Machine learning (ML) is an interesting area of study that utilises computational methods, statistical analysis and domain knowledge to build systems that are capable of learning from the data and can make predictions or decisions based on it. People of every age and kind of a profile, from students to professionals and avid tech lovers, can benefit from a basic grasp of what ML is. This guide will simply orient you to the basics and guide you on your journey in becoming a principles-driven learner.
The topics covered in this article are:
- What is Machine Learning?
- Types of Machine Learning
- Steps to Getting Started with Machine Learning
- Tools and Libraries
What is Machine Learning ?
In simpler words, machine learning is a subfield of artificial intelligence, which is further defined as the capability of machine to imitate the human behaviour i.e., learning on their own.
Here, machines learns from the hidden patterns within datasets, which helps them in making predictions.
You can see numerous examples of machine learning around you, for instance take example of email spam filtering, email services use machine learning to filter out spam emails. They collect a large dataset of emails labeled as “spam” or “not spam” and extract features such as email content, sender information, and the presence of links.
Types of Machine Learning
There are three ways to use the technology of machine learning depending upon the need of the business:
Supervised Learning
Training the algorithm using the labeled input and output data. i.e., teaching the machine what to learn.
Unsupervised Learning
Training the algorithm with no labeled data. i.e., machine will automatically find what to learn.
Reinforcement Learning
Algorithm takes actions to max cumulative reward. i.e., machine will learn from it’s own mistake at every step.
Now focusing on each one of these methods in a broader way:
1.Supervised Learning
Supervised learning can also be understood as a process that is quite similar to explaining to a child what fruits are and showing them specific examples of apples, bananas, and oranges they should focus on. The child is trained to relate the distinguishing features such as colour and shape to each of the fruits. At a later time, the child can classify or name new fruits on the basis of the learned association. Likewise, in the supervised learning model, the prediction of the labels entails utilising data that has already been labeled to train a model and afterward label other unseen data.
Real-life examples:
Email Spam (Classification)– The algorithm takes historical spam and non-spam emails as input. Consequently, it draws patterns in data to classify spam from others.
Stock Price Prediction (Regression)– Historical business market data is fed to the algorithm in this method. With proper regression analysis, the new price for the future is predicted.
2.Unsupervised Learning
Unsupervised learning is like giving a child a mix of different fruits without telling them the names. The child groups similar-looking fruits together based on their features like color and shape. Similarly, in unsupervised learning, a model identifies patterns and clusters in data without predefined labels.
For Example:
Data with similar traits are asked to group by the algorithm. These groups are called clusters, and the process is called clustering. In retail analytics, various customers are usually clustered based on their purchase and other behaviours.
3.Reinforcement Learning
Reinforcement learning is like teaching a dog new tricks through trial and error. The dog receives rewards for performing desired actions and learns to maximise its rewards over time through exploration and feedback. Similarly, in reinforcement learning, an agent learns to make decisions in an environment to maximise a cumulative reward.
For Example:
An exciting example of reinforcement learning occurs when computers learn to play video games by themselves. The algorithm keeps on interacting with the game environment through a series of actions. This environment, in turn, gives a reward or punishment based on the nature of action taken.
Steps to Getting Started with Machine Learning
Step 1 : Collecting Data
Machines initially learn from the data so, it is very important to collect reliable data so that machine learning model can find the correct patterns. The quality of data feed to the machine will decide the accuracy of the model. If the data will be outdated or full of errors prediction will be wrong.
Step 2: Preparing the Data
After getting all the data we prepare it, first, shuffle the data to ensure even distribution and eliminate order bias. Next, clean the data by removing unwanted entries, handling missing values, eliminating duplicates, and converting data types as needed, which may involve restructuring rows and columns. Then, visualise the data to understand its structure and the relationships between variables. Finally, split the cleaned data into a training set for the model to learn from and a testing set to evaluate the model’s accuracy.
Step 3: Choosing a Model
A machine learning model determines the output we get after running the machine learning algorithm on the collected data. We choose the relevant model for that according to our need. Over the time lots of machine learning models are derived about which we will learn further in this series.
Step 4: Training the Model
This is the most important step in the process of machine learning in this step we pass the prepared data to our machine learning model to find the patterns and make predictions. It results in the model learning from the data so that it can accomplish the task set. Over time, with training, the model gets better at predicting.
Step 5: Evaluating the Model
After training of our model it is important to check that how our model is performing on unseen data because if we use the same data used for the testing the result will not be accurate as model is familiar with the data.
Step 6: Parameter Tuning
Parameter tuning is done after training and evaluating our model to check if there is any scope in improving the accuracy of our model. Parameters are the variables in the model that the programmer generally decides.
Step 7: Deploy the Model
Now we can deploy our model for practical use, such as web application or mobile app.
Tools and Libraries
Programming Languages :
Python: Widely used for implementing machine learning because of its readability and extensive library support.
R: R is very popular in implementation of statistical modelling and data analysis.
Libraries:
Scikit-Learn: It provides simple and efficient tools for data mining and data analysis.
TensorFlow: This is an open-source platform for machine learning used particularly deep learning.
Keras: This is a high-level neural networks API, running on top of TensorFlow.
Pandas: Pandas is useful for data manipulation and analysis.
NumPy: It supports large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions.
Machine learning is a rapidly growing field with a vast array of applications. Starting with the basics and gradually exploring more advanced topics can set you on a path to becoming proficient in this exciting domain. Whether you’re looking to apply ML to solve practical problems or aiming for a career in data science, the journey begins with a solid understanding of the fundamentals.
Happy Learning !
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