Machine learning has silently become an indispensable part of our lives in the past decade. From selfie cameras to virtual assistants such as Siri and Alexa, we are dependent on machine learning to get our needs completed. The presence of products and applications that implement machine learning at their core will only increase with time.
With so much happening around machine learning, more and more young professionals keen on shaping their career in software programming and technology are preferring machine learning as their foundation. Hence, in recent years courses such as msc machine learning has become a lucrative career option. This post aims at guiding such enthusiasts. Here, we give detailed information on skills required to become a machine learning engineer, equipped to face real-time challenges.
1. Learning Programming Languages such as Python/C++/R/Java
If you plan to make a career in machine learning, you should familiarize yourself with all these languages. You should pay more attention to C++ as it will enhance your coding speed. R is highly effective when you are dealing with statistics and plots. Hadoop is Java-based. Thus, you will need to implement mappers and reducers in Java.
2. Probability and Statistics
Theories will be your saviors when you are learning about algorithms. Some useful samples include Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. A detailed understanding of Probability and Stats will help you in understanding these models. It is advisable to use statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.
3. Microsoft Excel
You might ask, aren’t we discussing skills for Data Science? Then why Excel? Because Excel is one of the best tools for data management. It is arguably one of the best 2D data editors and a fundamental platform for advanced data analytics. You can clean your data using Excel and can look-up required data among thousands of records.
Visual Basic for Applications (VBA)is one of the most under-rated Excel features. It is Excel’s programming language that enables you to run loops, macros, and other tools.
4. Distributed Computing
Machine learning jobs have evolved with time, and working on large data sets has become common. A single machine has certain limitations. Hence, it is incapable of handling large data sets. So, you need to distribute it within an entire cluster. In such cases, projects like Apache Hadoop and cloud services like Amazon’s EC2 make the task easier.
5. Advanced Signal Processing Techniques
An indispensable part of machine learning is feature extraction. There are no universal solutions for a problem. Every problem has a unique solution. Hence, you could implement cutting-edge signal processing algorithms including, wavelets, shearlets, curvelets, contourlets, and bandlets.
6. Data Visualization
Data visualization is one of the most fun parts of machine learning as it is more of an art than a hard-wired step. A data visualization expert builds a story out of the visualizations.
Plots like Histogram, Bar charts, and pie charts are useful for beginners. You can then move to advanced charts like waterfall charts and thermometer charts that are very useful during the exploratory data analysis stage. The univariate and bivariate analyses become much easier to understand thanks to colorful charts.
7. Cloud Computing
Data science includes using cloud computing products and services to help data professionals access the resources needed to manage and process data. A data scientist’s everyday role includes analyzing and visualizing data stored in the cloud.
Data science and cloud computing go hand in hand. Cloud computing allows data scientists to use platforms such as AWS, Azure, and Google Cloud. These platforms provide access to databases, frameworks, programming languages, and operational tools.
Understanding the concept of cloud and cloud computing is a critical skill for a data scientist.
8. Database management
80% of a data scientist’s work goes into preparing the data for processing in an industry setting. Data scientists should know how to manage data as they deal with a large chunk of data.
Database Management is a culmination of programs that can edit, index, and manipulate the database. In large systems, a DBMS allows users to store and retrieve data at any time. Using DBMS, you can support a multi-user environment to access and manipulate data parallelly. You can manipulate the data, the data format, field names, and file structure through DBMS.
9. Structured Thinking
Let us say your goal is to become a data scientist. You will break this big goal into multiple parts like acquiring skills, preparing your resume, applying for a job. Similarly, the ability to break down a problem into several multiple parts to solve it efficiently is known as structured thinking.
A data scientist always looks at problems from various perspectives. It is an acquired skill, but you can work on it and improve with practice.
10. Other skills
Apart from the skills mentioned above, you will need a few other skills to ensure you stand out from the crowd. The above-listed skills will ensure your foundation is strong, and by picking up a few more skills, your foundation will stand the test of time. Here are a few skills worth learning:
Update yourself
1. You should frequently update your knowledge and should know about the latest developments and changes. You can enhance your understanding of this field and update your knowledge by attending conferences and skimming through research papers, blogs, and videos.
Read a lot:
1. Make a habit of reading relevant papers such as Google Map-Reduce, Google File System, Google Big Table, and The Unreasonable Effectiveness of Data.
Now that you have an expansive list of skills, the next question you would have is, “How can I develop these skills?” If you come from a quantitative background, the journey to becoming a Machine Learning Specialist will be smoother. However, not coming from a quantitative background will not stall your dream of entering into machine learning. It will only make it a bit more challenging.
If you are genuinely interested in machine learning and are passionate about the field of Data Science, then you can start at any stage and pursue a career in machine learning.
