Updated November 6, 2017
As a graphic designer I perform a simple task ten times per day, every single day, and this task takes me roughly one minute to perform. Add that up over a forty eight week work year and that takes precious time away from critical design productivity.
To be exact that time comes out to:
144,000 seconds or 2,400 minutes or 40 hours or 1 full work week.
What is 1 minute ten times a day? No big deal, right? Well, Clearly it is a big deal!
The task I am performing is so simple that I could train a three year old to perform it, but it still has to be done. And there are child labor laws against this, remember?
Task: Export design document — configure the document into the printer settings — set the properties — click print.
Now imagine getting back one full week of precious work productivity every single year of your career, based on a thirty five year career.
Thirty five weeks of work would earn you back 1,400 hours over an entire career.
Machine learning is about giving us the ability to stop wasting time on menial tasks, like taking 40 hours per year to print a document, in order for productivity and creativity to reach its fullest potential.
Machine learning already has amazing job opportunities right now, but the future potential is even greater.
Fortune put out an article stating that IBM projects Data Science to soar 28% by 2020. I tell you this fact to understand the importance of Machine Learning. The growth of machine learning will easily match that of data science. To understand why let’s define the difference between the two outcomes of each practice.
Data Analyst vs. Machine Learning Engineer
“In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you’re analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination—charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. This is especially important to note—the “audience” for your output is human. As a Machine Learning engineer, on the other hand, your final “output” is working software (not the analyses or visualizations that you may have to create along the way), and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves. This is why the software engineering skill set is so important to a career in Machine Learning.”
- Machine learning would give me the ability to save 40 hours per year in my job so that I could reallocate that time to more creative tasks.
- Data Science would tell me that I lost 40 hours per year and I need to figure out how to get that time back.
The difference between data science and machine learning is passive information vs active solution. Both are very necessary skills, but today we are going to focus on how to get you a job in machine learning.
The current need for Machine Learning
Excerpt from Forbes Article about the demand for Machine Learning:
“To stay competitive, companies need these specialists now and cannot wait five years for universities to produce graduates from new courses.”
The demand for machine learning is so high right now that companies are scrambling to find qualified individuals capable to perform the necessary tasks. Not only is this job incredibly future proof, but there are job openings available to you as soon as you develop the necessary skills.
What Skills Will You Need to Enter the Machine Learning Industry?
- Computer Programming (It seems like every longterm job requires this skill)
- Python will be the best language for you to get started in Machine Learning.
- Algorithms and Statistical Analysis
- This skill will help define where improvements can be made in a system.
- Evaluation and Application Skills
- Once you define an area within a system that is draining productivity you must evaluate the results and apply a solution. You will repeat this process many times as you attempt to boost productivity in a system.
- Analyze, Evaluate, Test. Analyze, Evaluate, Test. Analyze, Evaluate, Test.
- Become great at this skill!
- Software Engineering
- As a Machine Learning expert you will be called upon to help fix large systems within products or services. Your ability to carefully design these systems to avoid hang ups and crashes will be extremely important. Make sure you learn best practices for software engineers so that you are a invaluable asset to your client or company. Great Article by Tech Beacon about the best practices for Software Engineers.
Where is the Best Place to Build Your Machine Learning Skills?
Edureka’s Python course helps you gain expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers. You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and leveraging Apache Spark.
This course looks at python in a broad spectrum. You will not only understand how to apply python to Machine learning but you will also learn key data analysis concepts. This will increase you value and diversify your skills.
This course with Microsoft will get you in the door and help you to understand the basic operations of machine learning. you will learn how to explore classifications, what regressions are in machine learning, how to improve models and details on modeling, recommender systems (learn how to make the machine understand behaviors), and leverage hands on experience with R and python coding languages.
This course is taught by a professor from MIT and Duke, as well as a Senior Content Developer at Microsoft.
If you are already proficient in Python and you understand the concepts of data analysis for machine learning then this is the course for you. Once you have a grasp on those two concepts you must become proficient and executing and deploying machine learning software. There are professional best practices and industry standards in order to effectively collaborate on projects for machine learning.
Make sure you are strong in either python or Java before starting this course. If you have developed you skill in Python but you are unfamiliar with java. Take a free course to brush up on your skills: Intro to Java