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## Machine Learning Algorithms

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# What Are Machine Learning Algorithms?

Machine learning algorithms fuel mass data analysis and decision making in machine learning models. Machine learning algorithms consist of three parts: a decision process that makes classifications based on input data, an error function to evaluate predictions and adjust for accuracy and a model optimization process that adds weights to various factors in order to reduce discrepancies between the model’s estimate and the example.

## 5 Most Popular Machine Learning Algorithms

1. Linear regression algorithms are used to estimate real values based on continuous variables. A relationship is established between independent and dependent variables to determine the regression line, represented by the equation Y = aX + b.
2. Logistic regression is a classification used to estimate discrete values based on a set of independent variables, which allows us to make predictions about an event's probability of occurring.
3. Decision trees are supervised learning algorithms used for classification problems that split a population into two or more homogenous sets.
4. Naive Bayes classifiers assume that any given features are unrelated to the presence of other features, thereby asserting independence between predictors.
5. K-nearest neighbors (KNN) stores all available cases before classifying new cases to the nearest neighbor with which it shares common functionality.

## What Algorithms Are Used in Machine Learning?

Common machine learning algorithms include linear regression, logistic regression, decision trees and more.

Linear regression algorithms are used to estimate real values based on continuous variables by establishing relationships between independent and dependent variables through the use of a best fit line. The best fit line is what’s known as a regression line and can be determined through the equation `Y=a*X+b`, where `Y` is a dependent variable, `a` is the slope, `X` is the independent variable and `b` is the intercept.

Logistic regression is used to estimate discrete values based on independent variables, such as yes/no or true/false equations.

Decision trees are a supervised learning algorithm used for classification problems, especially when working with categorical and continuous dependent variables.

Some other commonly used machine learning algorithms include naive Bayes, KNN, K-Means, random forestgradient boosting algorithms.

More on Machine Learning AlgorithmsThe Top 10 Machine Learning Algorithms Every Beginner Should Know

## What Are Data Science Algorithms?

Common data science algorithms include several variations of search and sort algorithms.

Understanding how algorithms work in data science requires knowledge of Big O notation, which we use to classify algorithms according to how their run time or space requirements grow with the input size. This proess is crucial for selecting the right algorithms for the right workflow. We typically use data science algorithms  to either search through data or sort data elements.

• Simple search involves searching every item until the element of interest is located.
• Binary search begins at the sorted data’s midpoint to compare the target value to the middle value and only searches through the half of the data in which the value is located. This division process continues until the value is located.
• Sort algorithms include selection sort, which goes through a list and appends each element to a new list in the required order.
• Quicksort divides original lists into continuously smaller lists that are then combined to result in a larger, ordered list.
• Mergesort breaks lists into individual elements to create ordered pairs. These pairs are then grouped into ordered groups of four until a final merged list is created.
Courses

## Expand Your Machine Learning Algorithms Career Opportunities

Learn machine learning algorithms and other top data science skills with Udemy’s top-rated courses.

Udemy

Topic:

What you'll learn:

• In this hands-on project based course, students will learn fundamentals and actual…

4.1
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Udemy

Topic:

Mastering Machine Learning Algorithms including Neural Networks with Numpy, Pandas, Matplotlib, Seaborn and Scikit-Learn

What you'll learn:

• Have an understand of…

4.2
(301)
Udemy

Topic:

Master Class of Data Science with Machine Learning using Python

What you'll learn:

• The course provides path to become a data scientist

4.4
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Udemy

Topic:

Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression

What you'll learn:

• Apply SVMs to practical applications: image…

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Certifications

## Machine Learning Algorithms Certifications + Programs

Take your career to new heights by earning a data science certification from Udacity.

General Assembly’s Data Science part-time course is a practical introduction to the interdisciplinary field of data science and machine learning, which lies at the intersection of computer science, statistics, and business. You will learn to use the Python programming language to acquire, parse, and model data for informing business strategy.

This is a fast-paced course with some prerequisites. Students should be comfortable with programming fundamentals, core Python syntax, and basic statistics. There is an option to complete up to 25 hours of online preparatory lessons. Talk to the General Assembly Admissions team to discuss your background and confirm if this is the right fit for you..

What you'll accomplish

A significant portion of the course is a hands- on approach to fundamental modeling techniques and machine learning algorithms. You’ll also practice communicating your results and insights by compiling technical documentation and a stakeholder presentation. Throughout this expert-designed program, you’ll:

• Perform exploratory data analysis with Python.
• Build and refine machine learning models to predict patterns
• from data sets.
• Communicate data-driven insights to technical and non-technical audiences alike.
• Apply what you’ve learned to create a portfolio project: a predictive model that addresses a real-world data problem.

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

Your next step? Submit an application to talk to the General Assembly Admissions team

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

Udacity
Intermediate
3 months
5-10 hours

General Assembly’s Data Science Immersive is a transformative course designed for you to get the necessary skills for a data scientist role in three months.

The Data Science bootcamp is led by instructors who are expert practitioners in their field, supported by career coaches that work with you since day one and enhanced by a career services team that is constantly in talks with employers about their tech hiring needs.

What you'll accomplish

As a graduate, you will be ready to succeed in a variety of data science and advanced analytics roles, creating predictive models that drive decision-making and strategy throughout organizations of all kinds. Throughout this expert-designed program, you’ll:

• Collect, extract, query, clean, and aggregate data for analysis.
• Gather, store and organize data using SQL and Git.
• Perform visual and statistical analysis on data using Python and its associated libraries and tools.
• Craft and share compelling narratives through data visualization.
• Build and implement appropriate machine learning models and algorithms to evaluate data science problems spanning finance, public policy, and more.
• Compile clear stakeholder reports to communicate the nuances of your analyses.
• Apply question, modeling, and validation problem-solving processes to data sets from various industries to provide insight into real-world problems and solutions.
• Prepare for the world of work, compiling a professional-grade portfolio of solo, group, and client projects.

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here.

Your next step? Submit an application to talk to the General Assembly Admissions team

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

Udacity
Intermediate
3 months
5-10 hours

In this program, students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom.

Udacity
Intermediate
3 months
5-10 hours

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