2 min read
Machine Learning is the boom nowadays along with Big Data and Artificial Intelligence. Involving the use of neural networks and huge datasets to make machines think on their own and develop their solutions to specific problem statements, Machine Learning is being employed to make many of our daily life tasks done easily and efficiently by using computers. Whether it is making Deepfake videos of Mark Zuckerberg or generating music from a specific genre, Machine Learning finds its application in all domains.
With that in mind, we have compiled 3 best free courses on Machine Learning that will help you spend your summer holidays productively:
1. Learn ML From Scratch – Go from Zero to Hero
This course on Udemy spans 3 hours worth of video content covering the basics of Machine Learning and then go towards Supervised and Unsupervised Learning techniques. The course also incorporates practical examples and applications of some algorithms which are used in model evaluation and improvement. The course closes off with a round up of working with datasets and a short overview on advanced machine learning concepts.
2. Introduction to Tensorflow for Deep Learning
Tensorflow is an extremely popular framework by Google for developing applications using Machine Learning. Written in Python, this framework is being used by a lot of companies throughout the world. By taking this course, you will be handling state-of-the-art image classifiers upfront and use Tensorflow models on your devices as well as work with large datasets. 2 developers from Google and 1 from Udacity itself are teaching this course and they promise that after taking this course, you will be able to make your own standalone AI applications. What’s more to say?
3. Machine Learning Interview Preparation
For those who think they already know a lot about AI and ML should take this course if they are about to apply for a job somewhere. The course will teach you to effectively handle data structure questions and whiteboard problems as well as cover all important questions and technical strategies.
Featured Image Credit: Towards Data Science
