Machine learning has been making a silent revolution in our lives for the past decade. From capturing selfies with a blurry background and focused face capture to getting our queries answered by virtual assistants such as Siri and Alexa, we are increasingly depending on products and applications that implement machine learning at their core.
In more basic terms, machine learning is one of the steps involved in artificial intelligence. Machines learn through machine learning. How exactly? Just like how humans learn – through training, experience, and feedback. One may choose to invest in a good artificial intelligence program or begin with a data science course to start on one’s AI learning journey. It may be well to keep in mind that one may find the data science course fee to be on the higher side. But the knowledge and career advancement opportunities make it worthwhile.
Once machines learn through machine learning, they implement the knowledge so acquired for many purposes including, but not limited to, sorting, diagnosis, robotics, analysis, and predictions in many fields.
It is these implementations and applications that have made machine learning an in-demand skill in the field of programming and technology.
Look at the stats that show a positive trend for machine learning projects and careers.
- Gartner’s report on artificial intelligence showed that as many as 2.3 million jobs in machine learning would be available across the globe by 2020.
- Another study from Indeed, the online job portal giant, revealed that machine learning engineers, data scientists, and software engineers with these skills are topping the list of most in-demand professionals.
- High-profile companies such as Univa, Microsoft, Apple, Google, and Amazon have invested millions of dollars in machine learning research and designing and are developing their future projects on it.
With so much happening around machine learning, it is no surprise that any enthusiast who is keen on shaping their career in software programming and technology would prefer machine learning as a foundation to their career. This post is specifically aimed at guiding such enthusiasts and gives comprehensive information on machine learning skills that are needed to become an ML engineer, who is ready to dive into the real-time challenges.
Machine Learning Skills
Organizations are showing massive interest in using machine learning in their products, which would in turn bring plenty of opportunities for machine learning enthusiasts.
When you ask machine learning engineers the question – “What do you do as a machine learning engineer?”, chances are high that individual answers would differ from one professional to another. This may sound a little puzzling, but yes, this is true!
Hence, a beginner to machine learning needs to have a clear understanding that there are different roles that they can perform with machine learning skills. And accordingly, the skill set that they should possess, would differ. This section will give clarity on machine learning skills that are needed to perform various machine learning roles.
Broadly, three main roles come into the picture when you talk about machine learning skills:
- Data Engineer
- Machine Learning Engineer
- Machine Learning Scientist
One must understand that data science, machine learning and artificial intelligence are interlinked. The following quote explains this better:
Data science produces insights. Machine learning produces predictions. Artificial intelligence produces actions.
A machine learning engineer is someone who deals with huge volumes of data to train a machine and impart it with the knowledge that it uses to perform a specified task. However, in practice, there may be a little more to add to this:
|Machine Learning Role||Skills Required||Roles and Responsibilities|
- Python, R, and Databases
- Parallel and distributed
- Knowledge on quality and reliability
- Virtual machines and cloud environment
- MapReduce and Hadoop
- Cleaning, manipulating and extracting the required data
- Developing code for data analysis and manipulation
- Plays a major role in statistical analysis of data
|Machine Learning Engineer|
- Concepts of computer science and software engineering
- Data analysis and feature engineering
- Metrics involved in ML
- ML algorithm selection, and cross validation
- Math and Statistics
- Analyses and checks the suitability of an algorithm if it caters the needs of the current task
- Plays main role in deciding and selecting machine learning libraries for given task.
|Machine Learning Scientist|
Expert knowledge in:
- Robotics and Machine Learning
- Cognitive Science
- Mathematics and mathematical models
- Designs new models and algorithms of machine learning
- Researches intensively on machine learning and publishes their research papers.
Thus, gaining machine learning skills should be a task associated with clarity on the job role and of course the passion to learn them. As it is widely known, becoming a machine learning engineer is not a straightforward task like becoming a web developer or a tester.
Irrespective of the role, a learner is expected to have solid knowledge on data science. Besides, many other subjects are intricately intertwined in learning machine learning and for a learner, it requires a lot of patience and zeal to learn skills and build them up as they move ahead in their career.
The following diagram shows the machine learning skills that are in demand year after year: Image Source
In the coming sections, we would be discussing each of these skills in detail and how proficient you are expected to be in them.
Technical skills required to become ML Engineer
Becoming a machine learning engineer means preparing oneself to handle interesting and challenging tasks that would change the way humanity is experiencing things right now. It demands both technical and non-technical expertise. Firstly, let’s talk about the technical skills needed for a machine learning engineer. Here is a list of technical skills a machine learning engineer is expected to possess:
- Applied Mathematics
- Neural Network Architectures
- Data Modeling and Evaluation
- Advanced Signal Processing Techniques
- Natural Language Processing
- Audio and video Processing
- Reinforcement Learning
Learning machine learning skills is getting costlier day by day yet KnowledgeHut’s Data Science course fee has been providing the highest ROI (Return on Investment) for its learners. We recommend learners investigate the course curriculum before enrolling. Make sure the curriculum includes the skills we are going to delve deeper into below -
1. Applied Mathematics
Mathematics plays an important role in machine learning, and hence it is the first one on the list. If you wish to see yourself as a proven machine learning engineer, you ought to love math and be an expert in the following specializations of math.
- But first, let us understand why a machine learning engineer would need math at all? There are many scenarios where a machine learning engineer should depend on math. For example:
- Choosing the right algorithm that suits the final needs
- Understanding and working with parameters and their settings.
- Deciding on validation strategies
- Approximating the confidence intervals.
How much proficiency in Math does a machine learning engineer need to have?
It depends on the level at which a machine learning engineer works. The following diagram gives an idea about how important various concepts of math are for a machine learning enthusiast.
A) Linear algebra: 15%
B) Probability Theory and Statistics: 25%
C) Multivariate Calculus: 15%
D) Algorithms and Optimization: 15%
F) Other concepts: 10%
A) Linear Algebra
This concept plays a main role in machine learning. One has to be skilled in the following sub-topics of linear algebra:
- Principal Component Analysis (PCA), Singular Value Decomposition (SVD)
- Eigen decomposition of a matrix
- LU Decomposition
- QR Decomposition/Factorization
- Symmetric Matrices
- Orthogonalization & Orthonormalization
- Matrix Operations
- Eigenvalues & Eigenvectors
- Vector Spaces and Norms
B) Probability Theory and Statistics
The core aim of machine learning is to reduce the probability of error in the final output and decision making of the machine. Thus, it is no wonder that probability and statistics play a major role.
The following topics are important in these subjects:
- Probability Rules & Axioms
- Bayes’ Theorem
- Random Variables
- Variance and Expectation
- Conditional and Joint Distributions
- Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)
- Moment Generating Functions, Maximum Likelihood Estimation (MLE)
- Prior and Posterior
- Maximum a Posteriori Estimation (MAP)
- Sampling Methods.
In calculus, the following concepts have notable importance in machine learning:
- Integral Calculus
- Partial Derivatives,
- Vector-Values Functions
- Directional Gradient
- Hessian, Jacobian, Laplacian and Lagrangian Distributions.
D) Algorithms and Optimization
The scalability and the efficiency of computation of a machine learning algorithm depend on the chosen algorithm and optimization technique adopted. The following areas are important from this perspective:
- Data structures (Binary Trees, Hashing, Heap, Stack etc)
- Dynamic Programming
- Randomized & Sublinear Algorithm
- Gradient/Stochastic Descents
- Primal-Dual methods
E) Other Concepts
Besides, the ones mentioned above, other concepts of mathematics are also important for a learner of machine learning. They are given below:
- Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued, and Continuous Functions Limits, Cauchy Kernel, Fourier Transforms)
- Information Theory (Entropy, Information Gain)
- Function Spaces and Manifolds
2. Neural Network Architectures
Neural networks are the predefined set of algorithms for implementing machine learning tasks. They offer a class of models and play a key role in machine learning.
The following are the key reasons why a machine learning enthusiast needs to be skilled in neural networks:
- Neural networks let one understand how the human brain works and help to model and simulate an artificial one.
- Neural networks give a deeper insight into parallel computations and sequential computations
The following are the areas of neural networks that are important for machine learning:
- Convolutional Neural Networks
- Recurrent Neural Network
- Long/Short Term Memory Network (LSTM)
- Hopfield Networks
- Boltzmann Machine Network
- Deep Belief Network
- Deep Auto-encoders
Having an idea of physics definitely helps a machine learning engineer. It makes a difference in designing complex systems and is a skill that is a definite bonus for a machine learning enthusiast.
4. Data Modeling and Evaluation
Machine learning has to work with huge amounts of data and leverage them into predictive analytics. Data modeling and evaluation is important in working with such bulky volumes of data and estimating how good the final model is.
For this purpose, the following concepts are worth learnable for a machine learning engineer:
- Classification Accuracy
- Logarithmic Loss
- Confusion Matrix
- Area under Curve
- F1 Score
- Mean Absolute Error
- Mean Squared Error
5. Advanced Signal Processing Techniques
The crux of signal processing is to minimize noise and extract the best features of a given signal.
For this purpose, it uses certain concepts such as:
- convex/greedy optimization theory and algorithms
- spectral time-frequency analysis of signals
- Algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets, etc.
All these concepts find their application in machine learning as well.
6. Natural language processing
The importance of natural language processing in artificial intelligence and machine learning is not to be forgotten. Various libraries and techniques of natural language processing used in machine learning are listed here:
- Gensim and NLTK
- Sentiment analysis
7. Audio and Video Processing
This differs from natural language processing in the sense that we can apply audio and video processing on audio signals only. For achieving this, the following concepts are essential for a machine learning engineer:
- Fourier transforms
- Music theory
8. Reinforcement Learning
Though reinforcement learning plays a major role in learning and understanding deep learning and artificial intelligence, it is good for a beginner of machine learning to know the basic concepts of reinforcement learning.
Programming skills required to become ML Engineer
Machine learning, ultimately, is coding and feeding the code to the machines and getting them to do the tasks we intend them to do. As such, a machine learning engineer should have hands-on expertise in software programming and related concepts. Here is a list of programming skills a machine learning engineer is expected to have knowledge on:
- Computer Science Fundamentals and Programming
- Software Engineering and System Design
- Machine Learning Algorithms and Libraries
- Distributed computing
Let us look into each of these programming skills in detail now:
1. Computer Science Fundamentals and Programming
It is important that a machine learning engineer apply the concepts of computer science and programming correctly as the situation demands. The following concepts play an important role in machine learning and are a must on the list of the skillsets a machine learning engineer needs to have:
- Data structures (stacks, queues, multi-dimensional arrays, trees, graphs)
- Algorithms (searching, sorting, optimization, dynamic programming)
- Computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.)
- Computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.)
2. Software Engineering and System Design
Whatever a machine learning engineer does, ultimately it is a piece of software code – a beautiful conglomerate of many essential concepts and the one that is entirely different from coding in other software languages.
Hence, it is quintessential that a machine learning engineer have solid knowledge of the following areas of software programming and system design:
- Scaling algorithms with the size of data
- Basic best practices of software coding and design, such as requirement analysis, version control, and testing.
- Communicating with different modules and components of work using library calls, REST APIs and querying through databases.
- Best measures to avoid bottlenecks and designing the final product such that it is user-friendly.
3. Machine Learning Algorithms and Libraries
A machine learning engineer may need to work with multiple packages, libraries, algorithms as a part of day-to-day tasks. It is important that a machine learning engineer is well-versed with the following aspects of machine learning algorithms and libraries:
A thorough idea of various learning procedures including linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods.
- Sound knowledge in packages and APIs such as scikit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc.
- Expertise in models such as decision trees, nearest neighbor, neural net, support vector machine, and a knack for deciding which one fits the best.
- Deciding and choosing hyperparameters that affect the learning model and the outcome.
- Comfortable working with concepts such as gradient descent, convex optimization, quadratic programming, partial differential equations.
- Select an algorithm that yields the best performance from random forests, support vector machines (SVMs), Naive Bayes Classifiers, etc.
4 Distributed Computing
Working as a machine learning engineer means working with huge sets of data, not just focused on one isolated system, but spread among a cluster of systems. For this purpose, it is important that a machine learning engineer knows the concepts of distributed computing.
Most clusters and servers that machine learning engineers need to work are variants of Linux(Unix). Though randomly they work on Windows and Mac, more than half of the time, they need to work on Unix systems only. Hence having sound knowledge on Unix and Linux is a key skill to become a machine learning engineer.
Programming Languages for Machine Learning
Machine learning engineers need to code to train machines. Several programming languages can be used to do this. The list of programming languages that a machine learning expert should essentially know are as under:
- C, C++ and Java
- Spark and Hadoop
- R Programming
- Apache Kafka
- Weka Platform
In this section, let us know in detail why each of these programming languages is important for a machine learning engineer:
1. C, C++ and Java
These languages give essentials of programming and teach many concepts in a simple manner that form a foundation stone for working on complex programming patterns of machine learning. Knowledge of C++ helps to improve the speed of the program, while Java is needed to work with Hadoop and Hive, and other tools that are essential for a machine learning engineer.
2. Spark and Hadoop
Hadoop skills are needed for working in a distributed computing environment. Spark, a recent variant of Hadoop is gaining popularity among the machine learning tribe. It is a framework to implement machine learning on a large scale.
3. R Programming
R is a programming language built by statisticians specifically to work with programming that involves statistics. Many mathematical computations of machine learning are based on statistics; hence it is no wonder that a machine learning engineer needs to have sound knowledge in R programming.
4. Apache Kafka
Apache Kafka concepts such as Kafka Streams and KSQL play a major role in pre-processing of data in machine learning. Also, a sound knowledge of Apache Kafka lets a machine learning engineer to design solutions that are both multi-cloud based or hybrid cloud-based. Other concepts such as business information such as latency and model accuracy are also from Kafka and find use in Machine learning.
Of late, Python has become the unanimous programming language for machine learning. In fact, experts quote that humans communicate with machines through Python language.
Why Python is preferred for Machine Learning?
Python Programming Language has several key features and benefits that make it the monarch of programming languages for machine learning:
- It is an all-in-one purpose programming language that can do a lot more than deal with statistics.
- It is beginner-friendly and easy to learn.
- It boasts of rich libraries and APIs that solve various needs of machine learning pretty easily.
- Its productivity is higher than its other counterparts.
- It offers ease of integration and gets the workflow smoothly from the designing stage to the production stage.
There are various components of Python that make it the preferred language for machine learning. Such components are discussed below:
- Jupyter Notebook
1. Jupyter Notebook
Jupyter offers excellent computational environment for Python based data science applications. With the help of Jupyter notebook, a machine learning engineer can illustrate the flow of the process step-by-step very clearly.
NumPy or Numerical Python is one of the components of Python that allows the following operations of machine learning in a smooth way:
- Fourier transformation
- Linear algebraic operations
- Logical and numerical operations on arrays.
Of late, NumPy is gaining attention because it makes an excellent substitute to MATLAB, as it coordinates with Matplotlib and SciPy very smoothly.
Pandas is a Python library that offers various features for loading, manipulating, analysing, modeling and preparing data. It is entirely dedicated for data analysis and manipulation.
Built on NumPy, SciPy, and Matplotlib, it is an open-source library of Python. It offers excellent features and functionalities for major aspects of machine learning such as clustering, dimensionality reduction, model reduction, regression and classification.
TensorFlow is another framework of Python. It finds its usage in deep learning and having a knowledge of its libraries such as Keras, helps a machine learning engineer to move ahead confidently in their career.
6. Weka Platform
It is widely known that machine learning is a non-linear process that involves many iterations. Weka or Waikato Environment for Knowledge Analysis is a recent platform that is designed specifically designed for applied machine learning. This tool is also slowly gaining its popularity and thus is a must-include on the list of skills for a machine learning engineer.
This is a basic programming language that was used for simulation of various engineering models. Though not popularly used in machine learning, having sound knowledge in MATLAB lets one learn the other mentioned libraries of Python easily.
Soft skills or behavioural skills required to become ML engineer
Technical skills are relevant only when they are paired with good soft skills. And the machine learning profession is no exception to this rule. Here is a list of soft skills that a machine learning engineer should have:
- Domain knowledge
- Communication Skills
- Problem-solving skills
- Rapid prototyping
- Time management
- Love towards constant learning
Let us move ahead and discuss how each of these skills make a difference to a machine learning engineer.
1. Domain knowledge
Machine learning is such a subject that needs the best of its application in real-time. Choosing the best algorithm while solving a machine learning problem in your academia is far different from what you do in practice. Various aspects of business come into picture when you are a real-time machine learning engineer. Hence, a solid understanding of the business and domain of machine learning is of utmost importance to succeed as a good machine learning engineer.
2. Communication Skills
As a machine learning engineer, you need to communicate with offshore teams, clients and other business teams. Excellent communication skills are a must to boost your reputation and confidence and to bring up your work in front of peers.
3. Problem-solving skills
Machine learning is all about solving real time challenges. One must have good problem-solving skills and be able to weigh the pros and cons of the given problem and apply the best possible methods to solve it.
4. Rapid Prototyping
Choosing the correct learning method or the algorithm are signs of a machine learning engineer’s good prototyping skills. These skills would be a great saviour in real time as they would show a huge impact on budget and time taken for successfully completing a machine learning project.
5. Time management
Training a machine is not a cake-walk. It takes huge time and patience to train a machine. But it’s not always that machine learning engineers are allotted ample time for completing tasks. Hence, time management is an essential skill a machine learning professional should have to effectively deal with bottlenecks and deadlines.
6. Love towards constant learning
Since its inception, machine learning has witnessed massive change – both in the way it is implemented and in its final form. As we have seen in the previous section, technical and programming skills that are needed for machine learning are constantly evolving. Hence, to prove oneself a successful machine learning expert, it is very crucial that they have a zeal to update themselves – constantly!
Where does the journey begin?
The skills that one requires to begin their journey in machine learning are exactly what we have discussed in this post. The future for machine learning is undoubtedly bright with companies ready to offer millions of dollars as remuneration, irrespective of the country and the location.
Machine learning and deep learning will create a new set of hot jobs in the next five years. – Dave Waters
All it takes to have an amazing career in machine learning is a strong will to hone one’s skills and gain a solid knowledge of them. Investing in a Data Science course might be a great first step. You can find several options for Data Science course and training providers online but choosing one that offers an exhaustive curriculum and good instructors will be key to your learning experience. You can learn about KnowledgeHut’s Data Science courses here. And choose from the options that suit your needs, your skill level, available time, and the instructors leading the course.
All the best for an amazing career in machine learning!