Machine Learning Degrees Online in 2024. The power of computers to spot data patterns and make predictions beyond our skills is changing the world. Machine learning is giving rise to inventions big and small, from chatbots for customer service to software for facial recognition to self-driving automobiles, all because of the exponential growth of technology. Career prospects are plentiful in the business because many companies in various areas have adopted it.
A career in machine learning can lead you into many different fields, including data science, human-centred machine learning, engineering, engineering, and business intelligence. Machine learning specialists are in high demand, and big tech businesses are willing to offer top salaries.
If you want to get more information regarding the online courses of machine learning in 2024, delve into the article straight!
How to Select the Right ML Course?
Review the following points before deciding on the top machine learning degree online courses:
1. Set Objectives
Find out what areas of machine learning most pique your interest and do some research on them. If you want to know which classes are in demand in your desired field of study, you’ll need this information.
2. Evaluate Yourself
Take stock of your skills with the requirements of your chosen area of study. Identifying your knowledge gap and enrolling in the appropriate course is the objective.
3. Investigate the Program and Instructor
You must select a reputable education provider whose credentials are acknowledged on a global scale. Expert education should also be provided. A flexible schedule outlining the course’s benefits and content is essential.
4. Projects
Always pick a course that gives you real-world experience through big projects. The most recent technology and tools should also be covered.
Salary of the Machine Learning Jobs
Machine learning salaries can vary widely based on factors such as job role, experience, location, industry, and level of education. The average income of entry-level machine learning jobs is $70,000-$120,000 per year. On the other hand, the average salary of professional machine learning jobs is $120,000-$180,000+ per year.
Also See: Best Online Masters in Artificial Intelligence
Online Machine Learning Degree Programs
1. Machine Learning- Stanford University
This Machine Learning course is presented by Andrew Ng, former Baidu Chief Scientist and Google Brain Deep Learning Project Director. Machine learning algorithms are covered in both the theoretical and practical components of the course. Computer vision, language interpretation, database mining, and robot creation are just a few areas where you can learn to apply machine learning methods.
You will have the chance to work on real-world projects using optical character recognition in Octave and Matlab. However, a basic knowledge of linear algebra, probability, and statistics is essential.
The duration of the course is approximately 11 weeks. The course fees are $4,056-$5,408. It gives you a Shareable Certificate for your resume or LinkedIn page.
Features of the Courses
- If you’re interested in understanding the fundamentals of machine learning and want to know how to use Octave or MATLAB, this course is for you.
- Topics covered include linear regression with one variable or many variables, neural networks, logistic regression, unsupervised learning, regularization, support vector machines, and more.
- You will also solve problems on anomaly detection and multiclass
- Every week, you’ll have the opportunity to take an auto-graded quiz.
2. Machine Learning With Python- Coursera
Coursera mainly focuses on the general concept on machine learning algorithms. To help you get a feel for the fundamentals, the instructor, the slide animations, and the algorithm explanation work together quite well.
There is less emphasis on the mathematics underlying algorithms in this Python-based course. After finishing each unit, you’ll be able to put what you have learned into practice by creating a web-based Jupyter notebook. With each notebook, you’ll solidify your understanding and get step-by-step guidance on applying a method to actual data.
When you learn a new algorithm, your teacher will go over its purpose, advantages, and disadvantages as well as when to employ it. This knowledge is crucial for new students to grasp the bigger picture, but it is frequently omitted from other classes. It costs $39 per month for a Certificate
Features of the Courses
- Intro to Machine Learning
- Regression
- Classification
- Clustering
- Recommender Systems
- Final Project
3. Machine Learning for Data Science and Analytics- ColumbiaX
You will learn the fundamentals of machine learning and its various algorithms in this course. A variety of Machine Learning algorithms will be covered in this course, including Support Vector Machines, Logistic Regression, Unsupervised Learning, Linear Regression with One and Multiple Variables, etc. Data analysis and topic modelling are also covered in depth, so you’ll be able to uncover meaningful insights inside massive datasets.
Statistical machine learning theory is given more weight in this ML course than ML’s practical applications. You will be able to demonstrate your mastery of Machine Learning for Data Science and Analytics with a shareable certificate once you finish this course. Proficiency in arithmetic and computer programming is required of all applicants. The course duration is 5 weeks. Moreover, the classes are free of cost.
Features of the Courses
- Understand machine learning through predictive analytics.
- Support Vector Machines, Logistic Regression, Unsupervised Learning and Linear Regression
- Well-structured curriculum.
- The lessons are in-depth and informative.
- Schedule and learn whenever you are free.
4. Machine Learning for Coders- Fast.ai
If you have around a year of experience programming in Python, Fast.ai has put together a fantastic free machine-learning course just for you.
It’s incredible how much work the folks behind Fast.ai have put into this and all of their courses. The material is drawn from the Data Science department at the University of San Diego, therefore the lectures are conducted in a classroom setting with students, much like the MIT OpenCourseWare model.
Several videos, a few homework tasks, detailed notes, and a forum for class discussions make up this course. If you are a programmer interested in learning and using ML techniques, this course is a great choice.
Features of the Courses
- Random Forests
- Random Forest Deep Dive
- Performance and Validation
- Model Interpretation
- Feature Importance. Tree Interpreter
- Data Products
- RF From Scratch
- Gradient Descent
- Logistic Regression
- Regularization and Learning Rates
- NLP and Columnar Data
- Extrapolation
- RF from Scratch
- Embeddings
- Complete Rossmann
- Free of cost
- Live Coding
5. Machine Learning- Google AI
Google AI Education is the source of this course. It is a free online platform that mixes articles, videos, and interactive information.
If you need to know how to solve ML problems quickly, then you should take the Machine Learning Crash Course. Python, as in the first course, is the language of choice, and this time around we learn about TensorFlow. A Google Colab-hosted interactive Jupyter notebook is a part of the curriculum for each major unit.
If you are interested in covering all your bases and have dabbled with ML, this is the finest option on the list. Many of the intricacies of machine learning, which could normally take hundreds of hours of accidental study, are covered in the course.
You can go through the course material at your speed thanks to the concise and easy-to-understand video lectures and essays.
Features of the Courses
- Logistic Regression
- Classification
- Training and loss
- TensorFlow
- Training sets
- Feature Engineering
- Cleaning data
- Feature Crosses
- Regularization
- Model performance metrics
- Embeddings
- ML Engineering
- Overfitting
- Neural Networks
Conclusion
Machine learning degrees online have become a gateway for aspiring professionals to enter the exciting and rapidly evolving field of artificial intelligence. The flexibility, accessibility, and quality of these programs make them an attractive option for learners worldwide.
By carefully considering factors such as accreditation, curriculum, and industry connections, individuals can make informed decisions and embark on a journey to acquire the skills needed to thrive in the era of machine learning.
