Can you list some real-world applications of machine learning?
Many machine-learning applications exist, and a variety of machine-learning algorithms are accessible for learning, some of them are
One of the most popular applications for Machine learning is Image Recognition. The output of each pixel in an image is described by the measurement for what is also known as a digital image. Another amazing feature that has been created solely by machine learning is face recognition. It is beneficial to be able to identify a face and notify people accordingly.
The development of the voice recognition application benefits from machine learning (ML). They are also known as virtual personal assistants (VPA). When you ask over the voice, it will assist you in finding the information. After you ask a question, the assistant will search for the facts or information you requested and gather the necessary data to provide you with the best response. In today's machine learning world, there are various voice recognition devices available, including smart speakers like the Amazon Echo and Google Home. Samsung S8 and Bixby are two smartphones with the Google Allo mobile app.
It enables the development of technology that predicts the cost of a cab ride or a particular trip for a given time frame. Only machine learning is used when the cab is booked and the app predicts the approximate cost of the trip. When we use GPS services to check the route from source to destination, the app will show us the many routes to take and check the traffic at that specific time for the fewest number of vehicles and where there is more congestion of traffic. By using machine learning tools, that is accomplished or retrieved.
What are the different types of machine learning algorithms?
Machine learning algorithms can be classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning.
Supervised learning is a form of machine learning in which output is predicted by machines using well-labeled training data that has been used to train the machines. The term "labeled data" refers to input data that has already been given the appropriate output. In supervised learning, the training data given to the computers serve as the supervisor, instructing them on how to predict the output correctly.
In semi-supervised learning, a significant amount of unlabeled data is combined with a small amount of labeled data throughout the training process. Unsupervised learning (with no labeled training data) and supervised learning are both types of learning (with only labeled training data). It is a unique case of poor supervision.
Unsupervised learning is a machine learning technique in which models are not supervised using a training dataset. Models instead use the provided data to uncover hidden patterns and insights. It is comparable to learning that occurs in the human brain while learning new information.
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents have to take action in an environment in order to maximize the concept of cumulative reward. Reinforcement learning is one of the three basic machine learning paradigms alongside supervised learning and unsupervised learning.
What is bias in machine learning?
While making predictions, a difference occurs between prediction values made by the model and actual values or expected values. To conclude, this difference is known as bias in machine learning.
What is ILP?
Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will produce a program that includes all the good instances and none of the negative ones.
What are the 3 stages of building a model in machine learning?
The three stages of building a machine learning model are:
Choose a suitable algorithm for the model and train it according to the requirements.
Check the accuracy of the model through the data tests.
Applying the Model
Make the changes required after testing and use the final model for real-time projects
Here, it’s important to remember that once in a while, the model needs to be checked to make sure it’s working correctly. It should be updated by making the necessary changes.
What situations in machine learning require regularisation?
One of the most crucial concepts in machine learning is regularisation. It is a method for preventing the model from overfitting by providing it with more data.
When using training data, a machine learning model may sometimes perform well, but when using test data, it may not. When dealing with unseen data by introducing noise in the output, it means the model is unable to anticipate the result, leading to the term "overfitted" for the model. This issue can be handled using regularization techniques.
By lowering the magnitude of the variables, this strategy can be applied to keep all variables or features in the model. As a result, the model's scalability and accuracy are both maintained.
How is Deep learning and Machine learning different from one another?
Algorithms used in machine learning study data patterns and then use what they've learned to make decisions. In contrast, deep learning can learn by processing data independently and is very similar to the way the human brain identifies, evaluates, and makes decisions. The main difference is the manner in which data is shown to the system. Deep learning networks rely on multiple layers of artificial neural networks, but machine learning algorithms always need structured data.
What algorithm in machine learning is referred to as the "lazy learner" and why?
KNN is an algorithm for machine learning that is referred to as a lazy learner. Because K-NN dynamically calculates distance each time it wishes to classify rather than analyze the training dataset, it does not acquire any machine-learned values or variables from the training data.
List popular cross-validation techniques.
Cross-validation techniques in machine learning can be broadly divided into six categories. They are as follows:
- K fold
- Stratified k fold
- Leave one out
- Random search cv
- Grid search cv
What is Neural Network?
A compressed representation of the human brain is called a neural network. It has neurons that fire when similar inputs are present, much like the brain. The various neurons are linked together via connections, which facilitate the transfer of information from one neuron to another.