Machine learning is a branch of artificial intelligence that has been around long before generative AI catapulted into the social zeitgeist. ML technology, in essence, helps computers to gather insights from data without explicit programming. Because machine learning techniques around for a while, their applications are numerous.
With that said, below are three multibagger machine learning stocks to consider for April.
Nvidia (NVDA)
Shareholders of acclaimed graphics card maker, Nvidia (NASDAQ:NVDA), caught a major scare last week. NVDA share plummeted almost 14%. The chipmaker ended the week with its share price floating around $762/share, well below its $950/share high that the stock reached towards the end of the first quarter in late March. However, the shares are, again, defying expectations. As of the close of trading on Tuesday, Nvidia’s shares have risen 8.2% for the week, and there should be no doubt that the chipmaker could edge back up to its all-time high.
There should be no doubt about Nvidia’s prowess in machine learning and artificial intelligence technology. The chipmaker CUDA software offers a multiplicity of machine learning and data analytics libraries that can accelerate data science work while using Nvidia’s GPUs. Of course, Nvidia’s recently announced Blackwell are going to be game changers in both the ML and AI space.
Trading at 33.0x forward earnings, Nvidia’s stock looks like a great buy these days.
Advanced Micro Devices (AMD)
Advanced Micro Devices (NASDAQ:AMD) is the second semiconductor firm to make it onto this machine learning stocks list. The reasons are ultimately similar to what I have written for Nvidia. Over the past decade, AMD has solidified itself as a solid provider of GPUs that are not only powerful but also cost-efficient. Before the Covid-19 pandemic, AMD was generating only several billion in revenue per annum. As of their latest fiscal year, that figure has nearly quadrupled to around $23 billion.
In regard to the world of machine learning, AMD’s Radeon graphics are essential. If you have a Radeon-powered AMD graphics card, you will be able to develop desktop-level AI and machine learning workflows.
On top of AMD’s machine learning prowess, the chipmaker also has big ambitions for artificial intelligence. Back in December, AMD claimed its MI300x GPU was faster than anything Nvidia had on the market. These GPU accelerators will likely find their use in servers processing tons of data.
AMD shares fell 10% last week but are back on the rise again. Now may be the time for investors to reassess AMD’s potential.
Alphabet (GOOG, GOOGL)
It’s quite difficult to make a machine learning list without mentioning one of the companies providing the cloud infrastructure through which machine learning algorithms are deployed on a daily basis. Alphabet (NASDAQ:GOOG, NASDAQ:GOOGL) is the parent company of Google, which has its hands in everything from cloud computing, quantum computing, artificial intelligence and mobile handsets as well as operating systems.
Google Cloud continues to be where so many enterprises deploy machine learning and data analytics algorithms. The tech giant’s latest earnings report revealed that Cloud remained Google’s top growth engine. The fourth quarter of 2023 saw Google Cloud generate $9.2 billion in revenue, a sharp contrast to the $7.3 billion in the fourth quarter of 2022. Outside of providing the computing power to run machine learning algorithms. Google also provides a number of courses and certifications for aspiring machine learning engineers.
Investors should also be on the lookout for Google’s AI product Gemini, which could be an essential part of Google Cloud in the coming months and years.
Among the other companies on this list, GOOG shares trade the cheapest at 23.4x forward earnings. Shares are sitting at a sweet spot; it’s time for investors to ask themselves whether or not to invest now.
On the date of publication, Tyrik Torres did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.
Tyrik Torres has been studying and participating in financial markets since he was in college, and he has particular passion for helping people understand complex systems. His areas of expertise are semiconductor and enterprise software equities. He has work experience in both investing (public and private markets) and investment banking.