- Best LinkedIn Learning Marketing Courses You Must Know Of - January 25, 2022
- Should I Learn Traditional or Simplified Chinese - January 24, 2022
- Best LinkedIn Learning Python Courses To Pursue Next - January 24, 2022
Learning an in-demand skill like machine learning used to require enrolling in traditional college courses– but now more and more online learning platforms are doing their best to provide an alternative learning platform, like the Udacity machine learning courses we discuss here.
And machine learning courses are showing their relevance more than ever– not only for pursuing education but also for serious implications in the tech industry.
This year, Microsoft announced that the tech giant (with a net worth of over $1 trillion) is now using machine learning to predict cyber/ security threats. In a nutshell, Microsoft is collecting data from threats and using machine learning to predict patterns and procedures attackers use. Once a threat is identified, machine learning can help companies respond both more quickly and more efficiently.
Of course, cybersecurity courses have been important for a while, but this investment in machine learning, in particular, comes at the heels of some of the most serious attacks in the past two years, most notably the SolarWinds chain attack that exposed countless Microsoft users.
But while there is little doubt that machine learning engineering is more important than ever, opting to take a course through Udacity is an investment– one that shouldn’t be taken lightly.
In this article, I’ll tell you what you need to know before deciding to enroll in an Udacity course on machine learning– and point you in the direction of the best machine learning courses that Udacity offers.
What is machine learning?
Machine learning essentially enables the best of a machine’s AI to improve experiences intuitively, as opposed to being directly programmed. Machine learning courses typically involve studying algorithms and building models based upon the data collected.
Machine learning does branch off to different disciplines related to it, including machine learning engineering, computational statistics, mathematical optimization, dating mining, and, of course, studies in artificial intelligence.
Machine Learning Engineering
Machine learning engineering is the complex use of analytics and data science, combined with software engineering to create models, usually for consumer use. This can be used for outcomes such as algorithms for social media and other websites, but even has implications in financial sectors.
In other words, you interact with machine learning engineering every day. It’s what makes our internet experience more targeted to our preferences, and why everyone has a unique experience when going online, even to some of the same sites.
Computational statistics bridge statistics with computer science and is closely linked to machine learning. Computational statistics is also referred to as statistical computing and continues to evolve, but the main focus is working with raw data and using statistics to interpret it for use and analysis.
Here, the study is more about working with and interpreting data, whereas machine learning engineering has you actually creating algorithms and optimizing them for use.
Mathematical Optimization and Data Mining
Mathematical optimization is a broad term of study and is helpful for understanding courses in machine learning. In layman’s terms, optimization involves working with input data sets and individual value and maximizing or minimizing them for certain applications.
In mathematical optimization, you’ll be working with matrix operations, tableaus, pivoting, duality and minimization, and constrained optimization, which coincides with the principles of Calculus. Mathematical optimization is used in a variety of fields, from economics to computer science and engineering.
Meanwhile, data mining requires you to identify patterns in data sets and is connected not only to machine learning, but also database systems and statistics, as well as computer science. In dating mining, you collect, analyze and manage or organize data into meaningful patterns, which can be helpful for software and business databases alike.
What is machine learning used for?
Machine learning impacts all of us on a daily basis, assuming you’re online. In fact, if you’ve taken online courses before, even from an entirely different discipline like graphic design, machine learning has impacted your experience.
On a practical level, machine learning is used for internet search engines, email settings, mobile apps for smartphones, social media sites, voice recognition devices like Alexa, and countless other applications. All of these, of course, is based upon the premise of using algorithms to target and personalize experiences. As I showed in this introduction, it can also be used for cybersecurity, and even to filter out everyday spam comments and emails– something I can’t help but appreciate as a Youtube content creator.
Machine learning also has implications in the medical field. In the medical field, machine learning has been regarded as promising for quicker and more effective ways to manage healthcare and even diagnose illnesses.
Machine’s learning’s use doesn’t stop there, though: it’s also been discussed as a way to automate transportation– and it has some in common with the robotics field. In other words: machine learning has implications for our everyday lives, in more ways than we likely even are yet aware of. It’s an exciting, constantly evolving discipline that could shape the future of technology, healthcare, finances, and internet security for years and years to come.
When should I look for Udacity machine learning courses?
Before offering my recommendations for the best Udacity machine learning courses, I want to touch upon a central question: should you take courses for machine learning from Udacity?
The answer is not a simple yes or no– just that it depends, both on your goals and budget. Here are some factors to consider before enrolling in any machine learning or machine learning-related course from Udacity.
You have the budget
Udacity courses are more affordable than the average college course, but these are not free courses as you’d get on a platform like Skillshare, nor are they low-cost courses from a site like Pluralsight (of course, keep in mind that it’s difficult to find budget machine learning courses). Courses cost an average of $399/ month, and most courses take a few months.
You understand the pros and cons of open-source online courses
Open source online courses like Udacity do not have admission requirements (though some courses may require or suggest prerequisite courses from the same platform) and allow students to freely enroll.
Most, as is the case with Udacity, are also self-paced, meaning that you’ll have few barriers to first enrolling, and then have the flexibility to work within your schedule. Of course, these courses also allow you to learn from anywhere– currently Udacity has not yet updated its app for use for mobile devices.
But with that flexibility, also come some limitations. A course in an in-demand skill like machine learning may help with your resume, but it’s not an equivalent of a degree or official college certification program. And of course, taking a course on Udacity does not guarantee a promotion or better job prospects. In other words: taking these courses may help and expand your knowledge, but it’s important to also be realistic about potential outcomes, for Udacity or any learning platform.
You like the Udacity Model
Udacity offers an interactive learning model, where recorded video lectures are supplemented with hands-on activities, homework assignments, and 24-hour support. While self-paced, it’s more personalized than merely scrolling through video lectures.
You also need to feel that the pros outweigh the cons in terms of pricing, the learning experience, and more. I recommend taking a look at the syllabus before you enroll in any course, so you make sure it’s the right one for you.
Udacity Machine Learning Courses: My Recommendations
For my recommendations for the best machine learning courses, I’ve included options in their main offers for machine learning engineering, but also related disciplines that are either supplemental or even essential for understanding the field of machine learning. Here are my recommendations.
Introduction to Machine Learning with TensorFlow
This introductory course is a great place to start if you’re interested in machine learning but already have some basic knowledge. You’ll learn the foundations of machine learning, including data manipulation and working with algorithms. You’ll also be directly applying your new knowledge with hands-on projects, all using Python.
Since most of the learning involves Python, you should either have some basic knowledge of Python or experience with Python, with the option to take intermediate Python programming and/ or probability or basic statistics.
Estimated Time to Complete
Udacity has an estimated completion time of three weeks, assuming that you dedicate an average of 10 hours per week or a total of 30 hours.
This course includes methods for model construction, foundations for neural network design, and training in TensorFlow. You’ll also be required to create customer segments for a variety of problem domains. Enroll Here.
Machine Learning Engineer
This is the Udacity course to take if you’re settled on learning about machine learning engineering. With this highly-rated course, you’ll learn the more complex modeling techniques and work with advanced algorithms for practical implementation.
This is not a course to enroll in lightly, and is not for beginners, but has a concise look at some of the more advanced knowledge you need for mechanical engineering.
Prerequisites, as you can imagine, are a bit more demanding for this course. Before taking this course, Udacity recommends:
- Intermediate Python Programming Knowledge
- A minimum of 40 hours of programming experience
- Experience with libraries, such as pandas and Numpy
- Knowledge of data structures, including lists and/or dictionaries
- Intermediate Machine Learning Algorithms Knowledge
- Unsupervised models (k-mean clustering)
- Supervised models (linear regression)
- Deep learning models (neural networks)
Estimated Time to Complete
The estimated time to complete is three months if you devote around 10 hours per week.
In this course, you’ll work with Amazon SageMaker and apply models to web purposes, as well as perform A/B tests to analyze performance, update models, and continue to gather data.
Projects include building a Python package, deploying a sentiment analysis model, and using models for practical applications, such as flagging plagiarism. This more extensive course also includes a final capstone project. Enroll Here.
Deep Learning (School of Artificial Intelligence)
Deep learning will introduce you to the principles of neural networks, series predictions, and image classification. The main objective of this course is to learn about, examine, and build your own neural networks for purposes of image recognition, sequence generation, and accessibility for websites. Many of these principles overlap and are useful for machine learning.
The prerequisites for this course aren’t too intimidating. It’s considered a beginner’s course, so you want to know everything before enrolling. In fact, this is a course meant for students who are interested in the fields of AI, machine learning, or deep learning and have not yet taken courses. However, it is recommended that you are familiar with basic Python programming, as well as linear algebra.
Estimated Time to Complete
This is a longer program, with an estimated four months to complete, assuming an average of 12 hours per week.
Something I really like about this course is that the projects are at once practical but also creative– you’ll be tasked with predicting bike-sharing patterns, constructing neural networks for constructing a dog breed classifier, building recurrent networks for TV scripts, generate generative neural adversarial networks for realistic facial image construction, and finishing off by deploying a sentiment analysis model. Enroll Here.
Udacity also offers free courses that pertain to Machine Learning. Free courses are a great way to become more acquainted with Udacity without financially committing– and may be an option for those who are looking to pick up some beginning skills related to Machine Learning.
On the other hand, these free courses should not be seen as equivalent to other Udacity courses. Not only are they less in-depth, but they also don’t provide hands-on experience through projects and homework that comes with other courses.
In addition, the overall feel less personalized, without that 24-hour support and reinforcing activities. You may find that you won’t retain information as effectively. I recommend starting by trying a free course and see if you like it– that may help you decide if one of the more involved courses is right for you.
Free Machine Learning
This course is offered in partnership with Georgia Tech and includes the same self-paced learning model, with quizzes in place of more hands-on, involved projects. The course covers A.I. for computer modeling, mostly with the focus of improving user experience.
Part One covers a machine learning task related to voice recognition, while part two dives into the implementation of machine learning for services like Amazon and Netflix. It’s a more casual approach to machine learning and a nice introduction. Suggested time for completion is four months. Enroll Here.
Machine Learning Interview Preparation
Already have taken courses in machine learning and ready to apply to jobs? This free course takes just one week of your time and is meant to help you polish your interview skills specifically for machine learning-related positions.
Materials include questions related to technical strategies, image categorization, and algorithms that are common in interviews, along with self-check quizzes. But the highlight of this course is the unlimited access to mock interviews. Enroll Here.
Machine Learning for Trading
Also offered in partnership with Georgia Tech, this free course takes an estimated four months to complete and once again, connects learners with introductory material to real-world issues, alongside self-check quizzes to ensure comprehension.
This course is mostly concerned with the application of machine learning for stock trading– making it a great option for anyone also interested in business courses. It includes discussions pertaining to regression trees, linear regression, and how they are implemented in real-world training. Enroll Here.
Machine Learning for iOS
This fun course is great for the casual learner and takes a mere week to complete. By no means comprehensive, but still worth a look, the course covered how to work with the iOS framework for apps. It also provides a snapshot look at machine learning and some real-world examples. Enroll Here.
Frequently Asked Questions about Udacity machine learning course
Answer: Machine learning is a promising career path– both in terms of demand and in that it pays well. A 2019 Indeed analysis ranked Machine Learning Engineering as one of the tops in demand and compensated jobs, with an average annual salary of over $145k.
That said, there are other career paths related to machine learning, including roles in social media, AI engineering, business intelligence, data analysts, and data scientists, among others.
Answer: Machine learning is by no means easy to master. First, you need to have a level of comfortability with statistics, algorithms, and of course, working with data. The main challenges with machine learning are that you’ll need knowledge, but also creative thinking, problem-solving, and determination. Machine learning often requires thinking outside of conventions.
Machine learning is an incredibly useful and versatile area of study, and Udacity offers both free and comprehensive courses– but always make sure that you’re fully ready to commit before signing up.