This path is for anyone looking to develop a strong, foundational understanding of popular machine learning tools and techniques.
Unlike most data science or ML courses, this is NOT about learning how to code with Python or R. Instead, we'll use familiar, intuitive tools like Microsoft Excel to break down complex models and visualize exactly how they work.
We'll start by introducing the machine learning landscape and workflow, and exploring common univariate & multivariate data profiling techniques like frequency tables, histograms, heat maps, scatter plots and more.
Next we'll dive into the world of supervised learning, and review key concepts like dependent vs. independent variables, feature engineering, splitting and overfitting. In course #2, we'll introduce powerful classification models, including decision trees, logistic regression, and K-nearest neighbors.
From there we'll cover the building blocks of regression modeling and time-series forecasting, and explore powerful techniques for identifying seasonality, predicting nonlinear trends, and measuring the impact of key business decisions using intervention analysis.
Last but not least we'll introduce the world of unsupervised learning, and break down powerful unsupervised techniques including cluster analysis, association mining, outlier detection, and dimensionality reduction.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
WHO SHOULD TAKE THIS PATH?
- Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanations
- Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning
- R or Python users seeking a deeper understanding of the models and algorithms behind their code
WHAT ARE THE PATH REQUIREMENTS?
- This is a beginner-friendly course (no prior knowledge or math/stats background required)
- We'll use Microsoft Excel (Office 365) for course demos, but participation is optional
This path is for anyone looking to develop a strong, foundational understanding of popular machine learning tools and techniques.
Unlike most data science or ML courses, this is NOT about learning how to code with Python or R. Instead, we'll use familiar, intuitive tools like Microsoft Excel to break down complex models and visualize exactly how they work.
We'll start by introducing the machine learning landscape and workflow, and exploring common univariate & multivariate data profiling techniques like frequency tables, histograms, heat maps, scatter plots and more.
Next we'll dive into the world of supervised learning, and review key concepts like dependent vs. independent variables, feature engineering, splitting and overfitting. In course #2, we'll introduce powerful classification models, including decision trees, logistic regression, and K-nearest neighbors.
From there we'll cover the building blocks of regression modeling and time-series forecasting, and explore powerful techniques for identifying seasonality, predicting nonlinear trends, and measuring the impact of key business decisions using intervention analysis.
Last but not least we'll introduce the world of unsupervised learning, and break down powerful unsupervised techniques including cluster analysis, association mining, outlier detection, and dimensionality reduction.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
WHO SHOULD TAKE THIS PATH?
- Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanations
- Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning
- R or Python users seeking a deeper understanding of the models and algorithms behind their code
WHAT ARE THE PATH REQUIREMENTS?
- This is a beginner-friendly course (no prior knowledge or math/stats background required)
- We'll use Microsoft Excel (Office 365) for course demos, but participation is optional
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