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Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions. We know humans learn from their past experinces and machins follow instructions given by humans but what if humans can turing the machins to learn from the past data and to what humans can do act much faster, well that’s called Machin Learning but it’s a lot more than just learning it’s also about understanding and reasoning. Today we will learn about the basics of machin learning.

Machine learning

Let’s take an example of a human named as Paul, he loves listening to new songs he either likes them or dislikes, that Paul decides this on the basics of the song’s tempo johner intensity and the gender of the voice for simplicity let’s just use tempo and intensity for now, so here tempo is on x-axis ranging from relaxed to fast whereas intensity is on the y-axis ranging from light to soiling, we see that Paul likes the song with fast tempo and soaring intensity while he dislikes a song with relaxed and light intensity so now we know Paul’s choices. Let’s see Paul listens to a new song let’s name it a song a song “A” song, “A” has fast tempo and soaring intensity so it lies somewhere here looking at the data can you guess where the ball will like the song or not correct, so Paul likes the song by looking at Paul’s past choices we were able to classify the unknown song very easiely right let’s say now Paul listens to new song let’s lable it as song “B”. So song “B” lies somewhere here with medium tempo and medium intensity neither relaxed nor fast, neither light nor soaring now can you guess where the Paul likes it or not, not able to guess with this Paul will like it or dislike it other choice is unclear, correct we could easily classify song “A” but when the choice became complicated as in the case of song “B”

 How We Arrived at Our Definition:

As with any concept, machine learning may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practical definitions from reputable sources:

1. “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – Nvidia
2. “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford
3. “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co.
4. “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington
5. “The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” – Carnegie Mellon University


Machine Learning Basic Concepts :

There are many different types of machine learning algorithms, with hundreds published each day, and they’re typically grouped by either learning style or by similarity in form or function. Regardless of learning style or function, all combinations of machine learning algorithms consist of the following:

Representation (a set of classifiers or the language that a computer understands) Evaluation (aka objective/scoring function) Optimization (search method; often the highest-scoring classifier, for example; there are both off-the-shelf and custom optimization methods used)

Challenges and Limitations :

The two biggest, historical (and ongoing) problems in machine learning have involved overfitting (in which the model exhibits bias towards the training data and does not generalize to new data, and/or variance i.e. learns random things when trained on new data) and dimensionality (algorithms with more features work in higher/multiple dimensions, making understanding the data more difficult). Having access to a large enough data set has in some cases also been a primary problem.

One of the most common mistakes among machine learning beginners is testing training data successfully and having the illusion of success; Domingo (and others) emphasize the importance of keeping some of the data set separate when testing models, and only using that reserved data to test a chosen model, followed by learning on the whole data set.

When a learning algorithm (i.e. learner) is not working, often the quicker path to success is to feed the machine more data, the availability of which is by now well-known as a primary driver of progress in machine and deep learning algorithms in recent years; however, this can lead to issues with scalability, in which we have more data but time to learn that data remains an issue.

In terms of purpose, machine learning is not an end or a solution in and of itself. Furthermore, attempting to use it as a blanket solution i.e. “BLANK” is not a useful exercise; instead, coming to the table with a problem or objective is often best driven by a more specific question – “BLANK”.


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