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What You Will Learn
- Define common ML terms
- Describe examples of products that use ML and general methods of ML problem-solving used in each
- Identify whether to solve a problem with ML
- Compare and contrast ML to other programming methods
- Apply hypothesis testing and the scientific method to ML problems
- Have conversations about ML problem-solving methods
ln basic terms, ML is the process of training a piece of software, called a model, to make useful predictions using a data set. This predictive model can then serve up predictions about previously unseen data. We use these predictions to take action in a product; for example, the system predicts that a user will like a certain video, so the system recommends that video to the user.
Often, people talk about ML as having two paradigms, supervised and unsupervised learning. However, it is more accurate to describe ML problems as falling along a spectrum of supervision between supervised and unsupervised learning. For the sake of simplicity, this course will focus on the two extremes of this spectrum.
This course is an introduction to computer science and programming in Python. Upon successful completion of this course, you will be able to:
1. Take a new computational problem and develop a plan to solve it through problem understanding and decomposition.
2. Follow a design creation process that includes specifications, algorithms, and testing.
3. Code, test, and debug a program in Python, based on your design.
The problem-solving online course comprises the content related to computer science and programing language. It includes the training process of ML, which is a software known as a model. It is used to predict the data set which is unseen and these predictions utilize in certain actions. It helps to predict the user’s behaviour and interest in a certain thing and respond according to the predictions. The course purpose is to discussed and prescribed the ML problems and their solutions. The course main focus is on the broader field of problem-solving. With these candidates get the chance to learn problem-solving online and enhance the capabilities to understand and solve effectively.
The problem-solving tutorials are readily available for help and people usually learn from them. But with the course, a person gets a chance to learn the professional skills that play an important role in the professional field. A person can get a chance to:
· Understand the problem and easily be able to do its decomposition
· Take up to a problem conceptualization and develop an effective plan to overcome it.
· A person will be able to design the specifications and algorithm, as well as be able to run the testing.
· Code, testing, debugging and all other programs issues are based on the design, and with the problem-solving course, a person will be able to learn and find out well.
The problem-solving online course is designed to provide detailed knowledge about the ML problem-solving methods and model. It covers all the aspects related to building design, diagnoses the possible issues with the design and how to figure out the issue and debugging methods to successfully run the program. For the candidates who are interested to learn advanced methods and raise the programing knowledge and practical implication, this problem-solving online course is the best opportunity. Candidate not only enrols but also learn problem-solving online with the professional and real-time industrial expert. In the course outline methods, solutions, programming information and problem-solving tutorials are also incorporated that all are provided to candidates with the online sessions. Now the learning is smart and easy with online problem-solving tutorials.
A candidate who is interested to learn problem-solving online course will have to register with this program and get the benefits from the content. The course content is highly comprehensive and covers almost every aspect includes:
· Common ML terms and their definition
· Products examples and their description that use ML and general methods of ML problem solving
· Identification of the problem and its solution with ML
· Comparison and contrast of ML with other programming methods
· Application of hypothesis testing method and other scientific methods for ML problems
· Conversions about the ML problem-solving methods
Get yourself register!
The problem-solving online course will not just an opportunity to learn about ML programming but also helps to find out the problems in methods. As well as provide all possible solutions to find out ML problems and its solution with the application of the scientific method. So, rush and get yourself registered with the course and learn problem-solving online.
What You Will Learn
Data Science is a dynamic and fast growing field at the interface of Statistics and Computer Science. The emergence of massive datasets containing millions or even billions of observations provides the primary impetus for the field. Such datasets arise, for instance, in large-scale retailing, telecommunications, astronomy, and internet social media. This course will emphasize practical techniques for working with large-scale data. Specific topics covered will include statistical modeling and machine learning, data pipelines, programming languages, "big data" tools, and real world topics and case studies. The use of statistical and data manipulation software will be required. Course intended for non-quantitative graduate-level disciplines. This course will not count towards degree requirements for graduate programs such as Statistics, Computer Science, or Data Science. Students should inquire with their respective programs to determine eligibility of course to count towards minimum degree requirements. This course does not fulfill any major requirements for undergraduate degree programs offered by Computer Science.