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Machine learning is starting to redefine the way we live, here we detailed why it matters most right now. Machine learning is nothing but an application of Artificial Intelligence (AI). It provides systems the ability to automatically learn and improve from expertise without being expressly programmed. Machine Learning gives computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

You encounter machine learning almost everyday, think about

• Ride sharing apps like Lyft & Uber – How do they determine the price of your ride?
• Google maps – How do they analyze traffic movement and predict your arrival time within seconds?
• Filter spam – Emails going automatically to your spam folder?
• Amazon Alexa, Apple SIRI, Microsoft Cortana & Google Home – How do they recognize your speech?

Machine Learning can be categorized into three parts :

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

1. Supervised Learning:

Supervised Learning algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

2. Unsupervised Learning:

In Unsupervised Learning algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.

3. Reinforcement Learning:

Using this Reinforcement Learning algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

Languages suited for Machine Learning

• Python (best for both beginner and advanced level)
• R (good but slow run time)
• Matlab (good but costly and slow)
• Julia (Future best! very fast, good, limited libraries as it is new)
• C++ (difficult, very fast, used in production)

Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. SVM
5. Naive Bayes
6. kNN
7. K-Means
8. Random Forest
9. Dimensionality Reduction Algorithms
10. Gradient Boosting algorithms

• GBM
• XGBoost
• LightGBM
• CatBoost