What you'll learn:
- Use This Course to Improve Your Excel Skills
- Learn How to Perform Machine Learning Techniques on Your Own - No Coding Skills Required
- Fundamental Statistical Concepts
- Grasp the Intuition Behind Advanced Statistics
- How to Use Excel for Advanced Statistical Analysis
- Improve Your Analytical Thinking
- Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Cluster Analysis
- K-Means Clustering
- Decision Trees
Why machine learning and data science in Excel?
Do data scientists and data analysts use Excel at all?
The answer is a resounding “Yes, they do!”
Few people in an organization can read a Jupyter Notebook, but literally everyone is familiar with Excel. It provides the direct, visual insight that both experts and beginners need to apply the most common machine learning methods. Plus, it is naturally suited to data preparation.
In fact, the simplicity of Excel lowers barriers to entry and allows you to undertake your own data analysis right away. Even if you are not a computer science graduate with Python coding skills, this course will teach you how to perform machine learning and advanced statistical analysis on your own.
Excel is the perfect environment to grasp the logic of different machine learning techniques in an easy-to-understand way. All you need to do is get started, and in no time, you will be able to fully understand the intuition behind ML algorithms without having to code at all.
So, if you are not into programming but you want to break into data science, statistical analysis, and machine learning, and you aspire to become a data analyst or data scientist, you’ve come to the right place.
Machine learning methods we will cover in the course:
Linear regression
Multiple Linear Regression
Logistic Regression
Cluster Analysis
K-Means Clustering
Decision Trees
You will learn fundamental statistical and machine learning concepts, such as:
Regression coefficients
Variability
OLS assumptions
ROC curve
Underfitting
Overfitting
Difference between classification and clustering
How to choose the number of clusters
How to cluster categorical data
When to standardize data
Pros and Cons of clustering
Entropy (Loss function)
Information gain
As you can see, we aim to teach you the foundations of machine learning and advanced statistical analysis in a software that is truly easy to understand. And the best part is, once you finish this course, you will have the transferable theoretical knowledge you’ll need if you decide to dive into the advanced frameworks available in Python.
So, if you are passionate about machine learning but you don’t know how to code, then this course is the perfect opportunity for you. Click ‘Buy Now’, get excited, and begin your ML journey today!!
