So you’ve been hearing about machine learning and all its possibilities. But what are the actual skills you’ll need to learn or even enter a career in this field?
In this guide, I’ll take a look at the top 12 machine learning skills and how you can start learning them. One way to develop them is with a machine learning course, and we offer a fully-mentored one here at CareerFoundry.
Table of contents:
- What are machine learning skills?
- The 12 top machine learning skills you’ll need
- Final thoughts
- Machine learning skills FAQ
Find out more about these skills below!
What are machine learning skills?
Machine learning skills encompass a wide-ranging set of competencies rooted in statistics, mathematics, and computer science that enable machines to learn from data.
In broader terms, these skills involve creating algorithms that can process and analyze large datasets to identify patterns, make decisions, and predict outcomes without explicitly being programmed for each task.
Who can benefit from learning machine learning skills?
As technology continues to advance, the demand for individuals with machine learning skills is rapidly increasing across various industries like finance, healthcare, and marketing.
Some individuals that may benefit include:
- Students seeking a career in data science or artificial intelligence
- Professionals looking to upskill and enhance their current skillset
- Aspiring machine learning engineers
- Entrepreneurs interested in implementing machine learning algorithms into their business models
Whether you are a recent graduate or a seasoned professional looking to pivot into a new career path, developing these skills can open up numerous opportunities for growth and advancement in the job market.
The 12 top machine learning skills you’ll need
1. Probabilistic models and inference
One of the key skills you’ll need in machine learning is understanding and being able to use probabilistic statistical models and inference techniques.
These models allow machines to learn from data by assigning probabilities to different outcomes, making predictions based on those probabilities, and updating those probabilities as more data is collected.
Here are some examples of models:
- Bayesian networks
- Markov chains
- Hidden Markov models
- Gaussian processes
2. Programming expertise
Programming forms the backbone of any machine learning initiative, enabling the transformation of data into actionable insights.
Here’s a list of some popular machine learning programming languages:
- Python: As a leading programming language, Python boasts extensive libraries for machine learning, such as TensorFlow and scikit-learn.
- R: Predominantly used in statistics and visualizations, R is essential for data analysis and modeling.
- SQL: Command of SQL is vital for data extraction, manipulation, and querying from relational databases. Here’s a quick beginner’s guide to SQL to help you get started.
- Java: Java’s robustness and portability makes it suitable for high-performance machine learning applications.
- C/C++: For performance-critical applications, a thorough knowledge of these languages is indispensable.
- MATLAB: Familiarity with MATLAB is beneficial for prototyping algorithms and mathematical modeling.
It shouldn’t really come as a surprise that programming languages are one of the most common technical requirements of machine learning jobs out there.
3. Data cleaning
Data cleaning helps to maintain the integrity of machine learning models. Without meticulous preprocessing, datasets may lead to skewed insights and model inaccuracy.
Therefore, this makes it a key skill in machine learning.
It is typically an iterative process involving anomaly detection, correction, and validation to establish a reliable dataset. This foundation is critical for all subsequent machine learning efforts.
You’ll need to know how to use tools like Pandas in Python, and data wrangling packages in R, for efficient and effective data cleaning.
Only with clean data can machine learning algorithms reach their full potential, providing actionable and accurate predictions.
Our tutorial on data cleaning should provide you with a basic understanding if you’re new to this!
4. Feature engineering
Feature engineering is a pivotal step in enhancing machine learning models. This skill involves the creation and optimization of predictor variables, known as features, which can greatly influence the performance of predictive algorithms.
Feature engineering helps to construct predictive features that capture the underlying patterns in the data relevant to the predictive task at hand.
You’ll need to learn how to extract and select attributes that make machine learning algorithms work more effectively. This may involve aggregating data, engineering interactions between variables, and transforming variables to a more suitable scale or format.
Some examples of feature engineering include:
- Dimensionality reduction techniques such as principal component analysis (PCA) or singular value decomposition (SVD)
- Creating new features through feature selection methods like recursive feature elimination (RFE) and LASSO regression
- Transforming variables using techniques like standardization, normalization, or binning
In the realm of machine learning, features are the variables that feed into predictive models, thus their quality can make or break the success of the model. Selecting the right features is as critical as choosing the appropriate algorithm for the task.
5. TensorFlow and PyTorch proficiency
Next, we have TensorFlow and PyTorch, two of the most popular deep learning libraries used for complex machine learning tasks.
These machine learning tools are libraries that provide a high level of abstraction, making it easier to build sophisticated neural networks that can process large datasets.
You’ll need to learn how to code in these libraries to create high-performing models capable of handling techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
Keen on learning how to use these tools? CareerFoundry’s Machine Learning with Python specialization course is a good place to start. The course covers all the common machine learning models and algorithms with real-world applications using Keras and TensorFlow.
6. Natural language processing
Natural language processing (NLP) is a subset of machine learning that deals with developing algorithms and models to understand, manipulate, and generate human language.
This machine learning skill is becoming increasingly relevant as more companies adopt chatbots, virtual assistants, and other forms of artificial intelligence.
NLP involves techniques like:
- Text classification
- Sentiment analysis
- Named entity recognition
- Language translation
With the rise of voice interfaces and the expansion of natural language applications in various industries, NLP is an essential skill for any machine learning practitioner or enthusiast.
Tools such as tokenization, syntactic parsing, and semantic analysis are fundamental in NLP and form the backbone of machine understanding. Experience in these areas is highly sought-after.
7. Data modeling
Data modeling is an essential skill that involves creating abstract models to represent complex data relationships. It serves as the blueprint for data architecture, ensuring consistency and clarity in data analysis projects.
Effective data models require a keen understanding of the underlying business processes. They dictate how data will be stored, organized, and retrieved.
The use of Entity-Relationship (ER) diagrams and Unified Modeling Language (UML) to represent data structures and relationships is common in machine learning, too.
Moreover, data modeling is needed in predictive analytics, where it facilitates the training of machine learning algorithms.
8. Version control
Version control is commonly used in all use cases that involve writing code. It’s also another pivotal skill in machine learning workflows.
They provide a structural framework within which developers and data analysts can manage changes to scripts, notebooks, datasets, and models.
Moreover, these systems enable collaborative work environments where multiple versions of a project can progress simultaneously. Consequently, proficiency in such systems is fundamental to ensure consistency and efficiency in the life cycle of machine learning projects.
Professionals aspiring to specialize in machine learning should prioritize the mastery of version control systems, specifically Git.
Understanding branching mechanisms, merge conflicts, and commit histories is vital for maintaining a coherent code base. Furthermore, it’s a skillset that employers increasingly expect from candidates in the data analytics field.
Having version control practices ensures project scalability and recoverability, furnishing a safety net in the event of detrimental errors or data corruption.
9. Linear algebra and multivariate calculus
In order to understand the mathematical foundations of machine learning, having a strong grasp of linear algebra and multivariate calculus is crucial. Linear algebra helps in representing data and algorithms in matrix form, while multivariate calculus allows for optimization techniques to be applied to complex models.
Some examples include:
- Eigenvalues and eigenvectors
- Matrix operations (addition, multiplication, inversion)
- Gradient descent
10. Communication skills
In addition to all the technical skills mentioned above, another non-technical soft skill among machine learning practitioners is to have excellent communication skills.
As most machine learning projects involve cross-functional teams and collaboration, being able to effectively communicate complex concepts and findings to non-technical stakeholders is crucial.
Moreover, a good understanding of the business problem at hand and the ability to articulate how machine learning can solve it is necessary for successful project implementation.
11. Problem-solving skills
Problem-solving skills are an unavoidable trait that all data professionals should have, including machine learning practitioners.
This skill involves the ability to break down complex problems into smaller, more manageable components and develop a step-by-step approach to finding solutions.
In machine learning, this involves identifying and understanding different techniques and algorithms that can be applied to a given problem, evaluating their strengths and weaknesses, and selecting the most suitable one for the task at hand.
Effective problem-solving skills also involve being able to debug and troubleshoot code, as well as think creatively in finding solutions to unforeseen challenges that may arise during a project.
12. Continuous learning
The landscape of machine learning is perpetually changing and improving, rendering continuous education a non-negotiable for professionals.
Embracing a mindset of lifelong learning is critical to remain competitive and innovative in a field where new algorithms, tools, and best practices constantly emerge.
You should be expected to continuously expand your skill set and keep abreast of advancements in the industry. This includes staying up-to-date on new techniques, programming languages, and software tools that can enhance your machine learning capabilities.
Moreover, continual learning also involves regularly engaging with the broader data science community through events, conferences, workshops, and online forums.
Machine learning is an incredibly dynamic and rapidly evolving field. It requires a diverse set of skills that go beyond just coding and data analysis.
In addition to technical skills such as programming languages, statistics, and knowledge of algorithms, soft skills such as communication, problem-solving, and continual learning are also essential for success in this field.
Thinking of consolidating all your ML knowledge through a structured program? CareerFoundry’s Machine Learning with Python specialization course would be the perfect place to kick-start your learning.
For more related reading on machine learning, check out these links:
- 12 Great Machine Learning Projects to Get You Started in AI
- What’s the Difference Between Machine Learning and Deep Learning?
- 22 Most-Asked Machine Learning Interview Questions (and Answers!)
Machine learning skills FAQ
What are the skills required for AI?
The skills required for AI include programming languages such as Python, data analysis and manipulation, machine learning algorithms and techniques, natural language processing (NLP), data modeling, version control systems, mathematics (linear algebra and multivariate calculus), communication, problem-solving, and continual learning. Other valuable skills include knowledge of databases and cloud computing.
Is machine learning an IT skill?
Yes, machine learning is considered an IT skill as it involves the use of technology and programming to build predictive models and make data-driven decisions. However, it also requires a strong understanding of mathematics and statistics in order to effectively apply these techniques. Therefore, a combination of both technology-based and mathematical-based skills is typically used in machine learning.
How do I become proficient in machine learning?
To become proficient in machine learning, you’ll require a strong foundation in programming languages such as Python and R, as well as data analysis and manipulation techniques.
Additionally, gaining knowledge of machine learning algorithms and methods, natural language processing (NLP), data modeling, version control systems, mathematics (linear algebra and multivariate calculus), communication skills, problem-solving skills, and continual learning will play a big part in getting better at machine learning.