Nowadays technology has a role to play in everyday life-related matters. Whether you are shopping, doing online banking, or just listening to your favorite music. When we go online and search for something on a search engine website like Google, Bing, etc. The machine notes down your search type and starts to show advertisements similar to your search.
When we go online to a shopping website and check for a product. You will notice that the website recommends products similar to the one you have searched for. It would also suggest a combination of products to buy with the product of your choice. Also, it would suggest what other consumers have bought when they bought your search product.
What does that mean? This means that the machine or computer has learned from your input what you are searching for. Now using pre-fed algorithms, it starts to search and present similar items to the user. This learning and the predicting process is known as Machine Learning.
Machine Learning — An Overview
A set of algorithms or a subset of artificial intelligence directing the machine to learn from experience and present predictions based on its experience. These algorithms or set of instructions commonly known as programs in computer sciences are designed in such a way that whenever they are exposed to new data they learn and evolve.
There are three types of machine learning. These are Supervised Learning, Unsupervised Learning, or Re-enforcement Learning.
Supervised learning is task-driven and the most basic type of machine learning. Labeled data is used to train machine learning algorithms in this form of learning. If used in the right circumstances it is a powerful mode of learning.
Unsupervised learning can work with un-labeled data that is why it is data-driven. Thus, it allows the program to work on much larger data sets.
Re-enforcement learning evolves from the process of trial and error just the way humans learn during their course of life. Its algorithm improves itself, “encourages” favorable outputs, and “discourages” unfavorable outputs.
In other words, we can say machine learning is the evolution of machines. Now the question arises of how we can benefit from Machine Learning. There are numerous ways we can learn and understand ML. Many online teaching platforms are offering ML courses free and paid. Below we will look into the top 08 ML teaching platforms.
Top 8 Machine Learning Courses (Paid & Free)
Below we will look into different courses available online to learn about Machine Learning.
List of best machine learning courses:
- Introduction to Machine Learning Course — Udacity
- Artificial Intelligence and Machine Learning Fundamentals — Udemy
- Machine Learning Offered by Stanford University — Coursera
- Machine Learning A-Z: Hands-On Python & R in Data Science — Udemy
- Deep Learning A – Z: Hands-On Artificial Neural Networks — Udemy
- Machine Learning Specialization — Coursera
- How to Think About Machine Learning Algorithms — Pluralsight
- Understanding Machine Learning — Pluralsight
1- Introduction to Machine Learning Course — Udacity
Machine learning is the gateway to a thriving career in data analysis in today’s age and time. Machine learning harnesses predictive power by combining computer science and statistics. In this course investigation of data is learned through the medium of machine learning.
What will be achieved through completing this course?
It gives you insight into the extraction and identification of the features, which will help you present your data in the best way possible. It equips you with some important machine learning algorithms and the power for the evaluation of their performance.
For anyone who wants to develop his data analysis skills, this course is a must-have. This course imparts the basic knowledge of representing raw data into trends and predictions.
This course compiled by Udacity is free. When we say it is a free course it means that no certification will be awarded for attending this course. The learning platform Udacity offers both theoretical and practical knowledge related to machine learning. It makes machine learning more interesting by giving you programming experience in Python.
2- Artificial Intelligence and Machine Learning Fundamentals — Udemy
This course is specially designed for software developers and data scientists. So, they can improvise their abilities and expand their range regarding machine learning projects. Anyone having sufficient knowledge of Python and basic mathematic skills of secondary school level. Is eligible to register for the said course.
This course builds in you the capacity for performing predictive analysis by utilizing artificial intelligence. Not only that it also helps in solving problems related to the real world. In the field of artificial intelligence machine learning and neural networks are the building blocks. This course introduces neural networks and benefits from Moore’s Law which is applied to the modern computing power of the 21st century.
This course also equips you with the confidence and knowledge to build AI applications. This course enriches its learners by Python illustrated examples on a topic such as classification, regression, and other mathematical topics.
This course is compiled by the Udemy learning platform. At the time of writing this article, Udemy is offering a 90% discount on the course. This course is originally priced at $129.99 but with a 90% discount, it can be availed for $12.99 only. Anyone attending this course will get a certificate of completion. The attendee will also be given lifetime access to the course contents. This course is accessible on mobile as well as TV.
3- Machine Learning Offered by Stanford University — Coursera
This course is designed to test you on every topic that is covered in this course. Once the course is completed and all the quizzes done a certificate will be awarded to the attendee. This course is taught by Andrew Ng in collaboration with The Leland Stanford Junior University, commonly referred to as Stanford.
It is an eleven-week course that starts with an introduction to Machine Learning. In the introduction module, the learner is introduced to the idea of teaching a computer by explicitly programming it, to learn concepts using data. You can enroll in the course for free.
Many other concepts are taught in the following weeks. Concepts like Linear Algebra, Linear Regression with variables, etc. This course is valuable even for the developer. It gives them a strong understanding of the mathematics on which the machine learning algorithms are based. Thus, adding value for a developer.
Coursera is a great platform for all types of learners. Coursera is offering more than 5300 courses, degrees, and specializations, and a lot of free courses as well. It is one of those online platforms which has linked its courses with accredited universities. Also, the tuition costs are significantly lower than many other platforms. Last but not least the courses are conducted by leading experts in their fields.
4- Machine Learning A-Z: Hands-On Python & R in Data Science — Udemy
This course is specially designed to develop insight regarding machine learning algorithms. It helps to learn the creation of machine learning algorithms by two data science experts in R and Python. This course also lets the learner practice code with the help of a lot of examples. This course teaches the handling of particular topics such as Deep Learning, Re-enforcement Learning, NLP, and Dime like advanced techniques.
You can master machine learning on R and Python not only to add value to your businesses but for personal purposes as well with the help of this course. Making accurate predictions and robust machine learning models is an added skill that can be learned through it. It also develops the skill of the learner which is to choose for every type of problem the correct relevant model. This is an actual hands-on course and imbibes key skills in the learner in data science.
This is one of the best Machine Learning courses. It has been the choice of more than 739,163 and more than 139,000 have rated it as 4.5 out of 5. The course duration is 41 hours, with lots of exercises and quizzes to strengthen the grip of the learner.
5- Deep Learning A – Z: Hands-On Artificial Neural Networks — Udemy
Developing the know-how in creating deep learning algorithms in Python is the main objective of this course. Both Machine learning and data science experts teach deep learning with the help of real-world examples. Thus, making it a top-rated course. This course not only helps its learner apply Artificial Neural Networks but also gives an insight into the intuition of these networks.
Applying Convolutional Neural Networks and understanding the intuition behind Convolutional Neural Networks and Recurrent Neural Networks, are also the other major goals achieved through this course. This course is designed in such a way that it has a robust structure. Since Deep Learning is complex and very broad one needs a clear and thorough outline to navigate around it. The hands-on coding exercises are designed to develop an intuition on an instinctive level. This course also offers interactive support where the learner can send a query that will be addressed in 48hours.
As mentioned earlier above Udemy is one of the best online teaching platforms. To date 294,424 students have enrolled for this Deep Learning A – Z: Hands-On Artificial Neural Networks course. This course has 35,295 ratings and has been rated as 4.5 out of 5. This course has been created by Kirill Eremenko, Hadelin de Ponteves, and SuperDataScience team. This course is priced at $129.99.
6- Machine Learning Specialization — Coursera
This course is especially for people who are interested to go a step ahead from the methods of traditional machine learning. This is a machine learning specialization in Python and uses deep learning while discussing the applications on building machine learning. This specialization course is more advanced and focuses more on neural networks and deep learning.
Machine Learning Specialization course also teaches how deep learning and neural networks are used in problem-solving scenarios. The language used is Python, it also uses the TensorFlow library for neural networks. The modules in this course are Neural Networks and Deep Learning, Improving Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models.
This course also applies the working knowledge of Linear Algebra and machine learning. Specialization courses provided by Coursera include Hands-On projects. If you want to earn a certificate, then you would need to successfully complete the Hands-On projects. The certificate has helped many people start a new career after completing this course. This course has also helped people secure a pay increase or a promotion in their current job.
The enrollment in this course is free yet to earn a certificate a fee of $49/month is payable. Earlier in this article, we have mentioned a lot about the course delivery platform Coursera.
7- How to Think About Machine Learning Algorithms — Pluralsight
Primarily this course is aimed to impart the thinking about machine learning algorithms in four stages. Four basic approaches are taken to solve a problem. They are classification, regression, clustering, and recommendation. This course enables the learners in determining which approach is needed to solve a particular problem.
In the next stage, you will be learning to set up problems, statements, features, and labels. You will be also able to solve the problem by plugging in the standard algorithm. Eventually, this course will impart the skills needed in recognizing an opportunity and seizing it for a machine learning application.
Pluralsight offers three different payment options for its users. There is a monthly option, an annual option, and a premium option. The monthly is priced at $19, the annual is priced at $159, and the premium is priced at $239.
8- Understanding Machine Learning — Pluralsight
This course offers a basic introduction to understanding machine learning that is why a basic IT background is enough to take this course. This course gives a precise yet clear introduction to the critical topic of machine learning with a new perspective. This course will help you create a machine learning prediction solution.
This course will also enable its learners to be introduced to the Jupyter Notebook environment, Python, and the scikit-learn library. This course also gives the learners a walk-through to preparing data, selecting algorithms, training the model, and testing the model’s accuracy.
To take this course the learner needs a Pluralsight membership. The membership ranges from $29 monthly to $239 annually and is a must-have for software developers. This platform offers more than 5000 supreme quality courses. A 10-day free trial is also available to all without any hidden catches. During this 10-day trial, you are allowed to watch 200 hours of content.
Frequently Asked Questions
How would you distinguish between supervised and unsupervised machine learning?
When we talk about supervised learning we come to understand that it requires labeled data. For example, if one is working on classification which is a supervised learning task. Labeled data will be used to train the model, thus it can classify data into groups that are labeled. On the other hand, unsupervised data does not need labeling data.
What do you mean by deep learning and how does it differ from other machine learning algorithms?
A subset of machine learning is deep learning. It is concerned with neural networks, usage of backpropagation, and deals with the principles of neuroscience to model large sets of unlabeled or semi-structured data accurately. In short deep learning is a representation of an unsupervised learning algorithm, which learns to represent data with the use of neural nets.
Why should classification be preferred over regression?
Discreet values and data set to strict categories are produced by classification. Regression, on the other hand, provides continuous results that help to distinguish between the differences of individual points. If results are required to represent the belongingness of data points in explicit categories of the data set, the classification must be preferred over regression.
Today’s age is the age of data sciences and machine learning is an enormous component of this vast and ever-evolving field. The fact cannot be denied that in the competitive environment of career and business one needs to develop the skill set in visualization, statistics, and data analysis. Machine learning offers a wide range of knowledge and assists in predicting outcomes.
Machine learning is not only interesting and fun to learn but it also is very rewarding in career and business development. That is why many people want to enter the field and have a thriving and fruitful future.
That is why many platforms are there to offer many different courses in the field of machine learning. Since it has become one of the key fields of study, so many people which to take up these courses to either develop or enhance or give direction to their career or business.
Keeping the interests and requirements of a vast majority of people we have listed several courses being offered by different reputed and reliable platforms. These courses are designed to cater to new entrants, people with basic IT knowledge, and developers alike.
From the above list of courses, readers can choose either a paid course or a free one depending on their suitability. Some of the courses mentioned above are prepared and conducted by experts in this field. Many online courses give learners the option to download the content. Almost all the learning platforms offer the learners the facility of lifetime access to the course content.
Now decision is totally yours but make sure you choose the most reliable course according to your experience.