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5 Topics to Master for a Successful Career in Machine Learning and Artificial Intelligence

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It’s no secret that Artificial Intelligence and Machine Learning skills are required to get a job in the software industry. It’s been the hottest topic of 2018, and it doesn’t look like that’s going to change any time soon.

But AI and Machine Learning are just buzzwords without any real meaning to them unless you know what to equip yourself with, in order to get a job in these fields. Companies are already frothing at the teeth to their hands on talented and motivated programmers in AI.

To put the job demand into perspective, let’s look at some industry statistics:

According to Gartner, AI will produce over 2.3 million jobs by 2020.

Studies conducted by Gartner also report that global Artificial Intelligence business value would reach $1.2 Trillion in 2018.

Starting salaries for data scientists have gone north of $200,000, as reported by Bloomberg Business.

Looking to get a slice of that pie? Here are 5 topics you will need to know like the back of your hand if you’re looking to get a job in the field of Artificial Intelligence and Machine Learning.

Preparing for a Successful Career in ML and AI

1. Probability and Statistics

Markov models, Bayes rule, and other probability processes and models are what constitute a lot of Machine Learning algorithms. Once you start working on your learning plan it, it’s fairly obvious.

Artificial Intelligence and Machine Learning have to deal a lot in probability and uncertainty. Real world data is often littered with uncertainty. So, learning probability from scratch is one of the best ways you can equip yourself for a career in ML and AI.

But there’s also Statistics. Knowing Means, Medians, Deviations, Different Distributions like Binomial, Poisson etc., will be invaluable. This is to build and observe on pre-existing data. A lot of Machine Learning algorithms use these statistics to create valuable information and data.

2. System Design

A person in Software Engineering needs to know how to deliver software products. AI and Machine Learning aren’t often standalone products, but important components in an otherwise larger technology and innovation ecosystem.

Here, a learner will need to understand how different components work and communicate together, such as library calls, database queries and building interfaces for users to interact with the machine and create and AI ecosystem. But that’s not all.

Good software design is also optimized; designing properly to work well with existing hardware and ensuring your algorithms scale up and down as necessary, is also important. Your algorithms will be dealing with a tremendous amount of data, so ensuring they can handle all that data and not choke, is of utmost importance.

There are also testing, documentation, and design skills that you’ll need to have to ensure quality and maintainability over time.

3. Machine Learning Algorithms and Libraries

Most software engineers know how to implement Machine Learning algorithms through libraries, packages, APIs etc. The trick lies in applying them effectively, using standard models such as decision trees and neural nets.

In addition, there also needs to be a learning procedure to fit new data, created using models like Genetic Algorithms, Linear Regression, Bagging, Boosting etc. An aspirant is also expected to know how these parameters affect learning.

There are also the relative advantages and disadvantages of different approaches, which the aspirant is expected to understand.

Additionally, there are also a multitude of “gotchas”, such as overfitting and underfitting, data leakage, lost data, bias etc. All of these skills are crucial for anyone looking to have a successful career in Machine Learning and Artificial Intelligence.

4. Data Modeling and Evaluation

AI and Machine Learning thrive because of data, and it’s imperative to understand Data Modeling. Data Modeling is a science using which a Data Scientist performs estimating the structure of a dataset, so he/she can find useful patterns, correlations, and clusters.

In addition, Data Modeling also involves predicting properties of unseen instances through classification, anomaly detection etc. This modeling also needs to be continuously evaluated at how good a given model is.

It is also an important requirement to test the accuracy of the data being evaluated. The evaluation can take place through validation methods. Iterative learning algorithms are often used, ensuring any errors in the data are used to tweak the model for future datasets.

Understanding all of this is important for a Data Scientist who works with ML & AI, even if only standard algorithms are being implemented.

5. Programming Languages

This will vary depending on the job role in question, the platform being coded on, and a multitude of other factors. But if you know a base programming language, it won’t be too difficult to pick up any language mentioned here.

Python is already a mainstay in programming, and it doesn’t look like it will change any time soon. Additionally, you would need to learn Hadoop to analyze big data. If you haven’t already, you should brush up your Java skills. It’s still widely used, even if it’s getting a bit long in the tooth.

Apache Spark is another technology which is valuable in the field of Machine Learning and Artificial Intelligence. Finally, learning SAS is a valuable skill which cannot be overlooked in the field of businesses, Artificial Intelligence, and Machine Learning.

Even if you haven’t learned some of these skills yet, with some basic programming knowledge, it shouldn’t be that difficult. Programming skills often carry over between different languages and platforms, since they are all built on a similar underlying logic. But learning any past generation programming language should give you the understanding and aptitude, you need to get yourself acquainted with any of the other technical skills you may need in your career for ML & AI.

Never Stop Learning

With the recent craze to get into Artificial Intelligence and Machine Learning, the job market is positively booming. To carve up a slice of the job pie, aspirants will need to be on their toes and keep themselves up to date on any and all of the requirements companies demand.

The world of AI and Machine Learning is just beginning. It’s an exciting time ahead for anyone who chooses to get themselves knee-deep in the field.

There’s a lot of scope for growth, and even more scope to widen horizons, since almost all companies worth their salt will want to ensure they are AI-ready before their competition is. This rapid growth is not just healthy, it’s necessary for both the job market and the technology market.

So if you’re hopeful for a job in this industry, keep updating your knowledge and never stop learning.

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