In Part 1 we'll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We'll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin and box plots, scatter plots, and correlation: Variable types, empty values, range and count calculations, left/right censoring, etc. Histograms, frequency tables, mean, median, mode, variance, skewness, etc. Throughout the course, we'll introduce real-world scenarios to solidify key concepts and simulate actual data science and business intelligence cases. You'll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and more. In Part 2 we'll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.

"Turn Ideas into Python Programs with ChatGPT" is a unique online course that teaches you how to use ChatGPT, a powerful AI language model, to generate Python code without any prior experience in programming. This course is perfect for individuals who want to learn how to create Python automations and apps but have little to no coding experience. Instead of learning to code in Python, you'll learn how to write good ChatGPT queries that generate Python code. You'll then run this code in your computer and get the output which could be some generated files, web app, desktop GUI, a data analysis graph, etc. The course is divided into five sections, each of which focuses on a different aspect of using ChatGPT to build Python automations and apps.

This course will help anyone, at any level, to build a machine learning model and create a docker container in Python that can be deployed anywhere. Even if you are a complete beginner, you will have success. But if you have already built machine learning models countless times, you can still learn from this course, because your speed will increase if you want to create a baseline model very quickly. This course helps you implement machine learning prototyping as quickly as possible. Learn how to track your machine learning model building experiments.

Learn how to build Data Science Projects with Python.

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Python is a popular programming language that is widely used in the field of data science and data analytics. It is known for its simplicity, readability, and flexibility, making it a great choice for beginners and experienced programmers alike. One of the main reasons why Python is so popular for statistical analysis is because of the many libraries and modules available for this purpose. Some of the most commonly used libraries for statistical analysis in Python include NumPy, Pandas, and SciPy. NumPy is a library for working with large, multi-dimensional arrays and matrices of numerical data.

This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data. Statistics and mathematics are fundamental to the field of data science and data analytics. A strong foundation in these subjects is essential for understanding and working with data. Statistics is the science of collecting, analyzing, and interpreting data. It involves using statistical methods and techniques to understand patterns and trends in data, and to make predictions and decisions based on that data. Some key areas of statistics that are important for data science and data analytics include descriptive statistics, probability theory, hypothesis testing, and regression analysis.

In this course you are invited to learn all the fundamental skills required in any kind of activity related to the Natural Language Processing and you will learn them from a theoretical and practical point of view, in fact you will seat together with me coding and implementing any topic step-by-step, instruction after instruction. Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics that focuses on the interaction between computers and human (natural) languages. The goal of NLP is to enable computers to understand, interpret, and generate human language in order to communicate with humans in a more natural and intuitive way. Overall, a strong foundation in NLP requires an understanding of language structure, language processing, machine learning, and knowledge representation, as well as the ability to apply these concepts to solve real-world problems.

One of the most exciting aspects of AI art is its ability to generate completely novel works of art that are unlike anything that has been created before. This can be done through the use of generative models, which are trained on large datasets of existing artworks and then use that knowledge to create new, original pieces. In a course on "Create and Sell AI Art," students would likely learn how to use AI tools and techniques to create original artworks, as well as how to market and sell these artworks to a wider audience. This could include topics such as using machine learning algorithms to generate art, designing and implementing AI art projects, and leveraging social media and other online platforms to promote and sell AI art. In addition to technical skills, a course on "Create and Sell AI Art" may also cover topics related to the business of art, such as pricing and pricing strategies, negotiating with buyers, and developing a personal brand as an AI artist.

ChatGPT by OpenAI has quickly taken the world by storm. ChatGPT can write poetry, short stories, haikus, character biographies, computer code, and much, much more. In this course, we get you started with ChatGPT, getting you signed up for your own free account. From there, we randomly select topics to learn more about ChatGPT. We use ChatGPT to see how deep and wide this machine language tools learning base actually is.