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### Hands-Off Machine Learning with Google AutoML

Tabular data is omnipresent nowadays and can provide us with meaningful insights into both business and engineering problems. A common way of extracting these insights is by applying machine learning (ML) techniques to this data. The process of applying ML to a dataset consists of various steps, e.g., data preprocessing, feature engineering, and hyper-parameter optimisation, with each of these steps often being a time consuming trial and error process in and of themselves. Additionally, one needs to be an expert in the domain of ML in order to be efficient and effective at each of these steps. It can take quite some time for an organisation to either find these domain experts externally, or grow this expertise in-house.

### 10 Mathematics for Data Science Free Courses You Must Know in 2022

Knowledge of Mathematics is essential to understand the data science basics. So if you want to learn Mathematics for Data Science, this article is for you. In this article, you will find the 10 Best Mathematics for Data Science Free Courses. For these courses, You don't need to pay a single buck. Now, without any further ado, let's get started- This is a completely FREE course for beginners and covers data visualization, probability, and many elementary statistics concepts like regression, hypothesis testing, and more.

### Statistics And Probability Using Excel - Statistics A To Z

You've found the right Statistics and Probability with Excel course! This course will teach you the skill to apply statistics and data analysis tools to various business applications. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this course on Probability and Statistics in Excel. If you are a business manager, or business analyst or an executive, or a student who wants to learn Probability and Statistics concepts and apply these techniques to real-world problems of the business function, this course will give you a solid base for Probability and Statistics by teaching you the most important concepts of Probability and Statistics and how to implement them in MS Excel.

### How statistics can aid in fight against misinformation

An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.

### How statistics can aid in the fight against misinformation: Machine learning model detects misinformation, is inexpensive and is transparent

With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans. "We would like to know what a machine is thinking when it makes decisions, and how and why it agrees with the humans that trained it," Boukouvalas said. "We don't want to block someone's social media account because the model makes a biased decision."

### Blog: Different Roles In Data Science

The field of data science is continuously growing, subsequently, there are a ton of career roles available for one to choose from within the data science domain. This blog lists down some of the most emerging career options in data science one can opt for. Data science is a field that requires subject matter expertise (e.g., biology if you plan to do bioinformatics), programming skills, and training in mathematics and statistics. Data science as a service allows companies to get business insights leveraging advanced analytics technologies, including deep learning, without investing in in-house data science competencies. Data scientists help a company process a huge pool of information from a variety of sources.

### Best Data Science Blogs To Follow

If you are a data Scientist or learning Data science then there is nothing better than following a blog that provides the latest information. Here is a list of blogs you must follow to know more about data science, machine learnings, and AI. Simply Statistics is run by Jeff Leek, Roger Peng, and Rafa Irizarry. Simple Statistics also offers data science specialization courses. Flowing Data offers tutorials and resources for effective data visualization.

### The Complete Machine Learning 2021 : 10 Real World Projects

Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease. All the important libraries you would need to work on Machine learning lifecycle. Full-fledged course on Statistics so that you don't have to take another course for statistics, we cover it all. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go. Total 10 beginner to Advance level projects so that you can test your skills.