The world is filled with data. Lots and lots of data. Everything from pictures, music, words, spreadsheets, videos and more. It doesn’t look like it’s going to slow down anytime soon. Machine learning brings the promise of deriving meaning from all of that data.
In this series, I want to take you on an adventure through the world of AI, to explore the art, science, and tools of machine learning. Along the way, we’ll see just how easy it is to create amazing experiences and yield valuable insights.
1. Introduction
Machine learning is a sub-field of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
For the process of learning (model fitting) we need to have some observations or data (also known as samples or examples) in order to explore potential underlying patterns, hidden in our data. These learned patterns are nothing more that some functions or decision boundaries.
These patterns are learned by the systems (computer systems) automatically without human intervention or input.
2. Machine learning finds patterns in data
Simply, machine learning finds patterns in data and uses them to make predictions.
Whenever you have large amounts of data and want to automate smart predictions, machine learning could be the right tool to use.
3. Machine Learning is already everywhere
We see machine learning all around us in the products we use today, but it isn’t always apparent to us that machine learning is behind it all. While tagging objects and people in pictures is clearly machine learning, you may not realize that features like video recommendation systems are also often powered by machine learning.
Of course, perhaps the biggest example of all is Google Search. Every time you use Google Search, you are using a system that has many machine learning systems at its core, from understanding the text of your query to adjusting the results based on your personal interests. When you search for “Java”, machine learning determines which results to show first, depending on whether it thinks you are a coffee expert or a developer. Perhaps you’re both!
Today, machine learning’s immediate applications are already quite wide-ranging, including image recognition, fraud detection, recommendation engines, as well as text and speech systems. These powerful capabilities can be applied to a wide range of fields, from diabetic retinopathy and skin cancer detection to retail, and of course transportation, in the form of self-parking and self-driving vehicles.
4. Types of Machine Learning
Depending on the problem we want to solve and the data we’ve got access to, we also need to make sure to pick a suitable learning algorithm. Machine Learning comes with numerous learning types that can be used in different contexts to provide answers of different nature as needed. These are:
- Supervised Learning: In supervised learning, the dataset includes labelled examples. In other words, the set contains the correct responses (known as targets) of each example. The goal is to develop a model that takes as input a feature vector and outputs a predicted target variable.
- Unsupervised Learning: On the other hand, in unsupervised learning the dataset is a collection of unlabelled examples. This learning type is mostly useful when we want to perform clustering, dimensionality reduction or outlier detection.
- Semi-supervised Learning: When a dataset contains both labelled and unlabelled examples we may need to apply a semi-supervised learning algorithm.
- Reinforcement Learning: This type of learning is mostly suitable when the learning process is “sequential”. In reinforcement learning, the algorithm usually gets instructed when the predicted answer is wrong but it does not get told how to correct it.
5. What are the necessary conditions for successful Machine Learning?
Machine Learning and ‘Big Data’ has become more well-known and has generated a lot of press in recent years. As a result, many individuals and organizations are considering how and if it might apply to their specific situation and whether there is value to be gained from it.
However, building internal capabilities for successful Machine Learning (or making use of external expertise) can be costly. Before taking on this challenge, it is wise to assess whether the right conditions exist for the organization to have a chance of success. The main considerations here relate to data and to human insight.
There are three important data requirements for effective Machine Learning. Often, not all of these requirements can be satisfactorily met, and shortcomings in one can sometimes be offset by one or both of the others. These requirements are:
- Quantity: Machine Learning algorithms need a large number of examples in order to provide the most reliable results. Most training sets for supervised learning will involve thousands, or tens of thousands of examples.
- Variability: Machine Learning aims to observe similarities and differences in data. If data is too similar (or too random), it will not be able to effectively learn from it. In classification learning, for example, the number of examples of each class in the training data is critical to the chances of success.
- Dimensionality: Machine Learning problems often operate in multidimensional space, with each dimension associated with a certain input variable. The greater the amount of missing information in the data, the greater the amount of empty space which prevents learning. Therefore, the level of completeness of the data is an important factor in the success of the learning process.
Final Thoughts
In today’s article, we introduced Machine Learning which is definitely among the fields of Computer Science that have seen great advancement in the last few years. Finally, we discussed how machines are capable of learning as well as the different learning types that exist. Stay tuned for further posts in this series.
If you like this post, then check out my other posts in this series about
1. What are the types of Machine Learning?
2. Uni-Variate Linear Regression
3. Multi-Variate Linear Regression
4. Logistic Regression
5. What are Neural Networks?
6. Digit Classifier using Neural Networks
7. Image Compressing with K-means Clustering
8. Dimensionality Reduction on Face using PCA
9. Detect Failing Servers on a Network using Anomaly Detection
Last Thing
If you enjoyed my article, a clap 👏 and a follow would be fantastic and it is helpful for medium to promote this article so that others may read it. I am Jagajith and I will catch you in the next one.
