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Switzerland Machine Learning: A Small Guide

By gswardman June 6, 2022

The global machine learning industry is set to grow to more than $110 billion by 2027. If you are considering an ML job in Switzerland, now would be the right time to start.

Switzerland is among the many countries in Europe experiencing a shortage in machine learning and artificial intelligence expertise. However, this situation is expected to change soon as the demand for these professionals surges, and many companies adopt advanced technology.

Often artificial intelligence is used interchangeably with machine learning, but they are different. Read on for more insights on machine learning and the differences between ML and AI.

What is Machine Learning?

Machine learning involves using mathematical data models to ensure computers learn without instruction. It is considered a subgroup of artificial intelligence, and it relies on algorithms to establish patterns within data.

The designs are then used to build a data model capable of making predictions. Machine learning results are more precise with increased experience and data. Machine learning is versatile, making it an ideal choice in areas where data changes consistently, coding solutions is impossible, or tasks and requests shift regularly.

Which Country is Best for Machine Learning?

Demand for machine learning and artificial intelligence professionals has been rising tremendously across the globe amid fears that these technologies will render humans jobless. Global organizations have been hiring at a high rate, with technological advancement triggering more positions because organizations require skilled AI professionals to build and maintain various applications. Are you a machine learning expert seeking employment? Here are some of the best countries for machine learning.

  • United States
  • Canada
  • Europe
  • China
  • India
  • Japan

Which Field is Best for Machine Learning?

Machine learning is a multiple disciplinary field, and understanding it is not easy. Beginners may experience difficulties telling the difference between machine learning and affiliated fields or data mining and artificial intelligence. Here are some of the fields that are ideal for machine learning.

·       Foundations

Machine learning is based on the computer science and mathematics field. Machine learning strategies are best illustrated with matric and linear algebra, while their habits are best understood through statistics and probability tools.

·       Progenitors

Machine learning strategies come in handy in applied computer science fields like data science and data mining. Further, related AI fields analyze intelligent methods while learning from data and their surroundings.

·       Artificial Intelligence

Switzerland Machine Learning

Artificial intelligence is the construction and study of computational systems that execute tasks that humans can perform or things thought to be intelligent. For instance, humans can navigate an area and understand things they see, and comprehend the language they read and hear. Subgroups of natural language processing, computer vision, and robotics are available.

·       Data Science

The data science field focuses on solving complex problems with data. It is a subgroup of computer science. Machine learning offers a set of essential tools used in data science to facilitate the teaching of relationships in data to make predictions. It is related to other computers science fields like convex optimization and operations research, and mathematics such as information and decision theory.

Is Machine Learning in High Demand?

Are you looking for a secure and solid career? Being a machine learning engineer would be ideal. According to a recent survey, machine learning engineering is one of Zug, Switzerland’s most sought-after artificial intelligence jobs.

Artificial intelligence and machine learning job alerts have increased by more than 70% in the last four years and are expected to grow. A job in the machine learning field will be a great choice if you are looking for a well-paying career that will stand the test of time.

Many industries like financial services, eCommerce, marketing, education, and healthcare use machine learning and AI. Besides high pay, there are numerous benefits to enjoy in machine learning while executing tasks that revolutionize the world.

According to some of the leading employment websites in the US, information technology, data analytics, data engineering, and computer vision engineer startup jobs are in high demand, with employment expected to advance by 11% by 2029.

Is it Worth Learning AI and ML?

Studying machine learning and artificial intelligence exposes you to numerous opportunities. Primary learners will understand the tools and systems that you encounter every day.

Extensive lessons will help you know state-of-the-art artificial intelligence applications such as cybersecurity, self-driving cars, image recognition, IBM’s Watson, and face recognition. Further, learning artificial intelligence helps you pursue a senior software engineer working with quantum artificial intelligence, human-machine interfaces, and neural networks.

You may also work as full-time hardware and research engineer building home attendant robots or electronic parking aids. You can also become a software developer working for big companies like Facebook or Squirro to evaluate and process big data.

What does a Machine Learning Engineer do?

What does a Machine Learning Engineer do

Machine learning engineers are skilled programmers who research, develop, and design self-sustaining software to facilitate the automation of predictive models. These professionals develop AI systems that use massive data sets to produce and create algorithms that can learn and, in the end, make predictions.

To build machine learning systems, an engineer should evaluate, scan, organize data, perform tests, and improve the learning process to develop highly functional machine learning models and pipelines. Often, the functions of an ML engineer vary based on the available data science team and the company size. However, the general roles of a machine learning engineer include:

  • Outlining, building, and analyzing machine learning schemes, models, and systems
  • Conducting statistical analysis and leveraging results to enhance models
  • Searching and choosing relevant data sets
  • Training and retraining machine learning models and systems accordingly
  • Studying, modifying, and changing data science blueprints
  • Envisioning data to achieve deeper insights
  • Determining data distribution differences that can influence model functionality in real-world circumstances
  • Establishing when to integrate your findings into business decisions
  • Evaluating machine learning use cases and rating them based on their success potential
  • Substantiating data quality or certifying it through data cleaning
  • Improving existing machine learning libraries and frameworks

What is the Difference Between AI and ML?

Difference Between AI and ML

Here are some of the differences between AI and ML you should beware of.

While artificial intelligence focuses on tasks requiring human intelligence, machine learning is a subgroup of AI that solves specific tasks through data learning and making predictions. As a result, every machine learning involves artificial intelligence, but not every AI requires machine learning.

The core objective of artificial intelligence is to develop human-like smart computer systems to facilitate solutions for complex problems. Machine Learning, on the other hand, ensures machines learn from data to help them generate accurate results.

Deep learning and machine learning are two core subgroups of artificial intelligence, while deep learning is the core subgroup in machine learning.

Machine learning has a restricted scope, while artificial intelligence has a broad range.

Which Country has the most Machine Learning Jobs?

Here are some countries with a high demand for machine learning and artificial intelligence professionals.

·       United States

The USA is among the leading counties with numerous high-paying machine learning and artificial intelligence jobs. According to a study conducted by Pysa, the top 20 artificial intelligence firms spend more than $650 million to hire AI experts. The study revealed over 10,000 vacant positions at some of the top employers in the country.

The yearly investment among the leading 20 employers looking for artificial intelligence professionals is $33,292,647. This indicates that organizations that rely heavily upon technology and skilled personnel are set for success.

·       Canada

Canada is fast becoming the preferred place for artificial intelligence professionals. Reports from various employment websites reveal a 1,069% increase in artificial intelligence jobs in Canada since 2013.

The growth rate is considered higher than in the US and UK combined. Apart from some of the top companies in the country, global technology companies like Google, Facebook, and Microsoft establish research labs in Canada and recruit Canadian talents to lead the new AI branches.

·       Europe

There is an inadequacy of ML and AI skills in nearly all countries in Europe. However, numerous jobs are waiting for the right talent. Machine learning and artificial intelligence jobs pay way more than the average salary in the UK.

ML engineers earn a minimum of £54,617 annually, while data scientists make £56,385 per year. Following the increased demand, European countries like Switzerland have recruited experts from outside the European Union to fill the rising gap.

Finland remains the only European Union country to implement an artificial intelligence approach at the government level. As a result, digital businesses enjoy legal framework and government support to develop and conduct innovations. To counter the skill deficiency, the digital game sector in Finland is recruiting digital specialists from China, Russia, India, and the USA.

·       Germany

Germany is likely to face a 3 million skilled worker shortage if a recently released research is anything to go by. Germany’s technology firms seem not to worry about job losses triggered by artificial intelligence. Even though advancements in AI enable the companies to substitute the most menial jobs, they will still require experts to service and manage the new machines.

Skill shortage is one of the biggest hurdles blocking the development and implementation of ML and AI technology in Germany. A significant percentage of the organizations opine that finding skilled workers has barred them from financing innovation ventures.

Germany has attracted over 60,000 highly-talented workers since 2012 under the European Union’s Blue Card program. The country will require an increased inflow to compensate for the shortage of skilled workforce.

What are Learning Resources for ML?

Breaking into the ML field is not a one-day process. However, numerous resources available online have given beginners a head start. Today, you don’t need a degree in computer science to understand machine learning technology.

Worth mentioning is that there are various paths you can pursue to navigate through AI and ML. For example, through the top-down approach, you can briefly learn high-level components focusing on implementations through frameworks like Pytorch and Tensorflow.

Another approach involves focusing on the statistics, algorithms, and math behind ML. the latter approach may be highly beneficial for people seeking to pursue the research aspect of ML and AI. Identify the method that best suits your needs before starting. Here are some of the best resources for machine learning.

·       CS109A by Harvard University

CS109A is a GitHub repository comprising PDF Jupyter Notebooks for the lab’s aspect and PDF lectures for the theoretical section. It is a complete course available free of charge and developed by one of the leading universities in the world. It comes with 13 labs and more than 20 lectures. It is an excellent place to begin your machine learning journey. There is no price to pay for CS109A, which many people have termed a masterpiece.

·       Machine Learning by Andrew Ng on Coursera

According to experts, Machine Learning by Andrew Ng on Coursera is nearly the principle of navigating the industry. So far, more than 120,000 users have graded it, with the average grade being 4.9 on a scale of 5. While the videos are available in low resolution, the content is of excellent quality.

This course can be intensive, and learners should strive to understand their weaknesses and work on improving them. The labs come in Octave, a free language alternative to MatLab. According to experts, machine learning is best executed using R or Python.

As a result, the use of Octave in this course may not be the best decision. Watching videos lectures on this course is free, but you will need to pay approximately $100 to obtain your certificate of completion.

·       Python for Data Science and Machine Learning Bootcamp

Python for Data Science is an online course that runs for approximately 22 hours. You may need to rewatch it several times to be entirely conversant with it. It takes learners from Python refresher and library introduction before engaging in machine learning algorithms.

Learners who have completed the course say it’s a great approach to introduce learners to machine learning. However, the algorithms have not been comprehensively covered. Even though you can still use them to solve some problems, you will struggle to identify the best algorithm to use at any particular time and how to adjust them accordingly.

The course is not math-intensive but features Python implementation from different libraries. According to the author of this course, learners should use the book Introduction to Statistical Learning during this course to acquire a robust theoretical basis. Often, the system is available for the discounted amount of $9.99.

Books

Books are some of the best resources for machine learning. Reading helps you reinforce your theoretical knowledge of the approach you use in machine learning projects. Here are some of the best books you should consider.

·       The Hundred-Page Machine Learning Book by Andriy Burkov

While this book is short, it contains lots of insights. The author has compressed all crucial machine learning and artificial intelligence points into this 138-page book.

·       Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow 2.0 Book by Aurelien Geron — O’Reilly

This book is an ideal alternative to the Deep Learning and Machine Learning specializations by deeplearning.ai. It features ideal descriptions, and each concept comes with a great code to test side by side. Readers can also access the book’s open-sourced code by following the link below. https://github.com/ageron/handson-ml2

Machine Learning Engineer Switzerland Salary

Machine learning engineers in Switzerland earn an average of CHF 101,990 annually in eth Zurich area of Switzerland. The minimum extra cash compensation for machine learning engineers in Zurich and other locations like Basel, Bern, Winterthur, and Lausanne Swiss is CHF 25,230.

10 Examples of Machine Learning Jobs Geneva

Hiring managers cannot overlook the impact of a powerful machine learning job description in attracting the right talent cannot be underestimated. Here are some of the best ML and related jobs in Geneva.

  1.  

Machine Learning Engineer II

  1.  

Machine Learning Engineer

  1. Vice President, Machine Learning Science
  2.  

Data Scientist Intern - QuantumBlack

5.

Data Scientist Intern - QuantumBlack

6.

Machine Learning Scientist II

  1.  

Data Scientist Engineers

  1. Senior Data Scientist (Image Processing)
  2.  

Internship - Machine Learning Specialist

  1.  

R & D- Security Scientist - Machine Learning

Finally

Are you an employer seeking the best machine learning talent in Switzerland? Book an expert for ML today and get your project up and running in record time.