High-tech advancements have led to the collection of massive data across industries. Collating and analyzing this data is certainly a gargantuan task, isn’t it?
Deciphering the harmony between data and machine is quite a strenuous task. This has ignited quite a spark across industries – the maximum impact has been encountered by the tech industries.
In recent times, finding desirable suggestions on videos, products, courses etc., on Netflix, Facebook, Amazon etc.can be credited to the magic of machine learning algorithms.
Essentials of Machine Learning Algorithms for Beginners
According to the current scenario machines are used to predict human behavior. For example, a machine can easily predict one’s decision-making and problem-solving expertise. This gives further reasons to understand the concepts behind various machine learning algorithms.
Are you new student of data science? Are you looking for resources to deduce the basic concepts of Machine Learning, Artificial Intelligence, Deep Learning, etc.? Then this article can help you get the hang of some critical aspects of machine learning algorithms.
Types of Machine Learning in Artificial Intelligence
To begin with, it is necessary to understand the 3 broad categories of machine learning. These include:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning: By using ‘Supervised Learning’ a machine can identify a product based on the ‘Yes’ or ‘No’ technique. A machine (model) uses variables in known data sets to make corrections in these algorithms. For example, Decision Tree.
Unsupervised Learning: On the other hand, in ‘Unsupervised Learning’, there is an absence of this known data set. Apparently, the algorithms in such a learning process are created to assess and infer the random behavior of a large group or clusters of data sets. It may appear capricious in nature but is used to help a machine interpret structures in available data clusters.
Example, Learning and predicting a customer’s purchase behavior based on their past performance.
Reinforcement Learning: The unabated characteristic of a machine to decipher a positive or negative output, using the feedback received from an agent is what creates the foundation of ‘Reinforcement Learning’.
It, more or less, reflects the rewards or punishment received by the model in an interactive environment. For example, the act of an agent making a decision of using or bypassing rules while playing a game of Go or Pacman.
How does one identify the best Machine Learning Algorithms using various programming languages? This article discusses two of the most commonly used programming languages for machine learning…
Top Machine Learning Algorithms in Python
Naive Bayes Classifier Algorithm
This model is based on the maximum probability of finding an object based on its color, type, and origin. Observed as one of the most popular documents or disease classification (Supervised Learning) training modules, Naive Bayes Classifier Algorithm works on Bayes Probability Theorem and uses Python as its base language for coding.
K-Means Clustering Algorithm in Python
Make use of this algorithm to optimize an available unlabeled database (Unsupervised Learning). You can use this algorithm to identify the anomaly in a problem statement.
Linear Regression Algorithm
As we construct a machine learning algorithms pdf or a machine learning algorithms cheat sheet, a linear regression algorithm has an important role to play. Classified under the category of Supervised Learning, this algorithm helps the assessor to find the correlation between a dependent and an explanatory/ independent variable using a linear dataset.
Top Machine Learning Algorithms in R
R is one of the most favored programming languages these days after Python. One of the most noteworthy characteristics of R is that it’s free and can be run (easily) on any system. Similar to Python, this programming language can also be used to learn algorithms in both supervised, unsupervised, and reinforcement learning. In the pretext of an open-source programming language, below are a few essential machine-learning algorithms in R:
Logistic Regression Algorithm
It is a classification-based algorithm and quite a misnomer, for it is different from the Linear Regression algorithm. Should you need to interpret a result of the dependent variable using a binary (0,1) value system, and the independent variable you can make use of this algorithm to test and train the machine learning model.
kNN Algorithm in R
Also known as k-Nearest Neighbour Algorithm. This algorithm is based on a supervised learning mechanism. Based on the available input data, the machine learning model interprets the output via analysis. It is an elementary algorithm, used by beginners in machine learning coursework.
Machine Learning Algorithm Cheat Sheet
Given the complexity and enormity of the syllabus, a machine learning cheat sheet comes in handy to all the students of data science. Some of the most erudite materials of machine learning that can be honored as the appropriate cheat sheets are:
- NumPy Cheat Sheet: An abbreviation that expands as the ‘Numerical Python’ library. This cheat sheet is quite exemplary in its elucidations of various mathematical expressions and operations used in Python language.
- Afshin Amidi’s VIP Cheat sheet (on Supervised Learning). This cheat sheet extrapolates information on the nitty-gritty of data science in its various forms. It engages topics that include- Clustering, Linear regression, Neural networks, etc. These are the topics taught in Stanford’s computer science (CS 229 class).
- Data analysis with Pandas. Another classical machine learning algorithm, the source of this cheat sheet can be routed to https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U.
By and large, machine learning algorithms books for individual use can be created by taking into account the mathematical prologue as well as the fundamentals of predictive data analytics and statistical learning mechanisms. Data science is a vast and engaging domain. Studying the different variables (labeled or unlabeled) is a strenuous process and needs an in-depth analysis by the student!
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