Machine Learning (ML) is the basis of different kinds of Artificial Intelligence (AI) models. AI systems create human-like reasoning and learning in computer-based systems, and Machine Learning jobs use algorithms and statistical models to facilitate machines to learn and improve from experience without being explicitly programmed to do so. Machine Learning models can be considered as semi-structured functions wherein Data Scientists ‘train’ the models for specific outcomes without having to provide all the variables and interactions required.

The global Machine Learning market is booming and the demand for Machine Learning Engineers is on the rise. Many people translate the AI industry and automation boom as the loss of jobs. Although many traditional jobs might go off-market, the industry is estimated to provide 2.3 million Machine Learning jobs and other related opportunities worldwide in 2020, according to Gartner, Inc. The average Machine Learning salary of an experienced individual is estimated to be 805,032 INR per annum, as per glassdoor.co.in. This is the prime time to register yourself for Machine Learning bootcamps and work towards gaining yourself Machine Learning jobs.

Machine Learning Jobs – What it Entails

So, how does Machine Learning work? The secret lies in its semi-structured form which utilizes the ‘deep learning’ approach. A linear model is programmed in such a way as to take the given data and process it in a particular way. For instance, the rent for an apartment is calculated by entering the number of rooms available. ‘Deep Learning’ operates through multiple layers of feedback, imitating the human brain. These models will self-correct and optimize until its output gradually matches its input. The way social networks predict your behavior, based on your search history, is one example of the deep learning approach. Read this blog to find out more about Machine Learning engineering.

While seeking Machine Learning jobs, there are two job roles you can aspire to. One is the Data Scientist and the other is the Machine Learning Engineer. Data Scientist is the link between data models and business decision-makers. They help to fine-tune the data models so that businesses can ask the right questions of their data. Machine Learning Engineers are responsible for providing the data, which is fed to the data model to facilitate AI. In fact, they take theoretical data science models and scale them out into production-level models capable of handling terabytes of data on a daily basis. Broadly speaking, Data Scientists deal with the theory behind AI whereas Machine Learning Engineers put these theories into practice. Hence, a strong software engineering background is preferred for those seeking Machine Learning jobs.

Machine Learning Engineer- The Profile

Is a master’s degree or Ph.D. in Machine Learning required for landing Machine Learning jobs? I would say, not necessarily. What you need first is a passion for the industry, and then you need expertise. You need to be ready and willing to put in hours of seemingly meaningless/repetitive work before you breach the glamour-world of AI. Find a company that has projects on Machine Learning and sharpen your software and problem-solving skills. Below is a list of the skills you should possess to grab a Machine Learning job.

  1. Learn the fundamentals of Computer Science & Programming

A Machine Learning Engineer should be able to apply, implement or adapt computer fundamentals like computer architecture (memory, bandwidth, distribute processing, cache, etc.), data structures (stacks, trees, queues, multidimensional arrays, etc.), algorithms (searching, sorting, dynamic programming, optimization, etc.), computability and complexity as and when appropriate while programming. 

2. Develop Statistics & Probability Skills

Probability techniques like Bayes Nets and Markov Decision Processes and statistical analysis methods like ANOVA are at the heart of most Machine Learning algorithms. You need to be able to appropriately apply statistical measures like mean, median and variance, and use various statistical distributions like a uniform, binomial and Poisson to build and evaluate models from observed data.

3. Gain Knowledge on Evaluation of Data Models

Data models are used to find useful patterns like correlations, clusters, eigenvectors, etc. in any given data set. They are also used in anomaly detection and predicting the properties of unseen instances of classification. The continual evaluation of any given data model is necessary to ensure the smooth running of this estimation process. With the appropriate accuracy/error measure and evaluation strategies, iterative learning algorithms can utilize the resulting errors to tweak the model, to better it.

4. Acquire Skills in ML Algorithms

Standard implementations of ML algorithms are available through libraries/packages/APIs of scikit-learn, Spark, TensorFlow, and others. The Machine Learning Engineer must be able to effectively apply these and choose suitable models to fit the given data. You also must be aware of learning procedures and relative merits and demerits of various approaches to learning. Get exposed to different kinds of problems to familiarize with the various nuances of each and sharpen your ML skills.

5. Learn Software Engineering & Design

A Machine Learning Engineer often works with a small component that fits into the larger ecosystem of the product. You should understand how the different components work together; communication interfaces of each component must be designed to avoid bottlenecks as well as to scale well with large volumes of data.

Machine Learning has limitless applicability and is creating revolutions in fields as varied as education, healthcare, finance and more. You can find out more about Machine Learning Engineer Salary prospects in this blog.  The face of the world is changing dramatically, and the demand for AI and Machine Learning skills is on the rise. Like we said before, building a nuanced understanding of machine learning needs more than just tools — you need a well-crafted data science curriculum, hands-on projects, and 1:1 mentorship to guide your learning into a career. Check out Springboard’s data scienceAI/ML and data analytics career tracks for more.