Top Machine Learning courses in 2024
Bob Armstrong
20 May, 2024
The use of Machine Learning (ML) in finance has been in the news recently, with credit card issuers using Deep Learning to build fraud detection.
A branch of Artificial Intelligence (AI), ML uses algorithms to train AI to imitate the way humans learn by analysing massive amounts of data. Machine Learning models get better with time, as their predictions become more accurate.
Interested in upskilling into this fascinating, lucrative field?
Let's take a look at the top 10 Machine Learning certification courses in 2024.
10. Google Cloud Fundamentals: Big Data and Machine Learning
Google is at the forefront of AI and ML innovation and this certification is excellent.
If you have a background in statistics and experience with data modeling, Python, and SQL, this course can introduce you to the big data and ML capabilities of Google Cloud Platform.
This course helps you develop the knowledge and skills you need to get started with Google Cloud Platform. The course is ideal for Data Analysts, Business Analysts, Data Scientists, and other professionals who design data pipelines, maintain statistical models, and perform similar tasks.
At Firebrand, this course takes only one day.
9. TensorFlow Developer Certificate
If you're a Data Scientist or a Developer and want to certify your Machine Learning skills, this Developer Certificate created by TensorFlow could be just what you need.
This comprehensive course takes you through the fundamentals of Deep Learning and creating Deep Learning models using TensorFlow and Keras.
The course also covers JavaScript, Natural Language Processing using TensorFlow, and classifying images using convolutions with TensorFlow. At the end of the course, you can take the official exam and return to work certified. Find out more.
8. Microsoft Applied Skills: Build a natural language processing solution with Azure AI Language (AI-3003)
This course is part of a new initiative called Microsoft Applied Skills—courses that help you build highly specialised skills in a hands-on, practical manner.
AI-3003 is ideal for AI Engineers and also helps you prepare for the Microsoft AI-102 exam, Designing and Implementing a Microsoft Azure AI Solution.
The course shows you how Natural Language Processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. In just 1 day, it helps you develop the skills to create an NLP solution by using Azure AI Language. At the end of the course, you can complete a Microsoft Applied Skills assessment lab and earn your official credential. Find out more.
7. IBM Python for Data Science and AI
This course could be very useful for those with experience in Python and an interest in ML. Developed by IBM, the course teaches you to work with data in Python.
The course takes you through Python fundamentals, Data Structures, Python Programming, APIs, Data Collection, and more.
This course is ideal for those who would like to learn Python on the IBM platform; there are no prerequisites to attend. See the full course spec.
6. Google Professional Machine Learning Engineer certification
If you have experience with Google Cloud products and solutions, Google's Professional Machine Learning Engineer course could be a logical step up.
This comprehensive course walks you through all aspects of model architecture, data pipeline, interaction, and metric interpretations, as well as infrastructure management, data engineering, security, evaluating models, and augmentation.
The course helps you develop the skills you need to design, build, and implement the production of ML models to solve business problems.
There are no prerequisites to attend and, at Firebrand, it takes only 2 days. Find out more.
5. Databricks Certified Associate ML Practitioner for Apache Spark 2.4
This is a more specialised course for those with experience in ML, the Apache Spark 2.4 ML Library and workflows, and a working knowledge of Python.
This course helps you develop the knowledge and skills you need to apply Machine Learning techniques in the Spark ML Library, including supervised and unsupervised learning, clustering, model tuning, evaluation, interpretation, and more.
At the end of this course, you can sit the official Databricks exam and return to work certified. Find out more.
4. Amazon Web Services (AWS) Machine Learning Specialty (MLS-C01)
For those who use the AWS Cloud, have some experience with Machine Learning, and would like to specialise further, MLS-C01 is a great course to take.
This course teaches you to design, implement, deploy, and maintain Machine Learning (ML) models and solutions to support business growth and deter external threats.
It also takes you through:
- Feature engineering
- Analysing and visualising data for machine learning
- Hyperparameter optimisation
At the end of the course, you can sit the official AWS Certified Machine Learning Specialty (MLS-C01) exam and return to work certified. Find out more.
3. Intro to TensorFlow for Deep Learning
Similar to the TensorFlow Developer Certificate at number 9, this course takes you through the foundations of ML and Deep Learning and how they are applied to the TensorFlow platform.
This course can be very useful for Software Developers; as suited to an intro course, there are fewer prerequisites than for the Developer Certificate, with at least 40 hours of programming and a knowledge of probability and statistics.
Intro to TensorFlow for Deep Learning helps you develop the skills to build Deep Learning models and use TensorFlow models on mobile, in the cloud, and in browsers.
Topics include:
- Image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks
- Applying neural networks to solve natural language processing problems using TensorFlow
- Strategies to prevent overfitting, including augmentation and dropouts
2. Google Cloud Certified Professional Data Engineer
At number 2, we come to another specialised course. The Google Cloud Professional Data Engineer certification is aimed at experienced developers who are responsible for managing big data transformations. It's recommended that learners have at least one year of experience specifically in the Google Cloud Platform.
This course trains you to design and build data processing systems on the Google Cloud Platform (GCP) and enable data-driven decision-making by collecting, transforming and publishing data.
You'll also learn to:
- Leverage, deploy, and train pre-existing ML models
- Design for data and application portability
- Operationalise ML models
At Firebrand, this course takes only 3 days. Find out more.
1. Microsoft Azure Data Engineer Associate (DP-203)
The final course on our list helps you certify your skills as a Data Engineer for the popular Microsoft Azure platform. This course is at the intersection of Data Science and Machine Learning. To take it, you should have experience with parallel processing, data architecture patterns, and languages such as SQL, Python, or Scala.
This advanced course teaches you to implement, manage, and deploy data solutions on Microsoft Azure, ensuring that data pipelines and data stores that feed into ML are high-performing, efficient, organised, and reliable. The course also takes you through querying data, securing data, and managing users in the lake using Azure Synapse serverless SQL pools and transforming data with DataFrames in Apache Spark Pools in Azure Synapse Analytics.
At Firebrand, we are proud to have been awarded the Microsoft Solutions Partner for Data & AI (Azure) badge in recognition of our expertise in delivering quality solutions in this specialised business area.
This course takes only 3 days and ends with the official Microsoft DP-203 exam. Find out more.
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