Excel For Data Science And Machine Learning

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

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

Requirements

Understanding of Basic Statistics

Beginner Excel Knowledge

Description

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 regressionMultiple Linear RegressionLogistic RegressionCluster AnalysisK-Means ClusteringDecision TreesYou will learn fundamental statistical and machine learning concepts, such as:Regression coefficientsVariabilityOLS assumptionsROC curveUnderfittingOverfittingDifference between classification and clusteringHow to choose the number of clustersHow to cluster categorical dataWhen to standardize dataPros and Cons of clusteringEntropy (Loss function)Information gainAs 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!!

Overview

Section 1: Introduction

Lecture 1 What Does the Course Cover?

Lecture 2 What Is Machine Learning?

Lecture 3 Types of Machine Learning

Section 2: Simple Linear Regression

Lecture 4 Linear Regression: Introduction

Lecture 5 Linear Regression

Lecture 6 Linear Regression Model (Graphical Representation)

Lecture 7 Formatting Excel Spreadsheets

Lecture 8 First Regression in Excel

Lecture 9 What Is OLS?

Lecture 10 Interpreting Regression Tables (Part 1)

Lecture 11 Decomposition of Variability

Lecture 12 Interpreting Regression Tables (Part 2)

Lecture 13 Interpreting Regression Tables (Part 3)

Lecture 14 Simple Linear Regression - Exercise

Section 3: Multiple Linear Regression

Lecture 15 Multiple Regression Analysis

Lecture 16 Multiple Linear Regression (Example)

Lecture 17 Mutiple Linear Regression (Results)

Lecture 18 OLS Assumptions

Lecture 19 OLS Assumptions: Linearity

Lecture 20 OLS Assumptions: No Endogeneity

Lecture 21 OLS Assumptions: Normality and Homoscedasticity

Lecture 22 OSL Assumptions: No Autocorrelation

Lecture 23 OLS Assumptions: No Multicollinearity

Lecture 24 Dummy Variables

Lecture 25 Dummy Variables - Exercise

Lecture 26 Making Predictions Using Linear Regression

Lecture 27 Making Predictions Using Linear Regression -Exercise

Section 4: Linear Regression Practical Example

Lecture 28 Practical Example (Part 1)

Lecture 29 Practical Example (Part 2)

Lecture 30 Practical Example (Part 3)

Lecture 31 A Note on Multicollinearity

Lecture 32 Feature Scaling

Lecture 33 Practical Example (Part 4)

Section 5: Logistic Regression

Lecture 34 Introduction to Logistic Regression

Lecture 35 From Linear to Logistic Regression

Lecture 36 Logistic vs. Logit Function

Lecture 37 Applying Logistic Regression in Excel

Lecture 38 Interpreting Regression Coefficients

Lecture 39 Logistic Regression with XReal

Lecture 40 Understanding the Logistic Regression Summary (part 1)

Lecture 41 Understanding the Logistic Regression Summary (Part 2)

Lecture 42 ROC Curve

Lecture 43 Binary Predictors for Logistic Regressions

Lecture 44 Underfitting and Overfitting

Lecture 45 Testing the Logistic Model

Section 6: Cluster Analysis

Lecture 46 Cluster Analysis (Definition)

Lecture 47 Cluster Analysis (Application)

Lecture 48 Clustering vs Classification

Lecture 49 Cluster Analysis (Math Prerequisites)

Section 7: K-means Clustering

Lecture 50 K-means Clustering

Lecture 51 K-means Clustering in Excel

Lecture 52 K-means Clustering with Xreal

Lecture 53 Choosing the Number of Clusters

Lecture 54 Clustering Categorical Data

Lecture 55 Standardization

Lecture 56 Clustering and Regression

Lecture 57 Clustering (Pros and Cons)

Lecture 58 Types of Clustering

Lecture 59 Market Segmentation (Part 1)

Lecture 60 Market Segmentation (Part 2)

Section 8: Decision Trees

Lecture 61 Decision Trees

Lecture 62 Entropy (Loss function)

Lecture 63 Information Gain

Lecture 64 Decision Trees in Excel (Part 1)

Lecture 65 Decision Trees in Excel (part 2)

Lecture 66 Decision trees (Prediction)

Section 9: Machine Learning in the Cloud

Lecture 67 Machine Learning in the Cloud

Lecture 68 Setting up Azure Machine Learning Studio (AMLS)

Lecture 69 First Experiment in AMLS (Part 1)

Lecture 70 First Experiment in AMLS (Part 2)

Lecture 71 Publishing a Web Service

Lecture 72 The Future of Machine Learning

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