It's exactly like other discipline areas of programming - anyone can learn. It's a matter of how much time you spend on learning ML, what you want to get out of it, and how excited you are to keep at it.

Yes and no. Machine learning is a bit like an advanced generalisation of linear regression. And it's successful in a number of "use cases" (that may be hyped up), but the areas where it isn't successful don't get into the news. There are a lot of reasons why machine learning might not end up being the best choice, such as the following:

• the cost of getting the data vs the potential revenue gain from the data: in many cases the ratio between these to isn't known, so it's difficult to build a business case from it. data could be scattered all over the organization, and may not always be in nice "Excel tables" (it could be locked up in images, emails, handwritten log books, etc.)
• not having enough data points
• data quality, the signal to noise ratio in the data
• algorithms become "good" because a tradeoff is being made (the tradeoff makes the algorithm better at one particular problem, but this comes at the expense at poorer performance at other problems). This argument follows the "no free lunch theorem" (which has been mathematically proven) explained here. another example is that if there's already a physics-based model which describes the data, the machine learning model could fail to outperform it.
• extrapolation: regression works best when you interpolate between data points within training data, but starts to perform poorly when you need to extrapolate (make predictions outside the data it was trained on). Likewise, machine learning models tend to perform well within the training data, but if the new data has little resemblance to the original training set, expect the predictive performance to be poor.

So while machine learning is going to be awesome in some specific areas, be also aware that the non-success stories don't get hyped up.

Yes, its true. Initially, when I was looking forward to learn machine learning, I was thinking, "it sounds difficult so it must be difficult". But, after diving into it, I realized, all you need to know is math and logic and you will just learn it.

There's no such thing as a hard problem. Only ones which have not been broken down into smaller problems enough.

Yes, he is totally right.

ML is like Array map function, but more complicated. Today you also have a lot of ML libraries, so you don't need to know how it works inside. The most critical part you need in ML is data on input, then in the middle there is your model or in simple words - what do you want to extract from the data and finally at the end you will see some results.

However, to get best results in your unique case and for a bit complicated and custom scenarios, you need to know at least basics of the machine learning, math, analytics and statistics.

Moreover, for training models or making them better, you need not just data, but filtered (labeled) data so algorithm could extract value from a lot of labeled data sets, for example, if I will show you 2 pictures with some object in language you do not speak, you won't be able to learn how to say those objects, so I need also to provide you words for each picture (labels) and finally when you will have a lot of pictures with words, you will have high chance (probability) to tell me what you see on the next picture without a word I will show you. So in simple words, AI is a hype and myth, human needs to do a lot of job first.

Currently taking several ML courses. In reality if you can understand basic math, maybe some calc, and a little linear algebra. The concepts are really all you need to grasp. Learning to code is more to do with your knowledge of the language and the libraries available to you. Depending on what language you choose getting started with ML can be very easy or it could be very time consuming.

The basics of ML is to make the computer think like we do, so true it's not hard in that sense and of course with enough training it's possible, otherwise it would be impossible.