Machine Learning Cheat Sheet

Introduction to Machine Learning Cheat Sheet

Machine learning is the art of artificial intelligence. The major of machine learning is to make the peeps understand the current Machine learning trends and allow them to understand the raw data. With a better understanding of raw and various structures of data, they will use the data into machine learning models that can be utilized by people and organizations.

What is Machine Learning?

In classical computation, algorithms are the basis written as programmed instruction in such a way that computers can understand and solve the problem. In another hand, Machine learning Algorithms make the computer to train on the available data scrapped and use them to provide in-depth statistics and analysis reports within a specified range. With Machine learning algorithms the computer will tend to make an automated decision based on the data inputs it got trained.

In the Modern technological world with a lot of innovation and growth in the field of artificial intelligence & machine learning, it majorly benefits society by more innovative inventions. Current technology has more effective face recognition technology, voice-over assistants, effective chatbots, Recommendation engines, and the most significant achievement is Self-Driving cars that understand and navigate the passenger to the destination.

Machine Learning is a rapidly growing and continuously developing field. This article will cover the common machine learning algorithms and techniques vastly used in the Artificial Intelligence industry such as (supervised and unsupervised learning, random forest, k-nearest neighbor, and basics of deep learning). We will also deep dive into the list of programming languages widely used in machine learning and also explore which is more suitable for the given task in hand.

Importance of Machine Learning Cheat Sheet

• Machine learning is a Model building strategy that will make the organization gain information and make a data-driven decision in an efficient way It is a modeling framework which extracts raw information from the unstructured data and provides insights that make the organization make effective decisions.
• Machine learning is going to have bloomed on the economy as it can derive many patterns from the data which helps the organization to foresee business decisions to be made.
• Many Organization and enterprises work with lots of information have stepped into machine learning. It will help them unveil their unstructured data and acquire knowledge from the model findings. With the findings from data, it will help the organization to work more effectively and be in favorable positions over its market competitors.

Machine Learning Types & Methodology

In Machine learning, tasks were classified broadly into three paradigms. The most widely used and adopted machine learning methods were supervised learning and unsupervised learning.

Supervised Learning

1. In supervised learning, the model trains algorithms based on the training input and output data which is labeled. The main purpose of these algorithms is to learn by comparing its output with the training data. It will find errors and update the model accordingly with the patterns it uses to predict label values.

2. A most common case of supervised learning is the usage of historical data to predict the futuristic events. It uses historical information to provide insights of the market information and its trend. A classic example of supervised learning is to find the customer churn rate, determining the spam email classifier.

3. The supervised Machine learning algorithm can be grouped into two main buckets. It can be grouped as regression and classification problems.

• Classification: It can be defined in such a way that it will solve the problem which brings the labeled categories as the output variable. An example can be a classification between ham or spam email.
• Regression: It represents the output variable is continuous. A suitable example would be predicting the price of the house.

4. The most common supervised algorithms were listed below:

• Decision Trees
• Naive Bayes Classification
• Support vector machines for classification problems
• Random forest for classification and regression problems
• Linear regression for regression problems
• Ordinary Least Squares Regression
• Logistic Regression
• Ensemble Methods

Unsupervised Learning

1. In unsupervised learning, data will not have labels so that the learning will be based on the common features among its input data. It will not have any specific labels to be the output of the model. The machine learning methods will have more value for the unsupervised algorithms.

2. The unsupervised algorithm is to model the underlying structure and it will make the model to learn more about the data.

3. There is no right answers in the unsupervised learning algorithm. These algorithms are left to discover and identify the important and interesting insights in data.

4. Unsupervised learning problems can be further grouped into clustering and association problems.

• Clustering: A clustering algorithm will help you to discover the grouping and bonding in the data, an example will be the customer online shopping purchase behavior.
• Association: Association rule is where the new rules will be discovered that will describe the clustered portions of data.

5. Some of the classic unsupervised learning algorithms were listed:

• Clustering Algorithm: Hierarchical clustering, K-means clustering
• Neural Networks
• Deep learning

Benefits of Machine learning Algorithms

1. Machine learning has landed its foot in many domains and in all types of industries like retails, enterprise, healthcare, travel and hospitality, Finacle services, energy, and utilities.

2. Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. Machine Learning algorithms are good at handling data that are multi-dimensional and multi-variety, and they can do this in dynamic or uncertain environments.

3. Below mentioned are the fields where Machine learning use cases solve the industry problems:

• Manufacturing: Predictive maintenance and Monitoring.
• Retail: Channel marketing.
• Healthcare and life sciences: Disease detection.
• Travel and hospitality: Trend and Service pricing.
• Financial services: Risk Analytics and Fraud detection.
• Energy: Energy demand and supply optimization.

Conclusion – Machine Learning Cheat Sheet

Machine Learning is going to have huge effects on the economy and living in general. Entire work tasks and industries can be automated, and the job market will be changed forever. Machine learning is the next big thing that will have more growth in the industry and improve the economy. Entire manual works will be automated and will be taken over by machine learning applications. If you want to step into machine learning, this is the right time because the country is in desperate need of machine learning engineers. Our future will grow economically and technically and thanks to machine learning and artificial intelligence.

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