A Beginners Guide to Machine Learning: 7 things you should know

Consider a scenario: you are listening to a specific song, and your music app automatically begins recommending songs that match the music genre of the song you are currently listening to. It is an example of a classic real-world application of machine learning. The songs you manually search for are saved as training data; the system trains on the data you provide, learns your preferences, and recommends the one that matches your taste—Fascinated by this example? Dive right in to learn more about machine learning.

What is machine learning?

Machine learning is a branch of computer science that allows computers to learn from data and mimic human decision-making without being explicitly trained.

It gives computers the ability to learn without being explicitly programmed

Arthur Samuel

Example: Training of Medical professional

Initially, the medical students did not know about Surgery or Medicine. They train their machine(brain) and learn how to treat patients (they give input and output). They feed in their machine(brain) the kind of approach or logic they have to use in different medical cases. They check for accuracy every time they give a diagnosis, compare it with similar issues solved by other doctors, and adopt new ways. Over time, they gain enough confidence to treat using the learned and adopted techniques. This is how a model in machine learning works.

Critical components of Machine Learning.

To understand the working of Machine learning, we need to know :

We need different types of datasets to build different machine learning models. Without training data, the model cannot be created. Data collection is in two ways- 

The first method is to collect data manually using surveys or forms. This is one of the error-free methods to collect data, but it is also a lengthy and expensive process.

The other method of data collection is using online tools for it. For example, we can use automated data collection methods and collect data directly from Google, Twitter, Instagram, etc.

When analyzing an object, you look for measurable properties that can be used to analyze it; this measurable property is referred to as a feature. Columns represent features when the dataset is described in tables. Variables and attributes are other names for the features.

Various machine learning algorithms specify how the system’s training and learning should take place. Different approaches are for different problems.

Algorithms include regression, classification, decision trees, and others.

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What are the types of machine learning?

There are 3 types of machine learning.

Supervised learning
In Supervised Learning, the machine has prior knowledge and has an operator to give instructions. The model is provided with a known dataset with desirable inputs and outputs, and the algorithm must find ways to arrive at those inputs and outputs. The algorithm processes the training dataset to learn the patterns which lead to the desired result. Then, the algorithm makes predictions based on the observations while learning the patterns. This process continues till the algorithm reaches the highest accuracy or performance. 

The following fall under Supervised Learning:

Classification: The machine concludes by observing the patterns in the dataset. Based on this conclusion, it decides which category the new observations belong to; such type of supervised learning is called Classification.
Example: sorting of mails as Spam and not-Spam.

Regression: A regression algorithm involves one dependent variable and a series of varying variables.
Example: Predicting stock prices, weather forecasting, etc

Unsupervised Learning: The machine has no prior knowledge nor has an operator to give instructions. The machine studies the available data, identifies patterns, and finds the correlations and relationships present in the data. Then, the algorithm attempts to organize the data to describe its structure. This could imply grouping the data into clusters or arranging it more organized manner.

The following fall under Unsupervised Learning:

Clustering: Clustering is the process of grouping together sets of similar data based on the defined criteria. It helps segment data into groups and analyzes each data set to find patterns.
Examples- Identifying fake news, Classifying network traffic, etc.

Dimension Reduction:
Dimension reduction is the process of reducing the number of variables considered to find the necessary information. It is transforming a dataset from high-dimensional space to lower ones.

Reinforcement Learning: Reinforcement Learning usually involves an agent learning to behave in a given environment by performing actions. It provides feedback and adjusts its future course based on the outcomes of those actions—

The agent receives positive feedback for each positive step, and for each negative step, the agent gets negative feedback. Reinforcement learning entails learning without the use of labeled data. Because there is no labeled data, the agent must rely solely on its own experience to learn.


Three core components of the Machine Learning Problem


Representation refers to formulating the problem as a machine learning problem, typically one of three: classification, regression, or clustering.

It also tells how to represent knowledge.

Examples are decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, etc.


An objective function, also known as a scoring function, is used to distinguish between good and bad models. The evaluation function used by a specific algorithm may differ from the external one that the classifier is supposed to optimize. This function is required for a classification problem to determine whether a given classifier is good or bad. For example, it could be the squared error or likelihood for a regression problem.

Evaluation methods include accuracy, prediction and recall, squared error, likelihood, posterior probability, entropy k-L divergence, etc.


We need a method to search among the models for the highest scoring model. Optimization refers to how we search for models or improve the labels in the training data to obtain a good evaluation.

Optimization methods are combinatorial optimization, convex optimization, and constrained optimization.

Applications of machine learning

Some applications of Machine learning are:

  1. Speech recognition
  2. Self-driving car
  3. Pattern recognition
  4. Virtual assistant:
  5. Sentiment Analysis
  6. Dynamic Pricing

The most common algorithms used.

  1. Naïve Bayes Classifier Algorithm (Supervised Learning – Classification)
  2. K Means Clustering Algorithm (Unsupervised Learning – Clustering)
  3. Support Vector Machine Algorithm (Supervised Learning – Classification)
  4. Linear Regression (Supervised Learning/Regression)
  5. Logistic Regression (Supervised learning – Classification)
  6. Artificial Neural Networks (Reinforcement Learning)
  7. Decision Trees (Supervised Learning – Classification/Regression)
  8. Random Forests (Supervised Learning – Classification/Regression)
  9. Nearest Neighbours (Supervised Learning)

What Future Holds

Machine Learning is widely used by businesses to understand how their customers feel and think about them. It is used by all of the world’s technological behemoths. Due to its relevancy, it is experiencing a massive boom.

Gartner predicts that by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercial platforms rather than open-source platforms.

Machine learning and artificial intelligence are becoming more popular as career paths for new engineers. The increased demand for data analysts and scientists is only fueling the fire. However, machine learning has a long way to go and a wide range of possibilities to explore.

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