The wave of digital transformation has begun and companies across the world are embracing it. Technologies are advancing at a rapid pace and we have added many new terms in our daily lives like Blockchain, Augmented Reality, the Internet of Things (IoT), edge computing, and so on. But, at the top of the technology pyramid is artificial intelligence and, it’s important subset, machine learning. These have emerged as wonderful technologies and invaded the research space for quite some time now.
Whether you are a fresh graduate starting out your career in AI or data science or an experienced professional switching to this domain, knowledge of machine learning basics is a must. With companies adopting machine learning for its immense capabilities, many professionals are trying to gain ML expertise and taking their careers to new heights. Companies are pouring in huge funds for research and development in AI and machine learning these days. This translates into increased demand for machine learning experts who can build high-end products for people.
Don’t you want to step into this promising career field? If yes, you may be thinking about how to get started and whether your prior knowledge would be of any use in learning ML. Read on to know what are the basics required to enter the machine learning world and explore your opportunities.
Machine Learning – What is it?
Machine learning is often introduced as an area of artificial intelligence and computer science that focuses on making computer systems capable of learning like humans and performing the desired actions. Machine learning experts create software and algorithms that allow machines to make predictions based on input data and improve through iterations. Machine learning has started to play a pivotal role in a variety of applications like recommendation engines of eCommerce websites, facial recognition, spam filtering, natural language processing, banking fraud detection, and so on.
Machine Learning Basics
After discussing briefly about machine learning, let us know what are the basics one should know of when starting to explore the subject. Here are some of the fundamental topics:
Types of Machine Learning
Almost all the tutorials on machine learning first give you a complete overview of the technology and some of its commonly seen applications. Post that, learners come across the types of machine learning, which is basically divided into three categories – supervised learning, unsupervised learning, and reinforcement learning. Beginners usually start with the most mature and most studied type of learning, called supervised learning.
Terminology used in Machine Learning
Beginners who have no prior knowledge of machine learning may find some terms new. Any machine learning tutorial will use commonly used ML terms like bias, weights, cross-validation, underfitting, overfitting, and so on. These terms are used when you use different machine learning algorithms to train the models.
The process of Machine Learning
Next, you should know about the steps followed in the machine learning process. Companies first collect raw data from disparate sources, which can be structured or unstructured. The data is then cleaned so that there are no missing, duplicate, or corrupt values in the sets. In other words, the quality of data is improved so that it can be used further. This data is then divided into training and test data to start the process of model training. An appropriate algorithm is selected and training data is fed into the model. After successive iterations, the training is completed and test data is applied to check if the model is producing desired results.
Machine Learning Algorithms
As mentioned in the previous point, an algorithm is used to train the model. So, one should be aware of the different ML algorithms used and how to select the most appropriate one for a given scenario. For supervised learning, algorithms like decision trees, regression, Naive Bayes, and nearest neighbor are used. For unsupervised learning, algorithms like K-means clustering, neural networks, hierarchical clustering, neural networks, and apriori algorithms are used.
Programming languages and their ML libraries
Any field of computer science is incomplete without programming languages. When it comes to machine learning, languages like Python and R are widely used. Python has powerful libraries and frameworks for ML capabilities like Scikit-Learn, SciPy, TensorFlow, and Keras. Similarly, popular R packages for machine learning include caret, DataExplorer, dplyr, ggplot2, mlr3, and rpart. Based on which language you prefer, learn about the mentioned libraries or packages.
Machine Learning Online Course (An Easier way to Learn the ML Basics)
Knowledge of machine learning fundamentals is a valued skill set for many job roles like machine learning engineer, AI researcher, NLP scientist, data scientist, data analyst, and data architect. If self-study isn’t a viable option for you, then an ML online course is the most suitable option. All you need to do is search for a reliable training provider, check out their AI and machine learning courses, and enroll in any one of them. The course completion certificate that will be awarded to you will increase your credibility and get you a good job role with a lucrative salary at a global enterprise.
