The 5 Skills You Need to Start Machine Learning The 5 Skills You Need to Start Machine Learning
With any new skill, hobby, or career path, you likely have more questions than answers. How do I get started? What... The 5 Skills You Need to Start Machine Learning

With any new skill, hobby, or career path, you likely have more questions than answers. How do I get started? What skills do I need to focus on first? What sources do I trust to learn all of this? Data science and machine learning are no different. While each field under the umbrella of data science has its own unique set of skills, there are a few basics that are universal. Here are the five skills you need to get started with data science and machine learning.

1. Linear Algebra

Time to bust out the high school and college textbooks again, because you’ll be needing algebra if you want to excel in data science. Linear algebra involves a lot of vectors and matrices, which are useful in representing large amounts of data – something you’ll see often in your life as a data scientist. Linear algebra is a core skill for deep learning, if you choose to go down that path.

2. Statistics & Probability

Statistics involves the collection, analysis, interpretation, presentation, and organization of data. Sound familiar? There are lots of similarities between statistics and data science, such as examining probability, bayesian thinking, experimental design, regression, and so on.

3. Calculus

Uh oh, more math. While you may not need to go back and relearn everything about calculus from when you were 16, you need to understand the core concepts at least. This includes knowing more about gradient descent, linear regression, limits & derivatives, and so on.

4. Computer Science

Computer science has been around for quite some time, with a lot of theories and practices making their way over to data science. Many computer scientists make career transitions into data science, so there are plenty of parallels between the two. Core knowledge includes data structures, trees & graphs, lists & dictionaries, and more important skills.

5. A Coding Language

This is where it gets a bit fuzzy since there are debates about what coding language is best for data science. The most common two are Python & R, each with their own strengths and weaknesses.  Python is versatile and often used in computer science as well, while R is popular for data analysis. There are many libraries, frameworks, and platforms that use either R or Python, so knowing one language won’t limit you.

Bonus Skills on How to Start Machine Learning: Communication and Business Knowledge

It’s not all numbers, charts, and graphs. The best data scientists will also know soft, non-technical skills in addition to their coding and programming toolkit when learning how to start machine learning. You’ll likely be working with a variety of people, so it’s important to know how to communicate across departments – including verbal communication and data presentation – as well as knowing some basics of business to understand what a customer or client may want. 

Learn everything with Ai+

That’s a lot to learn to get started with machine learning. Luckily, Ai+ has a 14-part Bootcamp starting December 3rd that covers everything above and more. The Foundations for Machine Learning: Mini Bootcamp with Jon Krohn (Chief Data Scientist at Untapt, and author of Deep Learning Illustrated) will cover everything you need to know to get started with data science across 14 modules, all live and in real-time so you’ll get the hands-on experience that you won’t get anywhere else.

December 3, 10, and 17: Linear Algebra

Get the foundations of linear algebra and data science across three sessions. You’ll learn about linear algebra, common Tensor operations, matrices, and how these all apply to machine learning.

January 13 and 27, February 10 and 24: Calculus

Gain a semester’s worth of calculus knowledge in just a few sessions. John will cover the basics of calculus, computing derivatives with differentiation, automatic differentiation with Pytorch and TensorFlow, gradients applied to machine learning, and integrals.

March 2021: Probability and Statistics

Here’s where the meat of machine learning starts to come into play. See how probability plays into machine learning, learn about regressions, Bayesian statistics, the PyMC3 Notebook, and other tools you’ll likely be using daily. 

Spring 2021: Computer Science

It’s time for data structures, common algorithms, and other “day in the life of a data scientist” skills. You’ll learn about decision trees, random forests, gradient boosting, machine learning optimization, gradient descent, and other common topics under the umbrella of machine learning. At the end of this session, Jon will even dive into deep learning a bit, covering topics like PyTorch and neural networks. 

In just 14 sessions, you’ll go from knowing a bit of math and python to knowing how to truly put “machine learning” on your resume. This mini bootcamp is only $549, much cheaper than any course or college degree, and much more thorough than any generic video series you’ll find online. It’s hands-on, comprehensive, and will finally lead to you becoming a machine learning pro. Learn more about the bootcamp and register here!



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