A Beginners Guide to Machine Learning

Machine learning (ML) is a branch of artificial intelligence, where a computer is trained using data and algorithms to help us understand complex concepts and make better decisions.

ML uses data to train algorithms that learn to identify patterns, which can then be used to make informed decisions. It is an iterative process that allows machines to continuously refine their understanding, adapt to new data, and improve decision-making accuracy, making machine learning an indispensable tool in modern technology and problem-solving.

Types of Machine Learning

Supervised Learning: In supervised learning, the computer learns from labelled data. Imagine you want to train a computer to tell cats from dogs. During training, you provide the computer with pictures of both cats and dogs (the input) along with labels indicating which animal is in each photo (the output).

The computer learns by associating the images with the correct labels. Once trained, you can test it with new, unlabelled photos, and it will predict whether it’s a cat or a dog. Supervised learning is all about teaching the computer to make these predictions based on what it learned from the labelled data.

Unsupervised learning: Unsupervised learning is like letting a computer explore data without labels. In unsupervised learning, there are no “right answers” provided during training. The computer discovers patterns and structures within the data, and based on those patterns it will generate insights, identify clusters, or uncover hidden relationships among the data points.

Reinforcement learning: In reinforcement learning, the computer learns through trial and error. The computer explores different actions and learns which ones lead to the best rewards. Over time, it becomes skilled at making decisions to maximize rewards.

Example: Settle/Default on Loans

Let’s examine a real-world scenario of a supervised learning problem. Before a bank gives out a loan, they must be able to assess whether the individual will be at risk of defaulting on the loan. We can build a machine learning model that will be able to predict whether someone will settle or default on their loan.

For this problem you will need data on previous individuals who have taken out a loan, such as their age, occupation, salary, the size of the loan they took out, and whether they settled or defaulted on that loan. The model will consider all this information to identify specific patterns that could lead someone to default. Once the model is trained, it can be used during a loan application process to assess whether a new applicant is likely to default on their loan.

The Learning Process

Step 1: Data Collection – ML algorithms need a diverse and representative dataset to learn from.

Step 2: Data Preprocessing – Data must be cleaned, this includes removing irrelevant information, dealing with missing values, and converting the data into a format that is suitable for the model. This step is very important as bad data going into the model will result in bad data coming from the model.

Step 3: Feature Extraction – Features are the characteristics of the data that the model will use to make predictions, such as age, occupation, and salary in the example above.

Step 4: Model Selection – There is a wide selection of algorithms that can be used, different algorithms will be suited better to different tasks.

Step 5: Training – During the training, the model will adjust its internal settings to enable it to learn most effectively from the data. This is an iterative process that is continued until a satisfactory level of accuracy is reached.

Step 6: Testing and Validation – Now the model can be tested on data It has not seen before to assess its performance. The accuracy of the model can be evaluated by a range of metrics.

In conclusion, machine learning is like teaching a computer to learn from examples and make intelligent decisions. With supervised, unsupervised, and reinforcement learning, it adapts to different scenarios.

By understanding its learning process and diving into real-world examples like predicting loan defaults, you’ve taken your first steps into the fascinating world of machine learning. As you explore further, you’ll uncover its depth and potential to transform industries and our daily lives.

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