Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, the text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
Better Career Opportunities and Growth
A report by TMR notes that MLaaS (Machine learning as a Service) is predicted to grow from $19.9 billion by the end of 2025, to a mere $1.07 billion in 2016. This is a staggering amount of growth, both in absolute terms, as well as year-on-year.
Machine learning makes a mockery of anything that can be called “important” – both at a financial as well as a global scale. If you are looking to take your career to another level, Machine Learning can do that for you. If you are looking to involve yourself in something that will make you part of something global as well as contemporary relevance, Machine Learning can do that for you as well.
Better Salaries
The best machine learning engineers these days are paid as much as immensely popular sports personalities! And that’s no exaggeration! The average machine learning engineer salary is 8 lakhs per annum – and that’s just at the starting of one’s career! An experienced machine learning engineer takes home anywhere between 15 to 23 lakhs per annum.
If you’ve ever wondered who can learn machine learning, the answer is – you can! And if you’ve asked yourself where to learn machine learning, here’s your answer: BIT offers a course in Machine Learning and AI, and it teaches you, among other things, NLP, Deep Learning, Reinforcement Learning, and Graphical Models. Moreover, it provides you a solid foundation in Predictive Analytics and Statistics as well.
Lack of Machine Learning Skills is Plaguing Corporations
Given the rapid rate at which technological leaps have been made, a lot of corporations have been left playing catch up. Digital transformation is a huge industry, and the truth of the matter is that there are simply not enough machine learning professionals to cater to new industry demands.
Machine learning and Data Science are intricately linked
If religion ruled the masses for entire centuries before modernity, it is now true that Data Science rules the masses, due to its all-explaining nature and commercial as well as innovative viability.
And Machine Learning is just a shadow of Data Science. To take your career as high as you can’t even imagine, you can become competent in both these fields, which will enable you to analyze a frightening amount of data, and then proceed to extract value and provide insight into the data.
There are five core tasks in the common ML workflow:
1. Get Data
The first step in the Machine Learning process is getting data.
This process depends on your project and data type. For example, are you planning to collect real-time data from an IoT system or static data from an existing database?
You can also use data from internet repositories sites such as Kaggle and others.
2. Clean, Prepare & Manipulate Data
Real-world data often has unorganized, missing, or noisy elements. Therefore, for Machine Learning success, after we chose our data, we need to clean, prepare, and manipulate the data.
This process is a critical step, and people typically spend up to 80% of their time in this stage. Having a clean data set helps with your model’s accuracy down the road.
After getting the data to a state you like, you need to convert the data sets into valid formats for your chosen ML platform. For example, you may need to translate the data into a .CSV file and upload it to AWS S3.
Finally, you split your data into training and test data sets. The training set is used to train the model in the next step, while the test data is used to validate the model in the fourth step. The typical default is a 70/30 split between training and test sets.
3. Train Model
This step is where the magic happens! The data set connects to an algorithm, and the algorithm leverages sophisticated mathematical modeling to learn and develop predictions.
These algorithms commonly fall into one of three categories:
Binary – Classify into two categories
Classification – Classify into many categories
Regression – Predict a numeric
4. Test Model
Now, it’s time to validate your trained model. Using the test data from Step 3, we check the model’s accuracy.
If the results are not satisfactory, you need to improve and retrain your ML model (Step 5).
5. Improve
Practice makes perfect! Here are a few things you can do to refine your model and improve accuracy:
Review your model’s results with your business stakeholders. Are there other data elements worth adding to your model to make it more accurate?
Reconsider your algorithm choice. Within each class of algorithms, there are dozens of algorithm choices. A different algorithm may perform better for you
Adjust the parameters of your chosen algorithm to improve performance. Sometimes small adjustments have a significant impact.
These are just high-level steps – ML is as complicated as you choose! That said, by understanding the basic process of developing ML models, you gain a solid foundation for further learning.
There are also some types of machine learning algorithms that are used in very specific use-cases, but three main methods are used today.
Supervised Learning
Supervised learning is one of the most basic types of machine learning. In this type, the machine learning algorithm is trained on labeled data. Even though the data needs to be labeled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.
In supervised learning, the ML algorithm is given a small training dataset to work with. This training dataset is a smaller part of the bigger dataset and serves to give the algorithm a basic idea of the problem, solution, and data points to be dealt with. The training dataset is also very similar to the final dataset in its characteristics and provides the algorithm with the labeled parameters required for the problem.
Unsupervised Learning
Unsupervised machine learning holds the advantage of being able to work with unlabeled data. This means that human labor is not required to make the dataset machine-readable, allowing much larger datasets to be worked on by the program.
In supervised learning, the labels allow the algorithm to find the exact nature of the relationship between any two data points. However, unsupervised learning does not have labels to work off of, resulting in the creation of hidden structures. Relationships between data points are perceived by the algorithm abstractly, with no input required from human beings.
Reinforcement learning
Reinforcement learning directly takes inspiration from how human beings learn from data in their lives. It features an algorithm that improves upon itself and learns from new situations using a trial-and-error method. Favorable outputs are encouraged or ‘reinforced’, and non-favorable outputs are discouraged or ‘punished’.
Based on the psychological concept of conditioning, reinforcement learning works by putting the algorithm in a work environment with an interpreter and a reward system. In every iteration of the algorithm, the output result is given to the interpreter, which decides whether the outcome is favorable or not.
Machine Learning is very popular as it reduces a lot of human efforts and increases machine performance by enabling machines to learn for themselves. Consequently, there are many career paths in Machine Learning that are popular and well-paying such as Machine Learning Engineer, Data Scientist, NLP Scientist, etc.
1. Machine Learning Engineer
A Machine Learning Engineer is an engineer (duh!) that runs various machine learning experiments using programming languages such as Python, Java, Scala, etc. with the appropriate machine learning libraries. Some of the major skills required for this are Programming, Probability, and Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc.
2. Data Scientist
A Harvard Business review article called a Data Scientist as the “Sexiest Job of the 21st Century” (And that’s incentive right there to become one!!). A Data Scientist uses advanced analytics technologies, including Machine Learning and Predictive Modeling to collect, analyze and interpret large amounts of data and produce actionable insights. These are then used to make business decisions by the company executives.
3. NLP Scientist
An NLP Scientist basically helps in the creation of a machine that can learn patterns of speech and also translate spoken words into other languages. This means that the NLP Scientist should be fluent in the syntax, spelling, and grammar of at least one language in addition to machine learning so that a machine can acquire the same skills.
4. Business Intelligence Developer
A Business Intelligence Developer uses Data Analytics and Machine Learning to collect, analyze and interpret large amounts of data and produce actionable insights that can be used to make business decisions by the company executives.
5. Human-Centered Machine Learning Designer
Human-Centered Machine Learning relates to Machine Learning algorithms that are centered around humans (as if that were not obvious from the title!!). An example of this is video rental services like Netflix that provide their viewers with movie choices based on their preferences to create a “smart” viewer experience.
Machine Learning helps to improve business decisions, boost productivity, detect disease, forecast the weather, and much more. Basically, a machine learns automatically from the inputs. Some of the best machine learning examples are mentioned below:
Wrapping Up
So these were some of the most popular examples of machine learning applications in the real world. If these applications have thrilled you, and you look for a career in machine learning, it’s time to take the best machine learning courses, certification, and training. These courses will make you proficient in machine learning techniques like supervised learning and unsupervised learning.
As the digital world is progressing and new technological changes are openly accepted, career opportunities for Machine learning professionals will invariably thrive. So start your journey and enter into the world of technology with machine learning.
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