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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?
Answer Part 1

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.

Answer Part 2

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 are the four types of machine learning?
Answer Part 1

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

Answer Part 2

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.
What is the best programming language for machine learning?
Answer Part 1

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

Answer Part 2

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.
Courses

Machine Learning Courses to Boost Your Skills

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

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)

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)

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 to Expand Your Career

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Prove your machine learning capabilities with a data science certification from Udacity.

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.

Intermediate
3 months
5-10 hours

In this program, you will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring.

Advanced
4 months
10 hours

Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.

This program is intended for students with experience in Python, who have not yet studied Machine Learning topics.

Intermediate
3 months
10 hours
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