Machine Learning, Artificial Intelligence, data science, and deep learning – These are a few buzzwords that you must have come across at least once if you are remotely active on the internet. These topics are gaining mass popularity with every passing day, and a lot of people are trying to jump into the industry and bag a career in these fields, while there is still a huge number of opportunities available as well as opening up.
According to a recent survey published by Fortune, positions for AI specialists have grown by 74 percent over the last 5 years. These fields have currently become the hottest jobs in the present market. So you must be thinking, how to grab a job for yourself in this industry? Well, worry not anymore, in this article we shall be sharing everything that you need to know about how to start your machine learning journey!
There are mainly 3 ways through which you can start your machine learning journey –
- Supervised Learning –
This training method comes with a set of specially labeled data sets. These sets of data can either be multi-class classified or come with binary classification. The model shall be trained beforehand under strict supervision, and with the help of those labeled data. Various types of online classes teach you how to do machine learning with a python course. Thus this learning procedure can further be classified into 2 types of algorithms –
- Classification – This type of algorithm is preferred in a situation when the output comes with a particular category or a choice. For instance, filtering email spam is a type of classification problem.
- Regression – This type of algorithm is preferred in a situation where the output variable comes with an actual value. For instance, an example of this can be the prediction of house prices for a specific locality.
2. Unsupervised Learning –
Unsupervised learning is when the model of training is based on an unlabeled data set. This basically means that the model comes with no prior information. It is capable of training itself just by grouping the same characteristics or patterns together. For instance, imagine a situation where you have to categorize a group of dogs and cats. Thus the data given to you will be an unlabeled data set consisting of images of dogs and cats. The unsupervised algorithm shall quickly research the image to find similarities in the pattern, and thus group the images of dogs separately from the images of cats. The main types of clustering algorithms are –
- Clustering – It is where similar entities are grouped into cluster formations. For instance, the scenario of grouping dogs separately from the dogs is an example of clustering.
- Association – It is where you associate similar patterns between more than 2 classes. For instance, if you want to watch a movie of a specific genre, you will be given recommendations based on what the viewers of the same movie watched next.