In today’s ever-changing technological world, machine learning (ML) emerges as a significant force, transforming industrial operations and the future of AI. The ML market size is projected to reach the value of US $33.4 billion by 2026 (source: Global Tech Council). So, there will be a higher demand for skilled ML experts than ever before. A person requires an essential skill set to succeed in the dynamic ML sector. The top AI ML certification programs provide hands-on experiences and help develop in-demand skills.

Here are the best ML courses you can enroll in to advance your knowledge and jumpstart your career in the machine learning field.

1. CAIC™ (Certified Artificial Intelligence Consultant)
It is the most influential certification internationally to supercharge skills and develop world-class AI and ML capabilities. This AI certifications provides exposure to the modern tools, techniques, frameworks, algorithms, and architecture used in artificial intelligence and machine learning to solve real-world business problems.

Provider: USAII® (United States Artificial Intelligence Institute)

Who can enroll: AI professionals who dream of more challenging and demanding artificial intelligence and machine learning consultant roles.
What you will learn: Understand data and how machine learning can be used for business functions. For example, customer analytics, securing sensitive data, fraud prevention with Cloud AI, privacy, debugging, etc.

USAII® offers the best ML certifications with flexible payment options and an easier refund policy. All the certification programs are self-paced, which means you get the ease to study at your own learning speed. You will even experience the most suitable examination policies to get the desired certification.

USAII® also provides learners with a resource center which includes e-learning and study books. All e-books consist of relevant and advanced topics such as ML, AI, NLP, Robotics, Reinforcement Learning, etc.


2. Machine Learning Crash Course — Google AI
This course provides a practical introduction to ML and comprises a series of lessons with video lectures, hands-on practice exercises, and real-world case studies. Each section of the course includes an interactive Jupyter Notebook hosted on Google Colab.

Provider: Google AI

Who can enroll: Individuals who have tinkered with machine learning but want to cover all the basics.

What you will learn: Topics important to solve machine learning problems quickly. This course discusses multiple nuances of ML such as model performance metrics, ML engineering, neural networks, linear and logistic regression, etc.

3. Machine Learning for All
This course is designed to make ML accessible to every person no matter whether they have backgrounds in math or programming. You will work on a hands-on ML project using user-friendly tools built by Goldsmiths, University of London.

Provider: University of London

Who can enroll: Technical and non-technical people who want to learn basic ML concepts and techniques.

What you will learn: Fundamentals of how modern ML technologies work, predict how data affects ML outcomes, and how to use a non-programming-based platform to train ML modules using datasets.

4. Machine Learning Specialization
This 3-course program is a collaboration between DeepLearning.AI and Stanford University. It provides a complete overview of modern ML techniques like supervised and unsupervised learning. By the end of this course, you will develop a good knowledge of crucial machine learning concepts.

Provider: DeepLearning.AI, Stanford University

Who can enroll: AI professionals who are just getting started with AI or beginners who want to get important knowledge of how ML models work and real-world experience developing Python ML models.

What you will learn: Understand fundamental AI concepts and build practical ML skills such as linear regression, logistic regression, recommender systems, artificial neural networks, and decision trees. You will learn how to use ML techniques to develop real-world AI applications.

5. Supervised Machine Learning: Regression and Classification
This is a beginner-level course with three modules. It is taught by Andrew Ng, a well-recognized professor at Stanford University. It is among the top machine learning certifications programs that provide the knowledge of important concepts and practical know-how to quickly apply ML to complex real-world problems.

You’ll get a wide introduction to supervised and unsupervised learning, and some best practices used in Silicon Valley for ML and AI innovation. It is an essential part of the Machine Learning Specialization so when you apply for this course, you will also be enrolled in this specialization.

Provider: Stanford University, DeepLearning.AI

Who can enroll: Newcomers who want to develop a good base knowledge of ML.

What you will learn: Develop ML models in Python using the best ML libraries and train supervised ML models for prediction and binary classification tasks.

Conclusion
According to Gartner, the salary of an ML engineer is much higher than what is offered to other profiles. So, if you are dreaming of a rewarding career in the machine learning field, then apply for the top ML certifications. These courses are important for learning how to use statistical models and algorithms to develop computer systems that depend on self-generated trial-and-error feedback to do tasks. Thus, choose the most suitable machine learning certification for your career objective considering time duration, results, budget constraints, course content, and other crucial factors.