Excel for Data Science and Machine Learning

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz 
Language: English | Size: 1.44 GB | Duration: 3h 14m 

Perform Machine Learning and Advanced Statistical Analysis On Your Own - Even If You Don't Code! 100% in Excel

What you'll learn
How to Perform Machine Learning Techniques on Your Own - No Coding Skills Required
How to Use Excel for Advanced Statistical Analysis
Linear regression
Multiple Linear Regression
Logistic Regression
Cluster Analysis
K-Means Clustering
Decision Trees
Fundamental Statistical Concepts

Description
Why learn machine learning and data science in Excel?

Do data scientists and data analysts need 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 direct, visual insight that allows even a beginner to understand the most common machine learning methods, and it is naturally suited to data preparation.

Moreover, 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 who is able to code in Python, this course will teach you how to perform machine learning and advanced statistical analysis on your own.

You will be able to understand the intuition behind ML algorithms without having to code at all. Excel is the perfect environment to grasp the logic of different machine learning techniques in an easy-to-understand way.

So, if you don't know how to code, but you want to learn data science, statistical analysis, and machine learning, and you aspire to become a data analyst or data scientist, this would be a great place to start.

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, this course aims to teach you the foundations of machine learning and advanced statistical analysis. We will do that in a software that is easy to understand to ensure you have acquired the theoretical knowledge before leveraging 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

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