Excel for Data Science and Machine Learning

Excel for Data Science and Machine Learning | Udemy
English | Size: 1.45 GB
Genre: eLearning

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!!

Who this course is for:
You Should Take This Course If You Want to Understand Machine Learning Fundamentals
Don’t Know How to Code but You Want to Perform Machine Learning On Your Own? This Is the Perfect Course for You
This Course Is Great If You Aspire to Become a Data Analyst or a Data Scientist