how hard is machine learning reddit

The midterm and final were the tough ones for me. One category of the machine learning algorithms can be utilized to accomplish 2 or more subtasks. I haven't taken a calc class or used calc in 15 years. Talking AI and ML with Airbnb machine learning engineer Chirag. As others said, really learning stats and machine learning is a long process. It’s Deep Learning frameworks are complex and take a bit of time to learn, and the fastest way to master a DL framework is to implement a challenging neural net architecture that exercises obscure features of the framework or ideas straddling multiple subfields of DL.

My focus right now is getting up to speed with TensorFlow and Keras.

If so you should be alright.I want to second this. We all learn slightly different ways and have different objectives of what we want to do with or for machine learning. However, AWS provides cloud-based tools for data analysis and processing (As so many owners share their datasets on the web, you may wonder yourself how to start your search or struggle making a good dataset choice.When looking for specific data, first browse catalogs of data portals. Note that although the class covered neural networks, it was not a course on Deep Learning. I'll check out those links.Darn, I think the Elements of Statistical Learning page is down. Users can also open a popup to glance at the dataset characteristics.Another nifty feature – registered users can bookmark and preview the ones they liked.When it comes to working with data, there are two options. If that is your goal then this post highlights five common mistakes programmers make on the path to quickly Programming concepts/etc were very easy for me to grasp, and everything was very interesting and put together in a way that I could follow it.However, when the topic went to "you should remember from calculus..." I got lost.

If you have debugging questions, use StackOverflow. Usually, data science communities share their favorite public datasets via popular engineering and data science platforms like Kaggle and GitHub.Users can download data in CSV or JSON, or get all versions and metadata in a zip. Remember to submit pull requests if you find bugs or add interesting features!It depends on your background. The platform also provides SDKs for R and Python to make it easier to upload, export, and work with data.It would be surprising if GitHub, a large community for software developers, didn’t have a page dedicated to datasets. This means that it’s not absolutely necessary to know linear algebra and calculus to get them to work. New and recently updated items are located in the corresponding folders.If you want to get more data by state institutions, agencies, and bodies, you can surf such websites as the UK’s Datasets that you can find within this source category can partly intersect with government and social data described below. Users with a Quandl account can choose a format for data they get. Users can also work with it in dBase, SPSS, and SAS Windows binary applications.Don’t forget to check the aggregators we mentioned earlier. You probably should, but I'll assume you know all of it.Take Andrew Ng's Coursera. Let me first start off by saying that there is no single “best way” to learn machine learning, and you should find a system that works well for you. Finally, lots of machine learning researchers are on Twitter and the Reddit Machine Learning community is a nice way to get the latest news on neural networks.

The Machine Learning Track is intended for students who wish to develop their knowledge of machine learning techniques and applications. Take Andrew Ng's Coursera ML before it and you'll be able to breeze through.Second taking Andrew Ng's ML first. These boards are organized around specific subjects. Their members communicate with each other by sharing content related to their common interests, answering questions, and leaving feedback.Datasets subreddit members write requests about datasets they are looking for, recommend sources of qualitative datasets, or publish the data they collected.
Could you please explain why do you think taking take AI before ML is worth it?New comments cannot be posted and votes cannot be castA place for discussion for people participating in GT's OMS CSPress J to jump to the feed.

And if you have never taken a statistics course (my CS undergrad didn't require it somehow), take "Intro to Descriptive Statistics" and "Intro to Inferential Statistics" on Udacity.I don't have time to do all that. For instance, Registered users can access and download data for free. The website (current version developed in 2007) contains UCI allows for filtering datasets by the type of machine learning task, number of attributes and their types, number of instances, data type (i.e. This is where you can get healthcare datasets for machine learning projects.The World Health Organization (WHO) collects and shares Source users have options to browse for data by theme, category, indicator (i.e., The CDC is a rich source of US health-related data.

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