Machine Learning comes under the umbrella of Artificial Intelligence (AI). It focuses on repetitive tasks and allows users to automate processes without human intervention.
We know that Artificial Intelligence has been one of the most revolutionary technological developments in recent history. But how does Machine Learning fit into all this?
We have curated this article to explain what is machine learning and how it’s going to shape our future in a variety of ways. Let’s get started..!!
What is Machine Learning?
Machine learning is a concept that computers can learn and adapt to new data without being explicitly programmed. It’s a subfield of artificial intelligence that can be applied to many areas such as advertising, investing, fraud detection, statistical analysis, etc.
You might know that we have a lot of big data being generated every single day. Machine learning helps us to make sense of that data while working for the betterment of our lives. Let’s take the example of credit cards and try to understand them better.
Suppose, if your credit card gets stolen, you would get an email or text from your bank verifying the transaction for fraudulent behaviors. This is simply because the bank’s algorithm knows your credit card’s purchasing habits, and performs necessary actions. This is one of the many instances where ML is used in our daily life.
Processes Involved in Machine Learning
The entire process of Machine Learning can be broken down into three main steps. These processes include:
1. Decision Process
The majority of the time, ML algorithms are responsible for decision-making based on predictions and classifications. Based on the input data, ML can produce an estimated pattern in the data.
2. Error Function
An error function in ML is used to evaluate the prediction of a certain model. For similar error examples, the function can make a model based on the most accurate one.
3. Model Optimization Process
For models that can fit in the data points in the training set, this can reduce discrepancies to a certain degree by adjusting the weights. This repetitive process can optimize any model to its utmost accuracy.
Types of Machine Learning
Machine Learning can be further classified into 3 different categories:
1. Supervised Machine Learning
In Supervised Machine Learning, labeled datasets are used to train algorithms for predicting outcomes with utmost accuracy. Once the input data is fed to the model, the weight gets adjusted accordingly.
This is a fairly common occurrence for the cross-validation process to avoid any overfitting and underfitting of the data model. Supervised Machine Learning can help organizations solve a plethora of real-world problems at scale.
One such example is classifying the spam in a separate folder within your inbox. Other use cases of supervised ML can be seen in neural networks, linear and logistic regression, support vector machine (SVM), random forests, and more.
2. Unsupervised Machine Learning
Unsupervised Machine Learning algorithms are primarily used for the analysis of clustered unlabeled data. Without any human intervention whatsoever, unsupervised ML algorithms can discover hidden patterns or data grouping.
Its ability to figure out the similarities and differences in datasets makes unsupervised ML algorithms the perfect fit for cross-selling strategies, exploratory data analysis, image and pattern recognition as well as customer segmentation.
Some of the other algorithms you can find in unsupervised ML are neural networks, probabilistic clustering methods, k-means clustering, and more.
3. Semi-Supervised Machine Learning
Lastly, semi-supervised ML offers the perfect mix between supervised and unsupervised Machine Learning algorithms. While training models, semi-supervised requires smaller labeled data sets that can guide through the entire classification process.
Semi-supervised learning can also mitigate the problem that comes from working with contained labeled data (or lack of enough labeled data) while training any supervised ML model.
Real-World Applications of Machine Learning
Even when you encounter Machine Learning in your daily lives, chances are you might not be able to point out its occurrence. Here is a list of some of the prominent examples of ML:
1. Speech Recognition
Also commonly known as Automatic Speech Recognition (ASR) or simply speech-to-text is one of the best applications of ML using natural language processing (NLP) to process human speech into writings. Every smartphone is equipped with some sort of speech recognition or another, for example, Siri or Google Assistant for conducting voice search.
2. Customer Service
With the majority of the businesses going online, offering state-of-the-art customer satisfaction is a must-have. This is where online chatbots are replacing human agents from the customer’s shopping experience.
Be it answering Frequently Asked Questions (FAQs), offering personalized advice, or cross-selling products, ML has become an integral part of e-commerce websites. The use of virtual assistants has become fairly common among messaging apps such as Slack and Facebook as well.
3. Computer Vision
Driven by AI technology, computers, and other systems are able to derive meaningful information from digital photos, videos, and other visual elements to take actions accordingly.
Machine Learning backed by neural networks allows us to use computer vision for photo tagging in various social media platforms, self-driving cars in the automobile industry as well as radiology imaging in the healthcare sector.
4. Recommendation Engines
Making the best use of the previous behavioral data, AI and ML algorithms can assist in discovering data trends. This model is further used as an effective marketing strategy for personalized product recommendations. The same algorithm can also be used to optimize stock portfolios for optimum growth.
Challenges in Machine Learning
Even with all the bells and whistles of Machine Learning, it still comes with its own setbacks. Some of the challenges and ethical concerns surrounding ML includes:
- Technological Singularity: In the unforeseeable future, there is going to be a time where AI can surpass human intelligence. In case of any mishappening, for example, a self-driving car getting into an accident. Under such circumstances who is to blame? These are some of the few ethical debates that revolve around AI and ML.
- AI and ML’s Impact on Job: This goes without saying, with the majority of the repetitive tasks getting autonomous without human intervention, it can be highly disruptive towards the job market. That is why it is very important for individuals to transition to other areas of the job market.
- Privacy: Data privacy and protection seem to be another pressing matter when it comes to AI and ML. With everything going online including the sensitive information of an individual, it can be vulnerable to hacking and other cyberattacks.
Wrapping Up: Machine Learning
Machine Learning continues to become one of the primary driving factors for making accurate predictions, forecasting, AI speech recognition, identifying images correctly as well as building chatbots.
In order to automate processes, ML surely plays a pivotal role. In the coming years, we can also expect other aspiring startups in the field of Machine Learning. So, let us know in the comment section below, what are your thoughts on the future of Machine Learning?
