This is your chance to get all your questions on AI answered. Don’t feel shy, just ask away. No question is too simple or too complicated for us to answer. We just want you to learn everything you ever wanted to know about AI. For the Webinar held on 27-07-2019, these are the questions that you asked us and we answered:
- Which Python libraries should I learn before learning machine learning?
- In computer science research, how can we make a results/experiments
comparison? Should we use the same data set or implement any other
- What exactly does data science means? Is it really going to revolutionise the
- Is it possible to predict using a fewer number of features than the number of
features that were used in training the model?
- What are the linear models in machine learning to do classification?
- What are K-means and K-medoids in data science?
- What are the parameters W, X and b represent in the computation of a machine learning algorithm?
- What are the different types of supervised learning? What is the difference between supervised and unsupervised learning?
- For machine learning, what type of mathematics do I have to know?
- Could you suggest some good books for AI and machine learning?
- What are some good machine learning /AI courses available on YouTube?
- Why is it advisable to start with Python if you want to learn machine learning?
- Which are some of the suggested courses to learn data structures and algorithms (irrespective of programming language)?
- Why is it necessary to clean the data before developing a machine learning model?
Feel free to ask us more questions! We will do our best to answer them as comprehensively as possible. Send in your questions here – http://aiforwomen.org/contact/
Article contributed by: Vinita Silaparasetty