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Introduction to Data Science, Machine Learning & AI using Python Training

Course Overview

If you want to become a Data Scientist, this is the place to begin! Introduction to Data Science, Machine Learning & AI (Python version) covers every stage of the Data Science Lifecycle, from working with raw datasets to building, evaluating and deploying Machine Learning (ML) and Artificial Intelligence (AI) models that create efficiencies for the organisation and lead to previously undiscovered insights from your data.

It begins by teaching you how to use Python libraries, such as Pandas, Numpy and SciPy, to work with all types of data in Python, including everything from data in a Relational Database to Google Images. You’ll learn how to manage, transform and visualise data in every conceivable way, in order to unearth the real value in your current and historic data. You’ll then use Python libraries such as Scikit- Learn to understand how to build, evaluate and deploy many Machine Learning (ML) and Artificial Intelligence (AI) models that not only predict into the future but constantly learn from data as new events unfold.

By the end, you will be able to confidently apply many ML & AI techniques to both enhance your organisation’s efficiencies and, through predictive modelling, be prepared for future possibilities.

NEW! DATA SCIENCE BUNDLES NOW AVAILABLE

This course is now available as part of a multi-course, blended learning premium training bundle for a limited time! Take your Data Science skills and career to the next level with multi-modal learning path bundles that lead to certification.

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This course is delivered over 5 full days.

Key Information

Course Length
40 hours

Learning Method(s)
Online materials
Online assessment

For Individuals

Full Cost Price
£3,114.00

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  • Translate everyday business questions as well as more complex problems into Machine Learning tasks in order to make truly data-driven decisions
  • Use Python Pandas, Matplotlib & Seaborn libraries to Explore, Analyse & Visualise data from varied sources (the Web, Word documents, Email, Twitter, NoSQL stores, Databases, Data Warehouses & more) for patterns and trends relevant to your business
  • Train a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library (eg. Decision Trees, Logistic Regression, Neural Networks)
  • Re-segment your customer market using K-Means & Hierarchical algorithms for better alignment of products & services to customer needs
  • Discover hidden customer behaviours from Association Rules and build a Recommendation Engine based on behavioral 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