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Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction
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Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction

Last Updated on February 20, 2024 by Editorial Team

Author(s): Vaishnavi Seetharama

Originally published on Towards AI.

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction

Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online. Machine learning(ML) is evolving at a very fast pace. You need to be on your toes to get to know the real trends or innovations in the field of ML. I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’.

Photo by Andrea De Santis on Unsplash

So, What is Machine Learning?

Definition says, machine learning is the ability of computers to learn without explicit programming. Instead of being told how to perform a task, they learn from data and improve their performance over time. This ability empowers them to identify patterns, make predictions, and even generate creative content.

Below is a very common example you will come across:

Imagine teaching a child about a cat. You show many cats to the child. It learns by seeing different features such as eyes, nose, ears, tails, sitting style, sound, etc. Then the child learns it.

Similarly, you give a Machine learning model many pictures, videos, Sounds, etc., labeled as Cat. The computer model analyses different features with the label. Then it can classify unseen or new data.

Types of Machine Learning

There are three main categories of Machine Learning, Supervised learning, Unsupervised learning, and Reinforcement learning.

Supervised learning:

This involves learning from labeled data, where each data point has a known outcome. For example, training a computer to identify spam emails by feeding it labeled emails as spam and not spam.

Unsupervised learning:

This involves learning from unlabeled data, where the computer has to identify patterns and relationships on its own. For example, grouping customers into different segments based on their purchase history.

Reinforcement learning:

This involves learning through trial and error. The computer interacts with an environment, receives feedback, and learns to take actions that maximize its reward—for example, training a computer to play a game by rewarding it for winning and penalizing it for losing.

How Machine Learning Works?

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets:

Collection of Data:

Collection of Relevant data is key for building a Machine learning model. It isn't easy to collect a good amount of quality data. Quality data is very important to improve the accuracy of the model, or it may lead to misinformation or bias.

You need to know two basic terminologies here, Features and Labels. Features are the individual, measurable properties or characteristics of the input data that the machine learning model uses to make predictions or classifications (e.g., Cat’s pictures, Sounds, etc.). Labels, also known as target variables, are the outcomes or results that the machine learning model aims to predict or classify based on the input features (e.g., Cat as a label, in our example).

Photo by Carlos Muza on Unsplash

Training a Model:

This step is where you build a machine-learning model. First, you need to select an appropriate algorithm for the task you are focusing on. Then You need to initialize the parameters of the model (e.g., Slope, intercept in case of linear regression). Calculate the errors in predictions, update the parameters, then repeat the process till you have an accurate model.

Testing and Evaluation:

Testing machine learning models on new, unseen data is a critical step in the model development process. This practice is essential for assessing how well a model generalizes to real-world scenarios and unseen examples.

Models should not only memorize the training data but also understand the underlying patterns. Testing helps identify whether a model has overfitted (memorized the training data but performs poorly on new data) or underfitted (failed to capture the underlying patterns). Testing provides a basis for evaluating the model’s performance using various metrics, allowing you to assess how well the model meets the specific goals of the task.

Common Machine Learning Algorithms

Machine learning algorithms are not limited to those mentioned below, but these are a few which are very common.

  1. Linear Regression
  2. Decision Trees
  3. Support Vector Machines
  4. Neural Networks
  5. Clustering Algorithms (e.g., K-means)

Applications of Machine Learning

Here are just a few of the countless applications of machine learning:

  • Face recognition: Used to unlock smartphones, tag people in photos, and even track criminals.
  • Speech recognition: Enables voice assistants like Siri and Alexa to understand our spoken words.
  • Natural language processing: Helps computers understand and generate human language, powering chatbots, and machine translation.
  • Recommender systems: Suggesting products or content you might like based on your past behavior.
  • Fraud detection: Identifying suspicious activity in financial transactions.
  • Medical diagnosis: Assisting doctors in diagnosing diseases by analyzing medical images and data.

Challenges and Limitations

Like any other field, you may find a few challenges on your path. A few of them are Overfitting, Underfitting, Curse of Dimensionality, etc.

Overfitting occurs when a model learns the training data too well, capturing noise and outliers rather than the underlying patterns.

Underfitting happens when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both the training and new data.

The curse of dimensionality refers to the challenges that arise when working with high-dimensional data. As the number of features increases, the amount of data required to generalize accurately grows exponentially.

Apart from the above, some of the Ethical Concerns and Biases are:

  • If training data is biased, the model can inherit and perpetuate these biases, leading to unfair or discriminatory outcomes.
  • Many machine learning models, especially complex ones like deep neural networks, can be challenging to interpret and explain, leading to concerns about accountability and transparency.
  • Models trained on sensitive data may inadvertently leak private information.
  • Models can be vulnerable to manipulation by intentionally crafted input data.
  • Regularly monitor model performance, detect biases, and adjust models.
  • Adopt ethical AI principles, involve diverse teams in model development, and consider the broader societal impact of deploying machine learning systems.

Why should you learn about machine learning?

Machine learning is a rapidly growing field, offering exciting opportunities for individuals and businesses alike. Here are just a few reasons to start learning today:

  • High demand: The demand for machine learning professionals is skyrocketing, with companies actively seeking skilled individuals.
  • High salaries: Machine learning careers offer some of the highest salaries in the tech industry.
  • Impactful work: You can use your skills to solve real-world problems and make a positive impact on society.
  • Continuous learning: This field is constantly evolving, ensuring a never-ending learning journey.
  • Personal growth: Learning machine learning will sharpen your analytical and problem-solving skills.

This blog series will serve as your roadmap to mastering this fascinating field. Let’s delve deeper into each type of machine learning, explore popular algorithms, and even build hands-on projects. By the end of this journey, you’ll have a solid understanding of how machine learning works and how you can use it to solve real-world problems.

Thanks for reading.

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