Machine learning is a powerful field of (AI) Artificial Intelligence focused on developing algorithms capable of learning and consuming data.
Over the years, machine learning tools have grown increasingly valuable to most industries. The proper solutions can assist with everything, from financial forecasting to business analytics tasks.
Machine learning courses are emerging to educate beginners and professionals with the needed skills to become machine learning experts.
I’ve reviewed the most popular courses delivered by reputable providers to pick up my 10 best courses you can take online.
1. Machine Learning Specialization
Specifications:
- Difficulty: Beginner
- Duration: About three months
- Price: Free to audit, $79 for Certificate
- Provider: Coursera
Course highlights
This beginner’s course is created and led by Andrew Ng, a co-founder of Google Brain, and Coursera, a Stanford and Stanford professor.
The course uses the open-source programming language Octave rather than R or Python for the assignments. It could be a deal-breaker for many, but the Octave language is an easy way to learn ML’s fundamentals if you are a novice.
Although this beginner-friendly course is fairly self-contained, some knowledge of Linear Algebra will help.
Enroll Here: https://www.coursera.org/specializations/machine-learning-introduction
Pros
- Taught by famous instructors.
- Get a certificate upon completion.
- Beginner-friendly
- Great for value
Cons
- Long duration
2. Demystifying Artificial Intelligence: Understanding Machine Learning
Specifications:
- Difficulty: Beginner
- Duration: 12 Lessons (58m)
- Price: Start for free, $2.00/month
- Provider: Skillshare
Course highlights
If you have taken other AI learning courses but still feel the topic is complex, this Skillshare course is the best online AI course for a straightforward breakdown.
Highlights of this course include:
- Helpful examples that deliver an exciting way of learning AI online.
- Use Microsoft Seeing AI for many business applications, including reading bar codes and improving workplace accessibility.
- Focusing on AI from an interface perspective rather than the data science perspective that most AI training courses do.
If you are an advanced learner, you may feel that the amount of content is a little bit limited. But this class contains valuable AI resources and concepts that you may not find in most AI courses online today.
Enroll Here: https://www.skillshare.com/en/classes/Demystifying-Artificial-Intelligence-Understanding-Machine-Learning/949463693
Pros
- 7 days free trial.
- Skilled teacher.
- Very well taught, very insightful
- Cover AI and ML basics in a clear way.
Cons
- The content is limited for advanced learners
3. Mathematics for Machine Learning Specialization
Specifications:
- Difficulty: Beginner
- Duration: 4 Months (4 hours/week).
- Price: $49 per month
- Provider: Coursera
Course highlights
This specialization is among the best specialization programs that provide all required mathematical topics for Machine Learning. This specialization program includes three courses:
- Linear Algebra
- Multivariate Calculus
- PCA
It is the perfect choice to learn Linear Algebra concepts, such as matrices or vectors, and Calculus concepts like Taylor series, linearisation, Regression, and optimization.
The last course of the specialization is on PCA (Principal Component Analysis), an unsupervised algorithm and the most popular dimensionality Reduction Algorithm.
This specialization also gives you various hands-on practices and exercises to strengthen your understanding.
Enroll Here: https://www.coursera.org/specializations/mathematics-machine-learning
Pros
- 7-day free trial.
- Many exercises and hands-on practices.
- Provide enough knowledge of Linear Algebra.
- You can get Course Certificates and Shareable Certificates.
Cons
- The last course lacks in quality compared to the others.
4. Machine Learning Specialization
Specifications:
- Difficulty: Beginner to intermediate.
- Duration: Approximately 7 months.
- Price: $49 per month
- Provider: Coursera.
Course highlights
If you must learn AI from scratch, this specialization is the way to go. It is engaging yet comprehensive, and you will learn from a great teacher and educator, Andrew Ng, who is also the founder of Coursera.
Through three courses, you will access modern machine learning, including supervised learning, unsupervised learning, and several of the best practices used in Silicon Valley for AI and machine learning innovation.
Also, you are introduced to statistical pattern recognition and data mining. A basic understanding of linear algebra is essential for this specialization.
Enroll Here: https://www.coursera.org/specializations/machine-learning
Pros
- Affordability
- Beginner-friendly.
- Great Instructor.
- Do not necessarily require any prior skills.
Cons
- Too general.
5. Machine Learning: Unsupervised Learning
Specifications:
- Difficulty: Intermediate
- Duration: Approx. 1 Month
- Price: Free
- Provider: Udacity
Course highlights
It is a graduate-level series of three courses, including the area of AI concerned with computer programs that improve and modify their performance through experiences.
The whole series is led by an engaging dialogue between two eminent Machine Learning professors: Professor Michael Littman (Brown University) and Professor Charles Isbell (Georgia Tech).
Enroll Here: https://www.udacity.com/course/unsupervised-learning–cd0026
Pros
- Rich learning content
- Interactive Quizzes
- Taught by industry pros
- Self-paced learning
Cons
- No certificate
6. Artificial Intelligence for Beginners: Tools to Learn Machine Learning
Specifications:
- Difficulty: Beginner
- Duration: 12 Lessons (1h 5m)
- Price: $2,00 per month
- Provider: Skillshare
Course highlights
This course covers needed topics for ML practitioners, AI product managers, and anyone in between that’s curious about those emerging fields. It is an excellent overview to ML and is a great first place to start,
It involves minimal amounts of code but doesn’t require any technical knowledge. So regardless of your background, you will walk away with the fundamentals for learning, practicing, and discussing machine learning.
Enroll Here: https://www.skillshare.com/en/classes/Artificial-Intelligence-for-Beginners-Tools-to-Learn-Machine-Learning/380376197
Pros
- 7-day free trial.
- Hands-on practices.
- Does not require any technical knowledge.
- An excellent introduction/overview to ML.
Cons
- A bit difficult for some beginners.
7. Get started with Machine Learning
Specifications:
- Difficulty: Beginner
- Duration: N/A
- Price: $13.99 a month for Pro Lite and $19.99 a month for Pro.
- Provider: Codecademy
Course highlights
The Pro path is ideal for learners who want to improve their Python skills, learn advanced programming, and have fun with artificial intelligence and machine learning. This course will teach you how to use Python to reformat the dataset and search for inaccurate data points.
In addition, you will learn critical data skills, such as sci-kit-learn, Python 3, pandas, Matplotlib, and Jupyter Notebooks. To become a Machine Learning expert, you should join this CodeCademy career path.
Enroll Here: https://www.codecademy.com/learn/paths/machine-learning
Pros
- Certificate of completion.
- Great instructor
- Gain real-world experience.
- Get up to speed quickly with the skill sets that you need.
Cons
- Higher price.
8. Deep Learning Specialization
Specifications:
- Difficulty: Intermediate
- Duration: Approximately 5 months.
- Price: Free to audit, $49/month for Certificate.
- Provider: Coursera
Course highlights
Also led by Andrew Ng, this specialization program is an advanced course series for everyone interested in learning about Deep Learning and neural networks and how they handle many problems.
The lectures and assignments in every course use the Python programming language and the TensorFlow library for neural networks.
To understand any algorithm presented in the specialization, you should already be familiar with machine learning and Linear Algebra in general.
Enroll Here: https://www.coursera.org/specializations/deep-learning
Pros
- Practical project.
- Have a certificate.
- Can try it for free.
- Taught by famous instructors.
Cons
- Require familiarity with machine learning and Linear Algebra in general.
9. Machine Learning with AWS Training
Specifications:
- Difficulty: Intermediate.
- Duration: 2 days.
- Price: N/A
- Provider: Learningtree
Course highlights
The main goal of this course is to teach you about the emerging world of patterns, practices, and techniques, helping you to understand proven solutions to common problems.
You will also learn about various machine learning and artificial intelligence services available on AWS. Through practical hands-on exercises, you will learn how to utilize those services to generate amazing results.
By the end of the course, you should gain a foundation of how to use various AWS services in your own projects.
Enroll Here: https://www.learningtree.com/certifications/aws/
Pros
- Hands-on exercises
- Self-paced learning
- Structured and concise
- In-depth and creative lessons
Cons
- Lack of comprehensiveness
10. Machine Learning in Python
Specifications:
- Difficulty: Intermediate
- Duration: 7 courses
- Price: Free
- Provider: Dataquest
Course highlights
In this course, you’ll go through each step of a typical machine learning project: core mathematical concepts, identifying applications, using algorithms and optimization methods.
At the end of the course, you’ll complete a real-world, end-to-end project and work through the entire machine learning lifecycle.
This project allows you to combine the skills you have learned so far, and it also serves as a portfolio project that you can show potential future employers to demonstrate. Your skills in a complete project.
Enroll Here: https://www.dataquest.io/path/machine-learning-in-python/
Pros
- Great value.
- Short duration
- Very comprehensive curriculum.
- Reasonably priced for the value.
Cons
- Explanations for the topics are too brief.
Learning Guide
Course Prerequisites
Some advanced machine learning courses should require the following knowledge before starting:
- Calculus
- Probability
- Programming
- Linear Algebra
Yet, many courses don’t have any prerequisites, making them ideal for anyone.
Fundamental Algorithms
There is a base set of machine learning algorithms that everyone should be familiar with and can use, including:
- Naive Bayes
- Decision Trees
- Random Forests
- Linear Regression
- Logistic Regression
- k-Means Clustering
- k-Nearest Neighbors
- Support Vector Machines (SVM)
After having the basics, some more advanced techniques to learn will be:
- Boosting
- Ensembles
- Reinforcement Learning
- Dimensionality Reduction
- Neural Networks and Deep Learning
Tackle a Project
Online machine learning is challenging and extremely rewarding.
It’s important to remember that just watching videos and doing quizzes doesn’t mean you’re learning the material.
You will learn more if you work on a side project that uses different data and has other goals than the course itself.
Dealing with projects gives you a higher understanding of the machine-learning context. As you learn more advanced concepts, like Deep Learning, there are almost endless techniques and methods to understand.
Read New Research
Machine learning is a rapidly growing field where new applications and techniques are constantly born. Once you’ve mastered the fundamentals, keep up to date with the latest and see if you can apply them to your own problems.
FAQs
What Jobs Can I Get With Machine Learning?
With Machine Learning expertise, you can land jobs like NLP Scientist, Research Engineer, Machine Learning Engineer, Machine Learning Cloud Architect, etc.
Is Machine Learning Hard to Learn?
The answer is Yes. Factors that make it difficult are the in-depth knowledge of many aspects of computer science and mathematics and the attention to detail one must take in identifying algorithm inefficiencies.
Also, machine learning applications require meticulous attention to optimize algorithms.
How Long Does It Take To Learn Machine Learning?
It might take around six months to complete a machine learning engineering curriculum. But it can take longer if you start without any knowledge of data science, statistics, or computer programming.
What Is the Salary of a Machine Learning Engineer?
The average annual salary of a machine learning engineer in the United States is $129,383. Salaries vary significantly, depending on many factors, such as years of experience, degrees and certifications, and locations.
Final Thoughts
Machine learning is one of the great career options if you are passionate about automation, data, and algorithms.
Your workday will be filled with moving and handling raw data, implementing algorithms to enable that data to be processed, and automating the process.
The courses mentioned in this article, such as Demystifying Artificial Intelligence: Understanding Machine Learning, will assist you in becoming an expert in machine learning as they cover the ML topics in depth.
In addition, these courses offer excellent placement and career guidance. In addition to the cost savings, these courses are also completely flexible, enabling you to study anytime and anywhere.