What is Machine Learning?
Machine learning is a subfield of artificial intelligence dedicated to the design of algorithms capable of learning from data. It has numerous applications, including business analytics, health informatics, financial forecasting, and self-driving cars.
In 2022, machine learning skills are widely in-demand. On Microsoft’s career page , 21% of the open developer positions currently mention “machine learning”. On Amazon’s career page , it’s 63%.
According to the Future of Jobs Report published by the World Economic Forum, machine learning is expected to be one of the world’s most in-demand skills through 2025.
Course Ranking Methodology
To create this ranking, I followed a three-step process:
First , I’m a developer at Class Central , the leading search engine for online courses.
So I went through our catalog of over 50K courses to put together a preliminary selection. I did so by taking into account factors like reviews, ratings, enrollments, bookmarks, and more.
So this was a rather objective step: I narrowed down the options by looking at well-defined metrics.
Second , I used my experience as an online learner to evaluate each preliminary pick.
Metrics such as course ratings rarely tell the whole story. I’ve completed many MOOCs , earned an online bachelor’s in computer science , and I’m enrolled in Georgia Tech’s online master’s in computer science ( OMSCS ). This has given me some perspective on what to look for in an online course, which I used to evaluate each of my preliminary picks.
So this was a rather subjective step: I combed through my picks to arrive at a near-final selection.
Third , I expanded this selection to include other valuable resources I’ve come across.
Since there are long-established courses in most topics, more recent courses on the same topic can go unnoticed. But sometimes, these are great. I made sure to include those when possible.
So this is a rather subjective step again: I rounded up my picks with excellent but less well-known courses.
The end result is a unique selection of courses that combines a decade of Class Central data and my own experience as an online learner to try to get the best of both worlds. So far, I’ve spent more than 15 hours building this list, and I’ll continue to update it.
My first pick for best machine learning online course is the aptly named Machine Learning , offered by Stanford University on Coursera.
What You’ll Learn
This course starts by laying down the mathematical foundations of machine learning. It begins with a review of linear algebra and univariate linear regression before moving on to multivariate and logistic regression.
It then jumps from topic to topic each week to cover a wide variety of machine learning techniques and models. These include deep learning, support-vector machines, and principal component analysis.
Finally, it touches on practical aspects such as how to design and leverage large-scale machine learning projects.
By the end of the course, you’ll have a broad understanding of machine learning, its concepts, and its methods. You’ll be able to implement fundamental machine learning algorithms such as back propagation and k-means clustering.
You’ll be equipped to tackle tasks such as multi-class classification and anomaly detection. And you’ll be able to use Octave and Matlab to complete practical projects involving optical character recognition using a wide variety of approaches.
One Thing to Note
This course uses Octave rather than, say, Python. The course will teach you the concepts but not the tools most commonly used nowadays in machine learning. Despite that, it remains in my view the machine learning course of choice, hence its top spot.
But if you’re looking for a course more relevant to the day-to-day of a machine learning practitioner, check the next pick.
How You’ll Learn
The course is broken down into 11 weeks. Each week involves about 6 hours of work. Concepts are taught through a combination of video lectures and readings.
In terms of assessments, each week includes at least one auto-graded quiz. Most include several. And most weeks include a multiple-hours long programming project. There are 8 in total.
My second pick for the best machine learning online course is Machine Learning Foundations: A Case Study Approach , offered by the University of Washington on Coursera.
Many academic machine learning courses like to approach the subject from a rather abstract perspective. They spend a lot of time laying down mathematical foundations and relegating more tangible aspects of the discipline to examples and exercises. This course flips that script on its head.
What You’ll Learn
The course starts by contextualizing machine learning: explaining what machine learning is, going over some of its applications, and making a case for its importance in the future.
The course introduction also takes the time to cover Python fundamentals as well as the rudiments of tools like Jupyter Notebooks.
The course then moves from case study to case study, using each one to illustrate a particular facet of machine learning: you use regressions to predict house prices, you use classification to evaluate sentiments in user reviews, you use clustering for grouping related articles, you use deep learning to identify objects in images, and so on.
If you’re someone that likes to learn through examples, the clear mapping between tasks and concepts in this course might help make the subject more palatable to you.
By the end of this course, you’ll understand fundamental machine learning tasks like regression, classification, and clustering, and you’ll know when to use each technique.
Top 10 Speech Recognition Software
Let’s take a look at our list of the top 10 software programs mentioned below.
1. Alibaba Cloud Intelligent Speech Interaction
This Chinese cloud major utilizes various technologies like speech synthesis and voice recognition and offers Intelligent Speech Interaction. This software comes with myriads of language interfaces.
The software uses a High Accuracy level and promotes continuous self-learning. It also comes with an excellent multilingual transcription capability. Along with a wide spectrum of Application Programming interfaces (APIs), it also comes with a developer guide. Some other features of this software include real-time subtitling and analysis of service calls.
The cost of this software includes an expenditure of $1/hour for recorded files and $1.40/hour for real-time voice recognition.
This software comes with a user-friendly fluid API that allows the developers to convert speech to text without any hassle. This considerably increases revenue by providing a rich experience and boosts workplace productivity. It provides over 90% speech recognition accuracy. The developers have taken to an innovative way of speech recognition by using heuristics-based voice processing, allowing the users to access the fastest and most accurate AI in the industry with an easy-to-make API call. The software has the potential to transcribe one-hour audio in just under 30 seconds.
The software comes with three price packages, namely, a Pay-As-You-Go package where you can purchase credits of your own volition, a Starter package where you can pre-pay $500-$1999 credits for the year, and a Growth package where you can pre-pay $2000-$4999 credits for the year.
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3. Amazon Transcribe
This is a voice recognition software by Amazon Web Services (AWS). It uses Natural Language Processing (NLP) for transcribing speech to text. This transcription platform is cloud-based. Transcribe offers an exciting 80% accuracy level with easy-to-read transcriptions. It provides ten alternative suggestions for transcription and gets better by learning the user’s input. Transcribe is extremely careful while handling sensitive and personal data, provides high security, and maintains privacy.
It comes with a free package of 60 minutes per month that lasts for a year. After that, it charges $0.00780 per minute.
This software comes with AI-empowered Noise and Echo Cancellation technology making its way as a leading software in the industry. It comes with a Talk-Time that gives valuable insights into the call, like the percentage of the call users are speaking. This allows the users to better communication. Krisp utilizes three technologies: Automatic Speech Recognition (ASR), Punctuation and Capitalization of the Text, and Speaker Diarization.
It comes in four packages:
- A Free package.
- A Pro package costs $96 annually.
- A Business package costs $120 annually, and an enterprise package needs personal settlements.
5. Nuance dragon
Microsoft owns nuance dragon software. The Automatic Speech Recognition (ASR) technology used by the software comes into various uses, including professional and individual applications. It offers up to 99% speech recognition accuracy, and custom voice commands can be defined in this model. This software comes with various developer resources that allow developers to build chatbots and various voice recognition software applications.
For Windows, the starting prices are $200 for Dragon Home and $150 for annual subscriptions for the Professional edition.
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6. Google Speech-to-Text API
This is a cloud-based Automatic Speech Recognition (ASR) software. The software provides language interface ranges of up to 125 languages, and some models are pre-trained for specific domains. It has an accuracy of 80-85%. Here the users can train the model with specific vocabulary according to the need of their domains. The Enterprise offers data security by leveraging the audio-to-text on-premises. Although the software can handle voice recognition in difficult scenarios, it requires technical expertise for handling the software.
The software offers the first 60 minutes for free and charges $0.004/15 seconds or more henceforth.
7. Microsoft Azure Cognitive Services for Speech
This software is owned by Microsoft and is built on the Azure cloud. The speech Software Development Kit (SDK) consists of two components that allow developers to build applications and provide a Speech Studio that helps modulate the software’s functionality.
Azure has a special feature that recognizes the speaker and the speech. This software can either run on the cloud or edge. Azure provides an accuracy level of 75-80%. It has a language interface spectrum of over 100 languages. The software provides elaborate courses on documentation and user-friendly code in the Studio.
The software comes for free for the first five months and costs $1/hour or more.
This is a 2017 startup with a specialization in Applied AI. The software uses deep learning technology to provide excellent speech recognition and user experience. This software provides an accuracy level of up to 100%. The reason behind providing such a high accuracy level is that the platform consists of automated speech recognition and human transcriptionists working conjointly.
Not only does it performs transcription, but also it does audio/video-to-text conversions. The model is continuously adaptable by training with custom vocabulary. It offers developers by providing extensive API documentation.
The usage of this software costs $0.00025/second.
This software provides accurate Automatic Speech Recognition (ASR) using deep neural networks. This software can be run on the cloud or on-premise and provides batch-based audio conversation. This software provides an accuracy level of 85-90%.
Voicegain provides a transcription assistant application that is quite user-friendly and can be used while holding meetings or processing recordings. It is adaptable and can be trained using audio data sets to match the desirable vocabulary. Voicegain also comes with a wide range of APIs. This software’s acoustics and language models are easily modifiable, which adds to the product’s value.
The cost of the cloud version of this software starts at $0.0025/minute.
10. IBM Watson Speech to Text
Watson is an AI engine owned by IBM with sound voice recognition capabilities. It also provides myriads of language interfaces, audio formats, and other programming interfaces, making it useful for call center analytics. The software comes with a sound 95% voice recognition accuracy level. It has the potential to transcribe seven different languages’ audio into text simultaneously. The language model is easily customizable and also well adaptable to match the respective product names.
The software comes for free for the first 500 minutes, after which it costs $0.01/minute.
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