So This is Day 1 of Machine learning with Python course…Although I did Andrew ng’s 1st 3 weeks of the old ML course .I didn’t continue the course since it was in octave, even though there are some nice github repo’s with python alternative’s of the programming assignment’s….I decided to go forward with this course. Also I would be enrolling in the “Deep learning specialization” by Andrew Ng after this course.

Here is the summary of what i learned today……….

What is Machine learning?

Machine learning is the subfield of computer science that gives “computers the ability to learn without being explicitly programmed.”

Let us Assume that you have a dataset of images of animals such as cats and dogs and we want to an application to differentiate between them. Using machine learning, allows us to build a model that looks at all the feature sets, and their corresponding type of animals, and it learns the pattern of each animal .It is a model built by machine learning algorithms. It detects without explicitly being programmed to do so. Machine learning follows the same process that a 4-year-old child uses to learn, understand, and differentiate animals. So, machine learning algorithms, inspired by the human learning process, iteratively learn from data, and allow computers to find hidden insights .These models help us in a variety of tasks, such as object recognition, summarization, recommendation, and so on.

One of the real life example’s of machine learning is Amazon and netflix recommending vidoes , movies ,TV shows based on the user’s liking.

A few of the more popular techniques

a)The Regression/Estimation technique is used for predicting a continuous value. For example, predicting things like the price of a house based on its characteristics, or to estimate the Co2 emission from a car’s engine.

b)A Classification technique is used for Predicting the class or category of a case, for example, if a cell is benign or malignant.

c)Clustering groups of similar cases, for example, can find similar patients, or can be used for customer segmentation in the banking field.

d)Association technique is used for finding items or events that often co-occur, for example, grocery items that are usually bought together by a particular customer.

e)Anomaly detection is used to discover abnormal and unusual cases, for example, it is used for credit card fraud detection.

f)Sequence mining is used for predicting the next event, for instance, the click-stream in websites.

g)Dimension reduction is used to reduce the size of data.

h) Recommendation systems, this associates people’s preferences with others who have similar tastes, and recommends new items to them, such as books or movies.

Difference between AI vs ML vs DL

AI tries to make computers intelligent in order to mimic the cognitive functions of humans. So, Artificial Intelligence is a general field with a broad scope including: Computer Vision, Language Processing, Creativity, and Summarization.

Machine Learning is the branch of AI that covers the statistical part of artificial intelligence. It teaches the computer to solve problems by looking at hundreds or thousands of examples, learning from them, and then using that experience to solve the same problem in new situations.

Deep Learning(Revolution in Machine learning)is a very special field of Machine Learning where computers can actually learn and make intelligent decisions on their own. Deep learning involves a deeper level of automation in comparison with most machine learning algorithms.

Supervised learning vs unsupervised learning

In Supervised learning we load the model with knowledge so that we can have it predict future instances .We teach the model by training it with some data from a labeled dataset. Which looks something like this….

There are two types of supervised learning techniques:

They are classification, and regression. Classification is the process of predicting a discrete class label, or category.

Regression is the process of predicting a continuous value as opposed to predicting a categorical value in classification.

This dataset is related to CO2 emissions of different cars. It includes; engine size, cylinders, fuel consumption, and CO2 emission of various models of automobiles. Given this dataset, you can use regression to predict the CO2 emission of a new car by using other fields such as engine size, or number of cylinders.

In Unsupervised learning We do not supervise the model, but we let the model work on its own to discover information that may not be visible to the human eye. It means, the unsupervised algorithm trains on the dataset, and draws conclusions on unlabeled data. Generally speaking, unsupervised learning has more difficult algorithms than supervised learning since we know little to no information about the data, or the outcomes that are to be expected. Dimension reduction, density estimation, market basket analysis, and clustering are the most widely used unsupervised machine learning techniques.

Clustering is considered to be one of the most popular unsupervised machine learning techniques used for grouping data points, or objects that are somehow similar.

Cluster analysis has many applications in different domains, whether it be a bank’s desire to segment his customers based on certain characteristics, or helping an individual to organize in-group his, or her favorite types of music. Generally speaking though, clustering is used mostly for discovering structure, summarization, and anomaly detection.

So that’s all for today………