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What is machine learning?

Machine learning is an advanced form of data analysis and a branch of artificial intelligence that replicates human learning through the use of large data sets and algorithms. Machine learning is designed to gradually improve over time through repeated actions that train algorithms on how to produce outcomes based on referential and repeating data. Many forms of common technology make use of machine learning, such as search engines, self-driving cars and virtual assistants.

What is machine learning used for?

  • Machine learning algorithms have many applications in modern technology,  including powering speech recognition technology, acting as the AI in recommendation engines, facilitating automated stock trading and more.

Machine learning is crucial to bridging the gap between user intention and technological output. The key to machine learning is its ability to improve over time as more data becomes available and is provided to the system’s algorithm to analyze.

A machine learning algorithm basically consists of three parts:

  • a decision process, which makes predictions and classifications based on input data;
  • a loss function, which evaluates a prediction of the model and compares it against other known examples to measure performance;
  • and a model optimization process, which applies and adjusts the model’s parameters to reduce discrepancies between the example and the model’s prediction. 

Machine learning algorithms have many applications in modern technology. Some common examples include powering speech recognition technology, acting as the AI in recommendation engines, facilitating automated stock trading and more.

What is the best programming language for machine learning?

  • Python is the most commonly used programming language in machine learning, with R, Java, Julia and C++ also used.

Python is used by more than 8.2 million developers worldwide, making it the most popular programming language currently in use. Accordingly, Python is the language of choice for machine learning, with corporations like Meta, Google, Netflix, Disney and others all relying on the language to power their models. 

Widespread adoption of Python in machine learning is due to more than just popularity, however. Here are a few more reasons Python is favored in machine learning:

  • Built-in libraries and packages make building models easier and reduce the development time required to produce already complicated models.
  • Python also offers a high degree of flexibility. It is object-oriented, procedural, functional and imperative in nature, allowing developers to take the approach they are most comfortable with when creating machine learning models.
  • Python’s high degree of flexibility facilitates more streamlined collaboration with other programmers when working on large-scale, supervised machine learning projects.

What are the four types of machine learning?

  • The four types of machine learning include supervised, semi-supervised, reinforced and unsupervised learning.

The types of machine learning refer to the amount of human intervention required to ensure the model’s accuracy over time.

  • Supervised learning is when the machine is taught by example. Operators provide an algorithm with a known data set that includes the inputs and outputs required. The operator knows the answers to the problem and the algorithm identifies patterns and makes predictions. The operator then corrects the algorithm and repeats the cycle.
  • Semi-supervised learning is the same as supervised learning but uses both labeled and unlabeled data so the algorithm can become familiar with more complex decisions.
  • Unsupervised learning involves the algorithm studying data to identify patterns without a clear answer provided, allowing it to determine correlations independently with only the data it has available, as well as historical knowledge, and organize it in structures. Unsupervised learning has three primary uses, training natural language processing models, clustering similar data for segmentation and reducing the number of variables needed to find the correct information, known as dimension reduction.
  • Reinforcement learning is when a machine is provided with a set of actions, parameters and end values to explore different options to determine the optimal solution. It is essentially trial and error on a much larger scale, performed in an efficient manner.
Courses

Expand Your Machine Learning Career Opportunities

Learn machine learning skills and boost your data science capabilities with top-rated courses from Udemy.

Udemy

Topic:

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

 

What you'll learn:

  • Master Machine Learning on…

4.5
(154979)
Udemy

Topic:

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

 

What you'll learn:

  • Use Python for Data…

4.7
(109649)
Udemy

Topic:

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

 

What you'll learn:

  • Build artificial…

4.7
(26571)
Udemy

Topic:

Learn how to use the R programming language for data science and machine learning and data visualization!

 

What you'll learn:

  • Program in R

4.7
(14305)
Certifications

Machine Learning Certifications + Programs

Prove your machine learning capabilities with 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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