Most modern products and tools have at least some artificial intelligence or machine-learning element. From personalized search results to photo-identifying tools, experts use various tools and techniques evolving at a blistering pace to build those algorithms.
As a result, it’s now one of the hottest and highest-paid jobs in tech. Some roles might require high-level degrees to work on the most cutting-edge tech, but experience with a few tools and mathematics can still get candidates jobs.
That includes several different roles, like data scientists that analyze massive datasets for trends to inform company decisions. They earn $122,431, according to a sampling of data based on H-1B visa applications from 2021. The US Citizenship and Immigration Services office requires companies to disclose salary information on H-1B visa applications when hiring international workers. The data doesn’t include additional compensation like cash bonuses or stock awards.
Data scientists for production models earn $143,960, according to the H-1B data. Advanced data scientists generally build machine-learning models that work within products, like personalization algorithms. Or workers can pursue the emerging field of machine-learning engineers, which put the models into production and fine-tune them. They earn $159,775, according to the H-1B data.
At the top of the pay scale, research scientists work at the cutting edge of AI, developing new techniques for complex models. These employees often have extensive experience, education, or both, and they earn $161,944, according to H-1B data.
Because of the wide range of positions available, breaking into machine learning can range from learning basic data-science techniques to more advanced deep-learning skills. The field is complex and evolving, but workers can start with a few fundamental skills.
Scikit-learn helps people comprehend the basics of machine learning; and it’s easy to use. Some experience in the programming language Python and a basic understanding of statistics will let users to do a lot.
There’s an extensive library of standard machine-learning tools available through Scikit-learn. Companies use it for models to bucket customers into groups or predict which customers are about to leave.
There isn’t a certificate for expertise in Scikit-learn because it’s a fundamental part of the field. But many core machine-learning and data-science certificates like those Amazon and Microsoft offer will dig into Scikit-learn.
Machine-learning experts need a basic understanding of statistics and probability. Modern machine-learning algorithms rely on those methodologies to help predict trends.
SciPy provides data scientists and machine-learning experts with tools for managing statistical analysis. That includes the tests they use to understand if the trends they see are significant or are flukes, a methodology called hypothesis testing.
There isn’t a certificate for understanding the statistical underpinnings of machine learning, but there are courses that cover the basics on learning platforms like Udemy.
Intermediate Python knowledge can get workers far in data science, but moving on to complex machine-learning problems requires understanding more intricate parts of the programming language.
Machine-learning experts need to know how to use Python-based packages like NumPy, a way to run algorithms on extensive datasets with many data types. For example, NumPy can help predict what a user might do with a product based on thousands of different data points.
While Python is a preferred language, many companies and institutions use another statistical-programming language, like R, instead. Most packages that work in Python also work in R.
The Python Institute offers certifications in more advanced Python skills.
Most statistical analysis in Python will use a tool called Pandas, which lets programmers manipulate large datasets. Programmers can arrange data in columns and rows, though each entry can contain any data type.
Pandas produces graphic representations of data with visualization platforms like Matplotlib or Seaborn. That gives machine-learning experts a way to see any trends in the data and present it internally if needed.
There isn’t a certification for understanding advanced Pandas usage. It’s typically wrapped up in core certificate programs and data-science courses like those the learning platform DataCamp offers.
Google brought AI to a more general audience in 2007 when it launched the open-source software platform TensorFlow. While TensorFlow is still ubiquitous, the open-source platform PyTorch has quickly emerged as a favorite among machine-learning experts and enthusiasts.
Machine-learning enthusiasts looking to break into AI should have a strong understanding of the strengths and weaknesses of these frameworks.
There isn’t a PyTorch certification, though Facebook AI runs a free course in Udemy for PyTorch. There is also a developer certification for TensorFlow.
Machine-learning frameworks like PyTorch and TensorFlow are both highly flexible languages. But there are tools that work with the platforms to reduce the complexity and focus specifically on problems like deep learning.
Keras is one of the most popular frameworks that sits on top of TensorFlow, opening up more complex techniques for a broader audience. Users can create deep-learning models with the framework.
Udemy hosts a course to learn both TensorFlow and Keras.
Most machine-learning analysis doesn’t happen on a laptop. Instead, it will occur on a cloud server, if not many of them.
Some machine-learning cloud tools like Google Colab are readily available, especially using TensorFlow. But many companies may be tied to Amazon Web Services or Microsoft Azure, and knowledge of those AI tools will be necessary to handle immense amounts of data.
Certifications like those for Amazon Web Services’ machine-learning specialty cover how to handle those problems. Microsoft also offers the Azure data-science associate certification.
Data scientists and machine-learning experts still have to access enormous amounts of data. That requires expertise in data lakes or warehouses, where that data is managed and stored.
Snowflake and Databricks both run data-management platforms. Large data frameworks like Spark operate with Python and are generally required for any machine-learning team building active models within products.
Databricks runs a certification program for Spark, while Cloudera has a certification for both Spark and its competitor Hadoop.
Natural-language processing and computer vision are two of the most prominent sub-fields of machine learning. Computer vision focuses on how computers understand images and videos, while natural-language processing is how computers understand human language.
It’s helpful to understand exactly what’s happening with the algorithms when working with those fields.
Additionally, these problems generally involve transformers, a common mathematical process behind modern machine-learning problems. While it’s not necessary to understand every individual step of how transformers work, machine-learning experts should understand some of the basics.
There is a variety of courses on Udemy and other platforms that explain natural-language processing, computer vision, transformers, and other common machine-learning problems.
There is a variety of emerging tools that are increasingly popular with machine-learning experts.
The open-source tool FEAST gives machine-learning experts a way to save some of their most extensive computations, saving a lot of time and money in cloud-computing costs.
Tools like Weights & Biases help machine-learning experts track any experiments running to see if they can understand user behavior and trends. Additionally, Hugging Face has become a go-to platform for machine-learning experts to find off-the-shelf algorithms.
Finally, machine-learning app stores like Hugging Face’s Gradio and Snowflake’s Streamlit give machine-learning experts a way to build more shareable and accessible machine-learning tools.
The field is rapidly expanding, with dozens of startups trying to build pieces of the machine-learning process. Most of them offer tutorials on their websites to help users understand how they work.
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