The IBM Data Science Methodology is an organized framework for using data to address research and commercial issues. From comprehending the issue and gathering data to developing models, assessing outcomes, and implementing solutions, it leads data professionals through every stage of the data science lifecycle. To guarantee that data-driven insights [...]
This intermediate course teaches students how to utilize Python for machine learning and data science. You will learn how to analyze data, create predictive models, and utilize well-known Python libraries like Pandas, NumPy, Matplotlib, and Scikit-learn through practical exercises and real-world datasets. Regression, classification, model evaluation, overfitting, regularization, and other [...]
Andrew Ng and DeepLearning collaborated to produce the Machine Learning Specialization, one of the most well-liked beginner-friendly machine learning packages in the world. Stanford Online and AI. It explains how to create realistic AI applications and offers a hands-on introduction to machine learning using Python. Three courses encompassing supervised learning, [...]
Jeremy Howard and the team at fast.ai developed a free, interactive deep learning course called Practical Deep Learning for Coders. Using Python, PyTorch, and the fastai package, the course teaches students how to create, train, and implement cutting-edge deep learning models. It covers computer vision, natural language processing (NLP), tabular [...]
Using real-world datasets in Google BigQuery, the free, beginner-friendly Intro to SQL micro-course from Kaggle teaches the principles of SQL. It is a great place for aspiring data analysts, data scientists, and business intelligence experts to start since it allows students to practice writing queries to obtain, filter, sort, aggregate, [...]
An approachable course that uses scikit-learn and Python to teach the basic ideas of machine learning. You will get an understanding of underfitting and overfitting, investigate data, create your first predictive models, validate model performance, and apply Random Forests to enhance predictions. There are practical coding exercises in the course. [...]
An approachable course that uses practical tasks to illustrate the basic ideas of machine learning. Learn how machine learning models operate, create your first predictive models with scikit-learn and Python, assess model performance, and increase accuracy with methods like random forests and validation. Why is it well-liked?Because it emphasizes real [...]
The beginner-friendly, practical Data Visualization course on Kaggle teaches you how to use Python libraries like Seaborn and Matplotlib to generate powerful charts and visualizations. You’ll discover how to examine statistics, spot patterns and trends, compare categories, see how variables relate to one another, and produce visually appealing reports. To [...]
The Pandas Micro-course is a practical, beginner-friendly course that teaches you how to manipulate and analyze data using the Python Pandas package. You will learn how to create and work with DataFrames, clean and transform data, organize and sort data, handle missing values, and combine datasets for practical data analysis [...]
The Python Micro-course on Kaggle is a hands-on, beginner-friendly course that focuses on data science applications while teaching the principles of Python programming. Learners can work directly in their browser while gaining hands-on experience with Python syntax, functions, loops, lists, dictionaries, strings, and external libraries through interactive coding activities in [...]