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Could someone tell me whether Machine Learning and Big Data are the same or different?

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They are distinctive however complementary. The Big Data is about Data and ML is about Logic. ML gives the algorithms to handle big data. Big Data is portrayed by 4 Vs. These are Volume (in exabytes), Velocity (gushing in heavily at an enormous rate), Variety (web-based media, rfid, weblogs, recordings, pictures, and so on), and Veracity (inadequate or inaccurate now and again). Such tremendous data can not be physically handled through deterministic algorithms. It requires utilizing 'smart' calculations (algorithms), which can handle all that information to concoct valuable noteworthy insights in real-time. ML algorithms attempt to copy nature: like Artificial Neural Networks, or the Genetic Algorithms. There are different calculations like Computer Vision, and Natural Language Understanding, which are near ML and fall inside the bigger umbrella of 'AI'.

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There is a huge difference between ML and Big Data. Basically Machine Learning is a part of AI and it is a field of study where we estimate the unknown future values from the features of the given dataset. Whereas Big Data is just used to analyse the raw data and check for the useful patterns for better decision making for any industry.

In ML, the data is very limited but in case of Big Data, data is available in a very huge amount.

Anyone having a basic knowledge of Python can go for the ML but for Big Data, a person must have a good hands on practice in the field of programming.

ML not need humans after the perfect implementation of algorithms during the training, whereas Big data always need humans to analyse the data and extract the better insights from that.

The future of ML is to increase the quantity of robots or to make the predictive analysis better. Whereas the future of the Big Data is to structure the huge amount of data for better analysis.

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