Mastering Statistics For Machine Learning: Beginner'S Guide

Unlock the Power of Data: Learn Essential Statistical Concepts, Techniques for Successful Machine Learning Applications

What you'll learn

Grasp key statistical concepts: Understand central tendency, dispersion, and probability basics essential for data analysis

Apply statistical techniques: Use statistics to analyze and interpret data through frequency distributions, histograms, and more

Master probability distributions: Learn to apply uniform, binomial, normal, and other distributions in problem-solving

Integrate stats with ML: Combine statistical methods with machine learning models for effective data-driven decision-making

Requirements

Basic Math Knowledge: Familiarity with basic arithmetic, algebra, and a general understanding of mathematical concepts.

Interest in Data Analysis: A keen interest in data analysis and machine learning will help learners engage with the course content

No Prior Experience Required: This course is designed for beginners, so no prior experience in statistics or machine learning is necessary

Access to a Computer: A computer with internet access for viewing course materials and using statistical software or tools like MS Excel or Python

Description

Imagine you're standing at the crossroads of data and discovery, ready to unlock the hidden patterns that shape the world around us. You’ve always known that the answers lie within the numbers, but now, you’re on the brink of something greater—a journey that will transform how you understand data and empower you to make decisions with precision and confidence.Welcome to "Mastering Statistics for Machine Learning: A Beginner's Guide," where you are the hero embarking on a quest to conquer the world of data science. With every lesson, you’ll wield the tools of statistics like a seasoned explorer, charting unknown territories in datasets, uncovering trends, and making predictions that once seemed out of reach.This course is your map and compass, guiding you through the fundamental concepts of statistics, from understanding central tendencies and measures of dispersion to mastering probability distributions and their critical role in machine learning. You’ll solve real-world problems, analyze data with newfound clarity, and, by the end, stand ready to integrate these powerful techniques into your own machine learning models.No prior experience? No problem. This journey is designed for beginners, ensuring that you start with a solid foundation and build your expertise step by step. All you need is a curiosity to explore and a desire to unlock the secrets within the data.Are you ready to become the data hero you were always meant to be? Your adventure in mastering statistics starts here.

Overview

Section 1: Introduction to the Course

Lecture 1 INTRODUCTION

Lecture 2 Topics to be covered in this Course

Section 2: Introduction to SESSION 1

Lecture 3 Introduction to SESSION 1

Lecture 4 What is Statistics?

Lecture 5 Population and Sample

Lecture 6 Data Collection in Statistics

Lecture 7 Frequency Distribution in Statistics

Section 3: MEAN in Statistics

Lecture 8 Measures of Central Tendency

Lecture 9 Measures of Central Tendency in MS Excel

Lecture 10 Solving a Question (MEAN) PART 1

Lecture 11 Solving a Question (MEAN) PART 2

Section 4: MEDIAN in Statistics

Lecture 12 Measures of Central Tendency (MEDIAN)

Lecture 13 Explaining MEDIAN with example

Section 5: MODE in Statistics

Lecture 14 Measures of Central Tendency (MODE)

Lecture 15 Modality in Statistics

Lecture 16 Doubts about MODE

Lecture 17 Histogram- Mode

Lecture 18 Doubts about the Histogram

Lecture 19 Riddle- Guess!!

Section 6: Measures of Dispersion in Statistics

Lecture 20 Measures of Dispersion

Lecture 21 Measures of Dispersion- Range

Lecture 22 Measures of Dispersion- Quartile Deviation

Lecture 23 Boxplot or Box Whiskers Plot and Outliners

Section 7: Standard Deviation in Statistics

Lecture 24 Problem of Standard Deviation

Lecture 25 Standard Deviation and Variance

Section 8: Covariance and Correlation

Lecture 26 Covariance in Statistics

Lecture 27 Correlation and Example PART 1

Lecture 28 Correlation and Example PART 2

Lecture 29 Skewness in Statistics

Section 9: Summary of SESSION 1

Lecture 30 Activities and Homework

Lecture 31 Queries by the Students

Lecture 32 Last Riddle- Guess!!

Section 10: INTRIDUCTION TO SESSION 2

Lecture 33 Summary of SESSION 1

Lecture 34 INTRODUCTION

Lecture 35 Introduction to Probability Basics PART 1

Lecture 36 Introduction to Probability Basics PART 2

Section 11: Probability in Statistics

Lecture 37 A Random Experiment

Lecture 38 Sample Space in Probability

Lecture 39 Event in Probability PART 1

Lecture 40 Event in Probability PART 2

Lecture 41 Trial in Probability

Lecture 42 Riddle- Guess!!

Section 12: Probability in Statistics

Lecture 43 Activity- Let's Solve

Lecture 44 Probability Possibility

Lecture 45 Let's Solve- Activities

Section 13: Conditional Probability in Statistics

Lecture 46 Conditional Probability

Lecture 47 Example 1 and Formulas

Lecture 48 Example 2 and Formulas

Lecture 49 Riddle- Guess!!!

Section 14: Random Variable in Probability

Lecture 50 Random Variable

Lecture 51 Example 1 of Random Variable PART 1 (Explanation)

Lecture 52 Example 1 of Random Variable PART 2 (Solving)

Lecture 53 Example 2 of Random Variable

Lecture 54 Homework for Practice

Lecture 55 Doubts in Example 2

Lecture 56 Last Riddle of SESSION 2

Section 15: INTRODUCTION TO SESSION 3

Lecture 57 Introduction

Lecture 58 Topics we will cover in SESSION 3

Lecture 59 Riddle- Guesss!!!

Section 16: UNIFORM DISTRIBUTION in Probability Distribution

Lecture 60 Uniform Distribution

Lecture 61 Types of Uniform Distribution

Lecture 62 Formula for Uniform Distribution and How to Apply?

Lecture 63 Let's Solve- Uniform Distribution PART 1

Lecture 64 Let's Solve- Uniform Distribution PART 2

Section 17: BINOMIAL DISTRIBUTION in Probability Distribution

Lecture 65 Binomial Distribution

Lecture 66 Formula for Binomial Distribution and How to apply it?

Lecture 67 Let's Solve 1- Binomial Distribution

Lecture 68 Let's Solve 2- Binomial Distribution

Section 18: NORMAL DISTRIBUTION in Probability Distribution

Lecture 69 Normal Distribution and it's Formula

Lecture 70 Let's Solve- Normal Distribution

Lecture 71 Normal Distribution- Importance

Lecture 72 Q/A with the Students PART 1

Lecture 73 Q/A with the Students PART 2

Lecture 74 Doubts about Normal Distribution

Section 19: POISSON DISTRIBUTION in Probability Distribution

Lecture 75 Poisson distribution and it's Formula

Lecture 76 Let's Solve- Poisson Distribution

Lecture 77 Q/A with Poisson Distribution

Section 20: EXPONENTIAL DISTRIBUTION in Probability Distribution

Lecture 78 Exponential Distribution and it's Formula

Lecture 79 Let's Solve- Exponential Distribution and it's doubts

Lecture 80 SUMMARY of this Session and some doubts

Lecture 81 Riddle- Guesss!!

Beginners in Data Science: Individuals new to data science and machine learning who want to build a strong foundation in statistics,Aspiring Machine Learning Engineers: Those looking to enhance their understanding of statistical methods crucial for machine learning applications,Data Analysts and Enthusiasts: Professionals and enthusiasts seeking to deepen their knowledge of data analysis through practical statistical techniques,Students and Academics: Learners from academic backgrounds who wish to complement their studies with practical, hands-on experience in statistics and its applications in machine learning