SkillSpeed a Training Organization by Sanjay Verma
Machine Learning
Skillspeed offers Abstract Machine learning is a vast area of research that is primarily concerned with finding patterns in empirical data. We restrict our attention to a limited number of core concepts that are most relevant for quantum learning algorithms. We discuss the importance of the data-driven approach, compared with the formal modeling of traditional artificial intelligence. We outline how to build high-dimensional feature spaces that will correspond to quantum states in quantum computing. An algorithm’s ability to generalize beyond training data is intrinsically linked with its complexity — this is the foundation of the Vapnik-Chervonenkis theory, which we briefly explain. There is no free lunch, however: a single algorithm will not work in every learning scenario, which leads to combinations of learners and ensembles. Dependence on the dimensions and volume of the data defines computational complexity for learning methods, which often do not scale well.
SkillSpeed offers Courses on a variety of topics
The tasks are different, but the approach is the same:
Step 1: Data collection. The more, the better. The data must contain the outcome you want to predict and additional information from which to make the prediction. For a street sign detector (“Is there a street sign in the image?”), you would collect street images and label whether a street sign is visible or not. For a credit default predictor, you need past data on actual loans, information on whether the customers were in default with their loans, and data that will help you make predictions, such as income, past credit defaults, and so on. For an automatic house value estimator program, you could collect data from past house sales and information about the real estate such as size, location, and so on.
Step 2: Enter this information into a machine learning algorithm that generates a sign detector model, a credit rating model or a house value estimator.
Step 3: Use model with new data. Integrate the model into a product or process, such as a self-driving car, a credit application process or a real estate marketplace website.
Machines surpass humans in many tasks, such as playing chess (or more recently Go) or predicting the weather. Even if the machine is as good as a human or a bit worse at a task, there remain great advantages in terms of speed, reproducibility and scaling. A once implemented machine learning model can complete a task much faster than humans, reliably delivers consistent results and can be copied infinitely. Replicating a machine learning model on another machine is fast and cheap. The training of a human for a task can take decades (especially when they are young) and is very costly. A major disadvantage of using machine learning is that insights about the data and the task the machine solves is hidden in increasingly complex models. You need millions of numbers to describe a deep neural network, and there is no way to understand the model in its entirety. Other models, such as the random forest, consist of hundreds of decision trees that “vote” for predictions. To understand how the decision was made, you would have to look into the votes and structures of each of the hundreds of trees. That just does not work no matter how clever you are or how good your working memory is. The best performing models are often blends of several models (also called ensembles) that cannot be interpreted, even if each single model could be interpreted. If you focus only on performance, you will automatically get more and more opaque models.
