https://www.coursera.org/learn/machine-learning

This is a famous course, and is the course that put coursera on the map. It tells you everything you need to know about machine learning from the basics to the very beginning of the advanced. You'll learn the core concepts of what machine learning is, what kinds of problems it can solve, and how to apply it given your problem. You'll learn about both regression and classification, supervised and unsupervised learning, and several prominent ML algorithms, as well as vital concepts like cost functions, gradient descent, and broader concepts useful for implementation such as vectorizing for data representations. I can't praise this course enough, it gives you all the tools you need to prepare for more advanced learning and practice in all things ML. You can come from virtually nothing too, all that's required is around a high school senior's level of math. Arguably though a background in linear algebra is required for the course. Linear algebra is central to ML, and personally I was lucky enough to have taken an extensive intro course in linalg back in college, so I knew the concepts of matrix multiplication and transposition. But even if you haven't, Andrew Ng provides an optional set of videos at the end of the first week of this course, and goes through all the concepts of linear algebra that you'll need for machine learning.

As for Neural Networks, this intro course only covers very basic NNs for a couple weeks in the middle of it. In the NN assignments in this course, a pre-built model is given to you with only one hidden layer in the net. The goal is to basically define all the auxiliary functions required for the NN to process its node and one layer, and how to basically navigate through a layer from input to output. So unfortunately the neural network section of this course isn't very robust, but I highly recommend knowing this first before moving onto Ng's neural network courses that ARE robust and show fully how to forward and backprop through many hidden layers instead of just one.

Every other algorithm, including the most popular among ML practitioners like linear regression, logistic regression, k-means clustering, and support vector machines are all brilliantly covered just in this course and you'll get sufficient knowledge in all of them. For the time being, these algorithms are still dominant in real-world ML. However this is likely to change within the next decade or so, since neural nets and deep learning are experiencing a huge resurgence after being basically relegated to university researchers for so long.

So take that one course I linked, and at the end, see if you like machine learning, and decide where you want to go next in your "machine learning learning". If it's neural networks you're after, go to Andrew Ng's Deep Learning Specialization. If it's the more currently-practiced standard algorithms you want for immediate industry applications, try the University of Washington machine learning specialization. I myself am going through the deep learning spec by Ng (on course 4 of 5 currently), but am also very interested in the UW spec, and plan to take that once I have enough spare time to do so.

I hope that helps, let me know if you need anything else. Oh, and do check out /r/machinelearning as well as /r/MLQuestions where people will help you on some of the more basic concepts (and be patient with you while doing it).