Introduction to Data Science, Machine Learning & AI using Python
Friday, August 14, 2020 - 4:30PM EDT
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- Enter appropriate voucher #
Government Members: V-U22215
Industry Members: V-U22216
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If you want to become a Data Scientist, this is the course to begin with. 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 organization and lead to previously undiscovered insights from your data.
The course 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 visualize 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 of this course, you will be able to confidently apply many ML & AI techniques to both enhance your organization’s efficiencies and through predictive modelling, be prepared for future possibilities.
You Will Learn How To:
- 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, Analyze & Visualize 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 behaviors 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