Artificial intelligence is shaping our future with more might and power than any other technology or thing, for that matter. Sensors and smart things are having a profound impact on how we perform basic functions using tech-enabled devices. Its critical discipline, machine learning, is also growing in popularity- unleashing a wide array of applications across industries.
Machine learning is the art and science of teaching computers to learn from provided data and make decisions or predictions. In its true essence, with machine learning, a computer device should be able to devise patterns without being explicitly taught to.
Machine learning is one use case of the infrastructure that can handle big data. A few places where we can see machine learning in action are:
- Supervised learning – Your email provider places every fishy email coming from bogus sources into the spam folder. A supervised learning algorithm takes labeled data and creates a model on its own to make predictions on new data.
- Unsupervised learning – Businesses use hundreds of demographics and behavioral factors to segment customers into distinct groups. Unsupervised learning happens when the data is not labeled or categorized. It is upon the machine to spot patterns in the data, classify it, and derive meaning.
- Reinforcement learning – Sensors, computer, and camera within a self-driving car integrate to form an environment where they can navigate a city without a human. Reinforcement technique uses a reward system of trial and error in order to optimize the long-term reward.
If we’ve got you excited, let’s dig deep into the world of machine learning and understand how the technology works, what makes it so special, and what the future holds for you.
The Current State of Machine Learning
AI augmentation is expected to hit $2.9 trillion in revenue generation by 2021, freeing the global workforce of 6.2 billion hours of productivity, Gartner predicts. Machine learning sits at the center of digital transformation as it backs up useful technologies such as robotics, process automation, and speech recognition. Therefore, within the AI category, ML will attract a majority of investment.
Innovations in industries courtesy Machine Learning-
- Banking – ML analytics can empower banks and other financial institutions to leverage advanced analytics and learn more about their customers, more efficiently. By analyzing customer behavior and tailoring services and products to meet their needs, companies previously saw an average increase of $369,000,000 per year in their revenue, according to McKinsey.
- Energy – Machine learning, in conjunction with deep learning will prove efficient to combat climate change by encouraging efficient energy utilization. Businesses are already using ML to sell solar panels and researchers predict that cloud-based monitoring systems will optimize energy in real-time soon!
- Retail – Machine learning can make a retail store’s experience dynamic and personalized. Convenient and customized brand encounters will improve profits and sales for retail stores.
- Healthcare – AI and ML are strong tools to help doctors and healthcare institutions. In one case study, a group of researchers used ML to predict whether patients would be hospitalized due to heart disease. They were able to achieve 82% accuracy rate which was 26% better and more accurate than the existing prediction models.
Get Started with Machine Learning
Machine learning, by its name, can appear too intimidating without a gentle introduction to its prerequisites. We suggest you don’t overdo this part, though. Unless you want to be a Ph.D. at machine learning, a bit of knowledge can do you good. The roadmap to learn machine learning goes along the following lines:
- The right place to begin is to revise linear algebra. Pay particular attention to the core concepts including vectors, matrix multiplication, Eigenvectors, and determinants- these are what make machine learning algos work.
- After that, calculus is your next battleground. Learn and understand the meaning of derivatives, and how they are used for optimization. Learn everything you can on single variable calculus and at least the basics of multivariable calculus.
- Learn Python for data science. Go through all libraries used in machine learning coding, such as Pandas, Numpy, SKLearn, Matplotlib. Machine learning leverages these tools.
- Understand statistics for data science, mainly Bayesian probability.
- Go through ML in theory. You might want to argue if it is worth the effort and time to go through the theory when you can make things work only by learning about ML packages. But, anyone who wants to apply ML in their work should have a firm grasp on the fundamentals. Learn machine learning theory to:
- Plan and collect data – Data collection can be a time-consuming process. Every ML engineer should have a firm grip on what data they need and how much.
- Make the right data assumptions and preprocess data – Different algorithms make different assumptions about any set of data. You should know how to preprocess your data, whether or not to normalize it, and how to make your data robust to missing information.
- Interpret model results – ML is not a black-box. Surely, not all results can be interpreted, but you need to analyze your model in order to improve it. Therefore, gather in-depth understanding to rate your model as overfit and underfit, gauge the room for improvement, and explain business results to stakeholders.
- Improve and tune-in your models – You need to know about the various regularization methods and tuning parameters to answer basic questions about whether your model needs feature-engineering or data collection, or if you can ensemble your models.
- Drive business value – ML projects cannot be done in a vacuum. You need to understand the tools you need, and what goals you are trying to achieve through them. Which metrics are important to your business?
- Practice toward targeted projects. There are three levels to achieve targeted practice with machine learning algorithms and concepts:
- Practice the machine learning workflow including data collection, cleaning, preprocessing, model building, fine-tuning, and testing and evaluation.
- Practice on real datasets to gain a deeper understanding of which models are appropriate for which kinds of challenges.
- Dive deep into individual topics and learn about clustering algorithms, and so on by utilizing and exercising your knowledge.
- Now you are ready to take the ML world by storm with some really cool and happening projects. Consider the Titanic Survivor Prediction problem or any such challenging statement that truly tests what you’ve learned.
Here are a few tools and technologies you need to equip yourself with to gain an edge over the competition and solidify your expertise with ML:
- Python libraries – NumPy, Pandas, SciKit-Learn, SciPy, Seaborn, Matplotlib
- Algorithms – Linear regression, Logistic regression, Naive Bayes Classifier, K – Nearest Neighbors, K – Means, Support Vector Machine, Decision Trees, Random Forests, Gradient Boosting
Future Predictions with ML at the Core
Without a doubt, ML will leave a strong impact on how operations and processes are managed in the enterprises of tomorrow. Already, ML is changing the dynamics in enterprises and causing companies to mix and match people and software to get things done.
The idea that AI is a simple robot program to do repetitive tasks is no more. Machine learning and AI are opening new channels of applications in various industries. It is safe to predict that the human-machine interaction will boost as a result of the demand for more and more sophisticated ML solutions.
Further, thanks to ML, we will experience a rise in AI assistants and how frequently we communicate with one. This year and the next will be imperative for us to see what these assistants are capable of. Companies such as Hyundai and Kia are planning to integrate AI assistants in their vehicles, starting this year.
Many more surprising and interesting use cases of ML await us in the future.
We are only seeing the tip of the iceberg when it comes to the potential AI and ML have to revolutionize the way we operate and perform. This is just the beginning of the wave of advancements we are expecting. Technology is only going to get faster and sharper!
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