What should you learn to work on machine learning field?

Machine learning is the next big thing in the technology field. It is the part of Artificial Intelligence. Machine learning enables computers to do certain tasks, such as voice recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed.

Machine learning is a branch of AI that gives computer systems the ability to automatically learn and improve from experience, rather than being explicitly programmed. In machine learning, computers use massive sets of data and apply algorithms to train on and make predictions.

Machine learning systems are able to rapidly apply knowledge and training from large data sets to perform facial recognition, speech recognition, object recognition, translation, and many other tasks.

Many people showing their interest in this field. You are reading this article because you are also interested in machine learning. So, what skills do you need to start?

Let's find out -

Programming in Python/C++/R/Java

If you want to join the machine learning field, then you need to learn Python, C++, R, Java languages at some point. It will help you speed up your code, works in statistics and plots. Side by side you should learn data structures like stacks, queues, multi-dimensional arrays, trees, graphs, etc., algorithms like searching, sorting, optimization, dynamic programming, etc., computability and complexity of P, NP, NP-complete problems, big-O notation, approximate algorithms, etc., and computer architecture such as memory, cache, bandwidth, deadlocks, distributed processing, etc.

Math and algorithms

Standard implementations of Machine Learning algorithms are widely available through libraries/packages/APIs, but applying them effectively you should have a deep understanding of a broad set of algorithms and applied math. This is important especially in deep learning because you will be working with data in the form of multi-dimensional matrices. You need to understand the math in order to create your models efficiently.

Learn probability and statistics

Probability and statistics are at the heart of the Machine Learning algorithms. You need to have a firm understanding of probability and stats to work with Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models.

Different ML algorithms

Well, this one is obvious. But, once you know the programming language and the math, you need to dive into machine learning and deep learning. Learn about different ML algorithms and architectures. Decision Trees, SVMs, Neural Nets and so on. Learn why a particular model is used and its pros and cons as compared to others. Learn how to optimize and tune the models.

Learn ML frameworks

As you will be working in the industry, you will need to learn how to build and run ML models in frameworks like Keras, Tensorflow, Pytorch, etc. If you’re just beginning you can start with Keras which acts as a frontend to frameworks like Tensorflow and abstracts away a lot of lower level details so that you can focus on your models and build them quickly.

Understand Big Data

Data engineering and architecture is a field of specialization in itself, but every machine learning expert must know how to deal with big data systems, irrespective of their specialization within the industry. Machine learning is nothing but learning from data, generate insight or identifying patterns in the available data set. There is a various application of machine learning algorithms like “spam detection”, “web document classification”, “fraud detection”, “recommendation system” and many others. Understanding how large amounts of data can be stored, accessed and processed efficiently is important to be able to create solutions that can be implemented in practice and are not just theoretical exercises.

Distributed computing

Most of the time, machine learning entails working with large data sets. To process this large data you need a cluster of computing. Projects such as Apache Hadoop and cloud services like Google computing engine or Amazon’s EC2, which makes it easier and cost-effective.

Software engineering and system design

At the end of the day, a Machine Learning engineer’s typical output or deliver software or system. And often it is a small component that fits into a larger ecosystem of products and services. You need to understand how these different pieces work together, communicate with them and build appropriate interfaces for your component that others will depend on.

Conclusion

The machine learning field is growing rapidly as companies try to get the most out of emerging technologies. There is always a new implementation and improvisation in this field. So, you should keep your up to date, learn from practice and read as much as you can. There are great free machine learning books online and you should read those also.