have certain features in your training data and you also know the value of what you want to predict based on those features i.e. target variable, it's called labeled data. When you don't know the value of the target variable based on certain features, it's called unlabeled data. In that case, the machine learning algorithm will group the data based on certain characteristics and assign labels itself.

Secondly, it is extremely important to preprocess the data before feeding it into the machine learning algorithm. Exploratory data analysis can help in identifying duplicates, missing values, outliers and inconsistencies in the data. Data must be cleaned and standardized. Also, machine learning algorithms require the data to be in a numeric format, therefore, it is necessary to encode the categorical data before applying any machine learning algorithm on it. All these preprocessing steps help ensure accurate predictions.

There are different machine learning algorithms for different types of problems and each algorithm has its own set of assumptions. These assumptions must be met before applying the algorithm to any dataset, otherwise the predictions or results won't be accurate.

Types of Machine Learning

There are four main types of machine learning.

1. Supervised Machine Learning
2. Unsupervised Machine Learning
3. Semi-supervised Machine Learning
4. Reinforcement Learning

1. Supervised Machine Learning

Supervised machine learning is the most widely used type of machine learning. This type of machine learning is applied when you have labeled data. There are two main types of problems which can be solved through supervised machine learning:

1. Classification
2. Regression

Classification is used when your target variable is discrete, i.e. when you have to identify the class given a certain set of features. It is usually used for categorical data. For example, classifying the animals as cats or dogs, or predicting customer churn behavior.

Regression is used when you have to predict a continuous variable. It is applied when your target variable is numerical. For example, predicting the price of a product or predicting medical expenses of a patient.

The model learns a mapping between the features and the target variable (label) during the training process.

2. Unsupervised Machine Learning

Unsupervised machine learning is applied when you have unlabeled data. You have certain set of features but you don't know their classes or values. In this case, unsupervised machine learning algorithms learn those features and assign a class or value to each set of features. Most widely used type of unsupervised machine learning is clustering.

Clustering divides the data points into different groups or clusters according to the similarities in their characteristics. For example, customer segmentation based on their purchasing behavior or image segmentation in healthcare systems.

The model learns the patterns and relationships between the data by itself.

3. Semi-supervised Machine Learning

Semi-supervised machine learning is a combination of both supervised and unsupervised machine learning. It has labeled as well as unlabeled data. It can be useful when there is a small amount of labeled data and a large amount of unlabeled data. For example, text classification or image classification.

4. Reinforcement Learning

Reinforcement learning is learning by doing and observing. The machine learns by interacting with the environment. It is based on the principle of reward and punishment where the model learns from its mistakes and its goal is to maximize the reward. This type of machine learning is used in robotics and scenarios like playing games.

Machine Learning Tools

There are a variety of machine learning tools available:

• Programming languages like R and Python
• Libraries including scikit-learn, TensorFlow, Keras, PyTorch, and NLTK

How to approach a Machine Learning problem?

The first and foremost step to solve a machine learning problem is to identify your problem and look at your target variable on the basis of which you would select your machine learning algorithm.

• If your target variable is discrete, then it's a classification problem in supervised machine learning.
• If your target variable is continuous, then it's a regression problem in supervised machine learning.
• If you don't have a target variable, then it's an unsupervised learning problem which might be solved through clustering.

Basic Workflow to Solve a Machine Learning Problem

1. Define the problem.
2. Collect the data ensuring its quality and integrity.
3. Preprocess the data utilizing the practices of exploratory data analysis (EDA). Remove any errors, anomalies, and inconsistencies in the data. Handle duplicates, missing values, and outliers. Standardize the data and encode the categorical features.
4. Choose your features and target variable. Features are usually called X and target variable is called y.
5. Choose the machine learning model based on your type of data and the target variable. If the data type of target variable is categorical, choose a classification algorithm, if the data type of target variable is numeric, choose a regression algorithm, and if there is no target variable, choose a clustering algorithm.
6. Divide your data into training and test sets. Usually an 80-20 ratio is maintained where 80% data is kept for training and 20% for testing purposes. But this can be varied depending on the specific problem.
7. Train your model on the training dataset.
8. Choose an evaluation metric based on the problem you are trying to solve. Metrics are used to calculate the difference between the actual (test) and predicted values. Usually accuracy, precision, recall, F1 score, and AUC ROC are used as metrics for a classification problem and R2, RMSE, and MSE are used as metrics for a regression problem.
9. Evaluate the performance of your model using the testing dataset.
10. Perform hyperparameter tuning and cross-validation to improve the performance of your model. There are different hyperparameters for every model and you can either tune them randomly or perform an extensive grid search to find out the best hyperparameters for a given scenario.
11. Finalize the model and make predictions on unseen data using your fine-tuned machine learning model.
12. Deploy the model in the form of an app, a web app or a software.

Machine Learning Algorithms

Following are some of the algorithms for different types of machine learning:

Supervised Machine Learning Algorithms

1. Linear Regression
2. Logistic Regression
3. K-Nearest Neighbors (KNN)
4. Support Vector Machines (SVM)
5. Naive Bayes
6. Decision Tree
7. Random Forest

Unsupervised Machine Learning Algorithms

1. K-Means Clustering
2. Hierarchical Clustering
3. Probabilistic Clustering
4. Principal Component Analysis (PCA)

Semi-supervised Machine Learning Algorithms

1. Self-training
2. Co-training
3. Graph-based Methods

Reinforcement Learning Algorithms

1. Q-learning
2. Policy Gradient Methods

Key Takeaways

Machine learning is a subfield of Artificial Intelligence which enables computers to learn patterns through huge amounts of data, create models to represent those patterns and make predictions using those models. Two major types of machine learning are supervised and unsupervised learning. Other types include semi-supervised and reinforcement learning. Supervised machine learning can be further classified into regression and classification whereas clustering is the most common type of unsupervised machine learning. There are different algorithms for each type of machine learning. Data must be preprocessed before feeding it into a machine learning algorithm and model assumptions must be met in order to get accurate predictions. Some common applications of machine learning in real-world include recommendation systems, disease prediction, and fraud detection.

References

https://www.coursera.org/articles/what-is-machine-learning

https://www.datacamp.com/blog/what-is-machine-learning

https://www.datacamp.com/blog/a-beginner-s-guide-to-the-machine-learning-workflow

https://www.ibm.com/topics/machine-learning

https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML