What is Machine Learning?
Machine Learning is normally associated with the artificial intelligence (AI). The artificial intelligence enables computers to perform certain tasks without being programmed explicitly. These tasks include prediction, planning, recognition, robot control, diagnosis and many more. The main emphasis of machine learning is on the development of algorithms that can teach themselves to grow and change when they are exposed to the new data.
Read more: Data Science and its Scope in Pakistan
Is Machine Learning Same as Data Mining?
No, but we can somehow relate the processes of Machine Learning and Data Mining, the reason being both search through the data to look for patterns. The primary difference between Machine Learning and Data Mining, however is that instead of extracting data for human comprehension, Machine Learning uses that data to improve the program’s own understanding. In short, the program’s actions are automatically adjusted in accordance with the data patterns detected.
Can I Become a Machine Learning Engineer?
In order to be a machine learning engineer, you must have a deep understanding of a broad set of algorithms and applied math, problem solving and analytical skills, probability and statistics and programming languages such as Python/C++/R/Java. Beyond all these skills, you cannot be a successful machine-learning engineer if you do not have innate curiosity. In short, it’s not enough to have either software engineering or data science experience. You ideally need both.
Key Skills Required
If you are pursuing career in machine learning, you’ll probably have to learn Python, C++, R and Java at some point. C++ will help you speeding the code up, R will help you with stats and plots and Java will help you to implement mappers and reducers.
Probability and Statistics
For you to be a machine learning engineer, a firm understanding of probability and statistics is inevitable. This will help you learning about algorithms.
Applied Mathematics and Algorithms
If you have a solid understanding of algorithm theory and its working, you’ll be able to easily discriminate models like SVMs. The machine learning job requires you to have a firm understanding of subjects like gradient decent, convex optimization, lagrange, quadratic programming and partial differential equations.
It has been found that majority of machine learning jobs these days entail working with large data sets and processing large data sets through a single machine isn’t possible. This is why you require distributing it across an entire cluster. The projects such as Amazon’s EC2, Hadoop and Apache will make life easier for you.
Mastering the Unix Tools
Since machine-learning engineers are more likely required to do all of the processing on linux-based machines, they require access to the unix tools such as cat, grep, find, awk, sed, sort, cut, tr, and more. Mastering these tools therefore, becomes really important.
Advanced Signal Processing Techniques
Feature extraction is one of the most important parts of machine-learning. Different types of problems need various solutions, you may be able to utilize really cool advance signal processing algorithms such as: wavelets, shearlets, curvelets, contourlets, bandlets. Learn about time-frequency analysis, and try to apply it to your problems. If you have not read about Fourier Analysis and Convolution, you will need to learn about this stuff too.
Remember! A machine-learning engineer needs to stay updated with any up and coming changes. He or she needs to stay fully aware of the news regarding the development to the tools (changelog, conferences, etc.), theory and algorithms (research papers, blogs, conference videos, etc.).
Since the online community changes rapidly, machine-learning engineers must expect and cultivate this change. They should read papers like Google File System, Google Map-Reduce, Google Big Table, The Unreasonable Effectiveness of Data. In short, a machine-learning engineer must extract knowledge out of the free machine learning books available online to ensure steady career progress.
Happy Machine Learning!