Have you heard people talking about machine learning but only have a fuzzy idea of what that means? Are you tired of nodding your way through conversations with co-workers? Let’s change that!
This guide is for anyone who is curious about machine learning but has no idea where to start. Let's check this one...
What is Machine Learning?
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that "learn" – that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI.
Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain.
“Machine learning” is an umbrella term covering lots of these kinds of generic algorithms.
Artificial intelligence(AI)
In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.
Is machine learning magic?
Once we start seeing how easily machine learning techniques can be applied to problems that seem really hard (like handwriting recognition), we start to get the feeling that we could use machine learning to solve any problem and get an answer as long as we have enough data.
But it’s important to remember that machine learning only works if the problem is actually solvable with the data that you have.
How to learn more about Machine Learning?
The biggest problem with machine learning right now is that it mostly lives in the world of academia and commercial research groups. There isn’t a lot of easy-to-understand material out there for people who would like to get a broad understanding without actually becoming experts. But it’s getting a little better every day.
If you want to learn, Andrew Ng’s free Machine Learning class on Coursera is pretty amazing as a next step. I highly recommend it.
Learn machine learning in 8 steps by yourself?
Learn the Prerequisites like basic information:- All machine learning algorithms are implemented with code. So programming skills in Python, R, Bash, or Java are a must for any aspiring ML engineer. In recent years, Python has emerged as the most popular programming language, especially for beginners. It has simple syntax, extensive built-in functions, the most-supported libraries, and wide package support.
a beginner or crash course in Python is the best way to get started with ML. Once you have mastered its basic functionalities, you will need to learn how to extract, process, and analyze data. Most ML and data science courses will have a section dedicated to efficient data analysis.
Learn ML Theory From A to Z:-Planning and Data Collection, Planning and Data Collection, Data Preprocessing, Improving and Bettering Your Models
Deep Dive Into the Essential Topics:-Practice Machine Learning Workflow, Work on Real Datasets and Learn Comprehensively.
Work on Projects:-Choose Based on Your Interest, Work on Basic Projects and Build Value-Adding Projects
Learn and Work With Different ML Tools:- TensorFlow, Auto-WEKA, Google Cloud AutoML, Amazon Machine Learning (AML) etc.
Study ML Algorithms From Scratch:-The most thorough explanations will likely include highly advanced math. If you are not keen on math-intensive descriptions, you can stick to step-by-step tutorials written in Python, R, or any other programming language. By the end of your study, you will understand the machine learning techniques used to load and prepare data, evaluate model skills, and implement a suite of linear, nonlinear, and ensemble algorithms.
Opt For a Machine Learning Course:-You can choose to take an ML/AI course at any stage of your learning process. Courses can help you gain momentum when you are first starting out, or help you hone specific skills in more advanced topics. You should aim to select a course that has a state-of-the-art curriculum and focuses on in-demand skills.
Apply for an Internship:-Try to apply for internships in the industry where you’d like to work. Indiscriminately applying for any and all open positions will only lead to more rejections. You should curate your professional resume and portfolio to the role you’re applying for. You can find ML internships by visiting dedicated websites like LetsIntern, Internshala, and AngelList, or by getting in touch with companies directly.
Conclusion
Machine learning is a powerful tool for making predictions from data. However, it is important to remember that machine learning is only as good as the data that is used to train the algorithms. In order to make accurate predictions, it is important to use high-quality data that is representative of the real-world data that the algorithm will use.
The aim of machine learning is to automate analytical model building and enable computers to learn from data without being explicitly programmed to do so.