Want to become a Machine Learning Engineer? Wondering what exactly is Machine Learning and what it is used for?
Few developers have been using machine learning for quite a long period of time in their products & for their business applications.
However, this was largely unknown by the rest of the world. It wasn't until recently that the question, “What is machine learning and artificial intelligence?” started to appear in public discourse, and affect our lives in a significant way.
Machine learning has taken the world by storm in the last decade, transforming businesses and the day-to-day lives of people.
As it becomes increasingly present in our real lives, we will look into the key questions about the field:
- What is machine learning with examples?
- How does machine learning work?
- Machine Learning Types
- What is the recommended programming language for machine learning?
- What are the top 5 real world machine learning use cases?
What is Machine Learning with examples?
- Machine learning Meaning : Machine learning is a branch of artificial intelligence that delivers computers the ability to make decisions without being explicitly programmed for each new situation. It gives computers the ability to learn from data, identify patterns, and make predictions.
- What is machine learning in simple words? Machine Learning functions much like a human brain.
If you give an apple to a baby and teach them, "This is an apple", the baby's brain will try to register this by recognizing when you show it another apple. Repeating this process 20 times helps babies learn that apples have certain characteristics, like being red, round, and having a brown stem.
Like the human mind, machine learning algorithms parse through and analyze huge amounts of historical data to identify patterns—gathering valuable insights in the process. Using those insights, the machine learning algorithms will predict the outcome of similar cases.
- How does it differ from a non-machine learning program? In a non-machine-learning program, you instruct the computer to make certain decisions based on predetermined factors. For example, if an object is red and spherical in shape, then it's probably an apple.
This is how the code would look like:
If object == red and object == spherical:
print(“object is apple”)
However, when your program encounters a green apple—that is, an item that does not meet the criteria of its definition for “apple”—then it will fail to identify this fruit as such.
In machine learning, this is not the case. If you have trained a machine learning program with millions of data points, then it will draw from that pool when making future predictions—including saying 'green apple' even though there are no green apples in its past training set!
Now it can easily recognize a green apple, even if it has never seen that particular type before.
Here is a summary of how a machine learning program differs from a non-machine learning program.
Let's see how machine learning algorithms can make decisions without having been told explicitly what rules to follow. We will examine how it learns from historical data.
How Does Machine Learning Work?
In our apple analogy, when you show an apple to a baby, the human brain records specific features of that object. For instance, it records features such as “red” in color, “spherical” in shape, and has a “smooth” texture.
Once the object is converted to a set of features, it labels it as an “apple.”
The next time you show it even a very different variety of apple, the mind will be able to correlate the features with the new object and will be able to recognize that it is an apple.
This is the same way machine learning algorithms work. They use a set of training data to learn about the relationship between variables.
Machine learning models, which are trained algorithm expressions, will be able to make decisions on a new set of data by correlating the features with those of the past data.
You have then created a system that makes decisions for new sets of information.
Machine Learning Types
What are the types of Machine Learning ? The field can be divided into many different types, each with their own strengths and weaknesses. All can be categorized into three main kinds of machine-learning techniques:
- Supervised learning,
- Unsupervised learning and
- Reinforcement learning.
What is supervised learning in machine learning? In supervised learning, the algorithm learns through machine learning examples. The examples must be known data composed of an input and an output.
For e.g., the input in our apple analogy could be the name of the fruit, and the output will be a boolean value of either “true” or “false,” indicating whether the object is an apple or not.
With supervised learning, the algorithm will analyze millions of pieces of such data.
Based on its learning, the system can predict whether a new fruit is an apple or not.
Here's a real world example where this method is applied. Do you have auto insurance? Have you filed a claim? If you hadn’t faced a need to file a claim it is well and good.
"Do you know around 10% of insurance claims are fraudulent claims?"
This means if the policy holder claims for an accident which is not actually covered by the policy coverage then it is a fraud claim.
If you are head of the fraud claims department in an insurance organization, you would have been asked to find the potential fraud claims, so that they can be carefully evaluated before processing the claim.
As head of fraud claims, if you were trying to learn about the relationships between fraud claims and the policy holder information, you might provide the algorithm with 1000 cases of customers who made fraud claims in the past and another 1000 customers who didn’t.
The labeled data supervises the machine learning algorithm to figure out the pattern you are looking for.
"If the algorithm learns from the past historical labeled data, then it is supervised learning"
Supervised learning is used in fraud claim identification, loan defaulter prediction, sales forecasting, etc.
One exciting innovation where it's utilized heavily is Face Recognition.
Do you use Google photos? Then Google's face recognition system will be able to identify you in future photos. You also see this in apps like Facebook. The program learns from your photos and photos where you've been tagged so they can suggest to other users to tag you in future images.
Within supervised learning there are 2 primary types,
- Classification and
In classification tasks, the algorithm determines whether something fits in a certain class. Results may show “yes” or “no.” In certain other cases, it determines whether it falls into classA, classB, or classC. In other words, classification is basically the process of classifying data into one of the many predetermined categories.
For example, many email providers, like Gmail, automatically place some emails in the Spam folder. Identifying whether an email is "spam" or "not spam" is an example of classification task.
Below is another example of a classification task where countries are classified as whether they fall under a low-income or a high income category.
In regression tasks, algorithms are used to predict the value of new data points by looking at similar existing data.
For example, predicting a country's happiness score based on its attributes (e.g., freedom from corruption) is one way you might use regression in practice.
"To sum it up, if your algorithm predicts whether a country belongs to a high income country or low-income one—that's classification, labeling them a predetermined category. However, if you're trying to find a happiness score which in this case is a value between 1 to 10, then it's regression"
What is unsupervised machine learning? It is a method for data analysis in which you don't provide the algorithm any information about the data set. Instead, the algorithm tries to organize the data in a structure that makes sense.
For example, let's say that an algorithm doesn't have any past training data and it's able to find some natural correlations and relationships between apples, oranges, and peaches in terms of size, texture, and color. If it's also able to segregate that there are three groups of fruits—apples, oranges, and peaches—then this is called unsupervised learning.
It's especially helpful for use cases where there is no prior training data and/or only very limited historical data.
"If the algorithm doesn't have past historical labeled data to learn from, and if the algorithm tries to organize the data into clusters based on natural pattern of occurrences of the data, then it is unsupervised machine learning."
Netflix's movie recommendation engine may not have prior history of likeness from a new user. However, it can still recommend movies. Where does it get its information? Unsupervised machine learning involves grouping of similar viewing patterns so it recommends similar content.
The same is true when looking for anomalies in computer networks. A software analyzes all the network activities, groups similar patterns, and flags any behavior that doesn't fit with the rest.
Similarly, when looking for anomalies in financial transactions, it looks at all the transactions across an entire organization and flags anything that doesn't fit with normal activity. Here are a few unsupervised learning algorithms and other functions where each works best.
- K-Means clustering is popularly used in fraud claim identification and loan default prediction applications by financial institutions.
- Another unsupervised machine learning algorithm is the Alternating Least Squares. It can be used to recommend movies or e-commerce products based on a similar user’s behavior or buying patterns.
- Dimensionality reduction, as the name suggests, reduces the number of variables required to extract meaningful and relevant information from data.
This is a type of machine learning in which machines use trial and error to come up with solutions to problems. In reinforcement learning, the computer faces a game-like situation, and it employs trial and error to achieve its goals.
To get the machine to perform as desired, it gets either rewards or penalties for its actions. The goal is to maximize the total rewards. A great example of reinforcement learning is robots trying to walk on unpaved terrain.
Since there are too many permutations and combinations of the pavement of the terrains, and there are numerous micro movements on how it needs to balance itself, the robot will have to do a lot of trial and error and learn from past steps. The robot will try to find the best way to move forward.
Whenever it successfully makes a step, the robot receives points or rewards. Each time it loses balance or falls down, it reduces the scores.
Eventually, it will adapt its approach in response to the different situations and will be able to walk successfully on unpaved terrain. This is called reinforcement learning.
"If the algorithm uses trial and error approach & also if it applies rewards and penalties based on its each action to eventually perform the optimal course of action, then it is reinforcement learning."
Reinforcement learning is a form of machine learning that allows an artificial agent to learn from its own actions and the consequences thereof. In other words, it maps situations to actions in order to maximize a numeric reward signal. This is done by doing a lot of trial and error.
Self-driving cars are an excellent example. In self-driving cars, there are numerous aspects to take care of, such as following the speed limit and staying within the drivable zone. Reinforcement machine learning does that by rewarding itself when it accomplishes a task successfully and by punishing itself with a penalty if a task is not achieved.
Humans think in a similar way. Automatic Parking and Lane assist are two examples of features on self-driving cars that use reinforcement learning. They receive rewards or penalties depending on their performance, and then try again based on these results.
Q-Learning is a reinforcement learning algorithm that finds the best course of action given the current state of the agent. You can use it to optimize ad recommendation systems. It does this by analyzing users' behavior and finding patterns, so you get ads that are more relevant to the products they're interested in.
Ad recommendation systems use Q-learning. In a normal ad recommendation system, the ads you get are based on your previous purchases or websites you may have visited. If you’ve bought a TV, you will get recommended TVs of different brands.
A Q-learning algorithm can optimize this recommendation system by recommending products that are frequently bought together. The system rewards itself when the user clicks on the suggested product.
What is the Recommended Programming Language for Machine Learning?
While R is used as a programming language for machine learning, most companies use Python as the go-to language for machine learning applications. Additionally, Python is easier to learn than R and has a larger user base.
Python offers several advantages over R, such as the number of libraries available, which allows developers to add features to their applications with ease. Additionally, Python is more flexible and can be used for a wide range of applications, including web development and data science projects.
Today, you might find some items in your home operating on this language. Home automation devices can process data in real time and interact with a range of technologies by using cloud-based machine learning based on Python. It's also used in many other industries such as finance, natural language processing, and computer vision.
So Python is the recommended programming language for machine learning, mainly because of its ease of use and for its wide open community support.
What are the Top 5 Real World Machine Learning Use Cases?
Banking, insurance, retail, e-commerce & some areas of IT are some of the industries that heavily use machine learning algorithms for their business use cases. Other fields that use it are digital streaming, health care, search, social media, and more. Here are some of the worlds biggest companies that apply it to their software:
- Netflix’s movie recommendation - Netflix uses machine learning algorithms to recommend movies based on your viewing history. The algorithms use a lot of data, including what you’ve rated with a thumbs up to the implicit data, such as what you’ve watched from start to finish, which other titles you watched without interruption or pause, and which ones you watched more than once. Then, Netflix uses all the data it has gathered—implicit and explicit—to provide better recommendations on what you can watch next.
- Amazon’s recommendation engine - Amazon is another company that utilizes machine learning algorithms to recommend products that you might like based on what you've already purchased. Try looking through the recommendations the next time you log into your Amazon account.You'll notice their similarity or relevance to your past purchases. These kinds of recommendation engines rely on Bayesian networks and Alternating Least Squares.
- Google’s spam detection in Gmail - Click on a spam email might lead to identity theft or phishing attacks, wherein hackers deploy malicious software on your system. Google and other popular email provider companies leverage the machine learning algorithms to determine whether a new email is spam or not spam, and automatically place the spam emails in a separate folder.
- Google Photo & Facebook face recognition & tagging - When you upload a photo to Facebook or Google photos, you may notice that the site automatically suggests you tag a friend in the picture as well as tag the page of the place. This is possible thanks to machine-learning algorithms that can identify faces, locations, and other objects.
- Allstate’s Insurance fraud claim identification - Allstate, an insurance company, uses machine learning algorithms to determine whether a claim is fraudulent. Classification algorithms like logistic regression, random forests or anomaly detection algorithms can be used to predict whether a claim is fraud or not.
What is the average salary of a Machine Learning Engineer ?
Machine learning jobs are abundant, but most people focus more on knowing the income, without knowing how the salaries are determined. It’s important to know that usual machine learning engineer salary varies according to the following three primary criteria:
- the number of years of experience
- the organization and the location to where you are applying for
- your qualifications from skill sets to certifications
According to Glassdoor, the average salary of a machine-learning engineer in the U.S. is $130,000 per year. Entry-level salaries start at $110,000 and expert-level salaries range up to $170,000 averaged across different locations in the U.S.
In India, the average salary of a machine learning engineer in India is around 12 lakhs INR per annum. Entry-level salaries start at 10 lakhs per annum INR, but experts can make up to 40 lakhs per annum INR on an average.
In wrapping up our exploration into the world of machine learning and data science, let's summarize the key takeaways:
- Machine Learning Essentials: First we explored the concept of machine learning, enabling computers to learn from data and make predictions without explicit programming.
- Machine Learning Mechanism: Next we discussed the process of converting past data into features, labeling inputs, and utilizing these features for predictions on new, unseen data.
- Types of Machine Learning: Then we explored the three main types—supervised, unsupervised, and reinforcement learning—each serving distinct purposes.
- Programming Language Recommendation: Next we highlighted Python's preference for machine learning due to its user-friendly nature and robust community support.
- Real-World Machine Learning Use Cases: Then we explored five top real-world applications, showcasing the practical implementation across industries.
- Career Insights: Finally we delved into the earning potential of a machine learning engineer, prompting consideration for a career in this rapidly growing field.
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There's a Chinese proverb that says
"the best time to plant a tree is 20 years ago — the second best time is now". The same goes for starting a career in machine learning.
If you haven't started your career in machine learning yet, now may be the best time to get started. Maybe it's time for you to learn about the field and see if it's what you're looking for.
Why not start your journey now with Big Data eLearning? Enroll in our free online courses, get access to our cheat sheets and strategies, and join our community. We'll be with you every step of the way as you embark on your successful machine learning or data science career.
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