Introduction to AI, Data Science & Machine Learning with Python

This course covers a wide range of topics, including data analysis, structured and unstructured data, natural language processing, sentiment analysis, data points, customer segments, logistic regression, random forests, nearest neighbors, business intelligence, target customers, data models, trading strategies, marketing campaigns, and programming language.

This course is designed for anyone who wants to gain foundational knowledge of data science, including both technical and non-technical beginners. It is particularly relevant for data scientists, analysts, and other professionals who work with data and want to improve their skills.

This course helps you gain business intelligence by teaching you how to uncover previously unseen business insights. You will analyze your organization, its people, and its environment as a network of inter-relationships and visualize these relationships to gain insights.

You will also learn how to build predictive models of revenue and other numeric variables using linear regression and how to use these models to inform business decisions.

Schedules are busy, but big data training online makes it easy to level up your career. If you need Big Data online training, we've got you covered. Our AnyWare course delivery option gives you the advantages of a live classroom right from the comfort of your computer screen–no matter where you are.

Yes. There are various opportunities to build a model and analyze issues throughout the training.

You just need an interest in gaining foundational knowledge of data science. This data scientist training course is designed for technical and non-technical beginners.

Marketing strategy is an important topic in this course as it teaches participants how to uncover new ways of segmenting customers, products, or services using clustering algorithms.

You will also learn how to build models of customer behaviors or business events from logged data using association rules and how to use this information to build a recommender for your customers that is unique to your product/service offering.

Natural language processing is used in this course to preprocess unstructured data such as web adverts, emails, and blog posts for AI/ML models. You will explore the most popular approaches to NLP, such as stemming and "stop" words, and learn how to prepare a term-document matrix (TDM) of unstructured documents for analysis.

This course covers a variety of data models, including linear regression, logistic regression, random forests, and nearest neighbors. You will learn how to express a business problem, such as customer revenue prediction, as a linear regression task and how to assess variables as potential predictors of the required target. You will also explore alternative approaches to classification, including neural networks and deep learning.

Sentiment analysis is used in this course to analyze customer feedback and opinions about products or services. You will learn how to perform sentiment analysis on customer reviews and use this information to inform marketing campaigns and trading strategies.

This course teaches participants how to work with both structured and unstructured data. You will learn how to use Python libraries to import, export, and work with all forms of data, from relational databases to Google Images. You will also explore clustering techniques on unstructured data such as tweets, emails, and documents.