Introduction To AI, Data Science & Machine Learning with Python

Data science is a field that has exploded in popularity in recent years, and for good reason. Companies across industries are increasingly relying on data to inform their decision-making, and skilled data scientists are in high demand.

In this comprehensive course, you'll learn the foundational skills and techniques you need to succeed in this exciting field. You'll start by exploring the role of a data scientist and the lifecycle of data science efforts within an organisation.

Then, you'll dive into the technical skills you need, such as using Python and its relevant libraries for data analysis and visualisation, preprocessing unstructured data, and building AI/ML models.

You'll also explore key machine learning algorithms, including linear regression, decision tree classifiers, and clustering algorithms. And, you'll learn how to apply these techniques to real-world problems, such as predicting customer churn and building recommendation engines.

Throughout the data science training, you'll have the opportunity to work on hands-on exercises and projects, allowing you to practice your skills and build your portfolio.

By the end of the course, you'll have a deep understanding of the data science process, the tools and techniques used by data scientists, and the ability to apply these skills to real-world problems.

Data Science Training In Python Course Information

In this course, you will:

• Differentiate between Predictive AI and Generative AI.

• Translate everyday business questions and problems into Machine Learning tasks to make data-driven decisions.

• Use Python Pandas, Matplotlib & Seaborn libraries to explore, analyse, and visualise data from various sources, including the web, word documents, email, NoSQL stores, databases, and data warehouses.

• Train a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library, such as Decision Trees, Logistic Regression, and Neural Networks.

• Re-segment your customer market using K-Means and Hierarchical algorithms to better align products and services to customer needs.

• Discover hidden customer behaviours from Association Rules and build a Recommendation Engine based on behavioural patterns.

• Investigate relationships & flows between people and business-relevant entities using Social Network Analysis.

• Build predictive models of revenue and other numeric variables using Linear Regression.

• Test your knowledge with the included end-of-course exam.

• Leverage continued support with after-course one-on-one instructor coaching and computing sandbox.

Training Prerequisites

None.