ML for Amateurs

What is Machine Learning ?

Before you go for some grocery shopping what do you do before going to the market? I always prepare a list of ingredients beforehand. Also, I make the decision according to the previous purchasing experience. Then, I go and purchase the items. But, with the rising inflation, it’s not too easy to work in the budget. I have observed that my budget gets deviated a lot of times. This happens because the shopkeeper changes the quantity and price of a product very often. Due to such factors, I have to modify my shopping list. It takes a lot of effort, research and time to update the list for every change. This is where Machine Learning can come to your rescue.

Machine learning is one of the most exciting technologies that one would have ever come across. It makes the computers more similar to humans: The ability to learn. Machine Learning algorithms are trained over instances or examples through which they learn from past experiences and also analyse the historical data. Machine learning is actively being used today, perhaps in many more places than one would expect.

Machine Learning Models

In Machine Learning there are different models that generally fall into 3 different categories:

1. Supervised Learning: It makes the machine learn explicitly, in this model, data with clearly defined output is given to the computer. The supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’. Techniques such as linear or logistic regressions and decision tree classification fall under this category of learning.
2. Unsupervised Learning:In Unsupervised Learning, there is no labeled data. The algorithm identifies the patterns within the dataset and learns them. The algorithm groups the data into various clusters based on their density. In general, unsupervised learning is a bit difficult to implement and thus it’s not used as widely as supervised learning. Most popular types are clustering and association as below.
3. Reinforcement Learning: This strategy built on observation and trial & error to achieve goals or maximise reward. The agent makes a decision by observing its environment. If the observation is negative, the algorithm adjusts its weights to be able to make a different required decision the next time. Reinforcement learning algorithms try to find the best ways to earn the greatest reward. Rewards can be winning a game, earning more money or beating other opponents.

Applications of Machine Learning

Machine learning is actively being used today, perhaps in many more places than one would expect. We probably use a learning algorithm dozens of time without even knowing it. Applications of Machine Learning include:

1. Self-driving cars: One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving.
2. Stock Market trading: Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning's long short term memory neural network is used for the prediction of stock market trends.
3. Product recommendations: Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user. Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser and this is because of machine learning.
4. Virtual Personal Assistant: We have various virtual personal assistants such as Google assistant, Alexa, Cortana, Siri. As the name suggests, they help us in finding the information using our voice instruction. These assistants can help us in various ways just by our voice instructions such as Play music, call someone, Open an email, Scheduling an appointment, etc.
5. Online Fraud Detection: Machine learning is making our online transaction safe and secure by detecting fraud transaction. Whenever we perform some online transaction, there may be various ways that a fraudulent transaction can take place such as fake accounts, fake ids, and steal money in the middle of a transaction. So to detect this, Feed Forward Neural network helps us by checking whether it is a genuine transaction or a fraud transaction.

Some Machine Learning Frameworks

Your choice of machine learning frameworks depends entirely on the algorithm requirements, your expertise, and the client’s budget.

1. TensorFlow
2. Google Cloud ML Engine
3. Sci-Kit Learn
4. PyTorch
5. H2O