Mastering Statistics for Machine Learning- Beginner Guide

Mastering Statistics for Machine Learning: Beginners Guide, available at $54.99, has an average rating of 5, with 131 lectures, based on 1 reviews, and has 3 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners in Data Science: Individuals new to data science and machine learning who want to build a strong foundation in statistics or Aspiring Machine Learning Engineers: Those looking to enhance their understanding of statistical methods crucial for machine learning applications or Data Analysts and Enthusiasts: Professionals and enthusiasts seeking to deepen their knowledge of data analysis through practical statistical techniques or 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 It is particularly useful for Beginners in Data Science: Individuals new to data science and machine learning who want to build a strong foundation in statistics or Aspiring Machine Learning Engineers: Those looking to enhance their understanding of statistical methods crucial for machine learning applications or Data Analysts and Enthusiasts: Professionals and enthusiasts seeking to deepen their knowledge of data analysis through practical statistical techniques or 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.

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Summary

Title: Mastering Statistics for Machine Learning: Beginners Guide

Price: $54.99

Average Rating: 5

Number of Lectures: 131

Number of Published Lectures: 81

Number of Curriculum Items: 131

Number of Published Curriculum Objects: 81

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: INTRODUCTION

Lecture 2: Topics to be covered in this Course

Chapter 2: Introduction to SESSION 1

Lecture 1: Introduction to SESSION 1

Lecture 2: What is Statistics?

Lecture 3: Population and Sample

Lecture 4: Data Collection in Statistics

Lecture 5: Frequency Distribution in Statistics

Chapter 3: MEAN in Statistics

Lecture 1: Measures of Central Tendency

Lecture 2: Measures of Central Tendency in MS Excel

Lecture 3: Solving a Question (MEAN) PART 1

Lecture 4: Solving a Question (MEAN) PART 2

Chapter 4: MEDIAN in Statistics

Lecture 1: Measures of Central Tendency (MEDIAN)

Lecture 2: Explaining MEDIAN with example

Chapter 5: MODE in Statistics

Lecture 1: Measures of Central Tendency (MODE)

Lecture 2: Modality in Statistics

Lecture 3: Doubts about MODE

Lecture 4: Histogram- Mode

Lecture 5: Doubts about the Histogram

Lecture 6: Riddle- Guess!!

Chapter 6: Measures of Dispersion in Statistics

Lecture 1: Measures of Dispersion

Lecture 2: Measures of Dispersion- Range

Lecture 3: Measures of Dispersion- Quartile Deviation

Lecture 4: Boxplot or Box Whiskers Plot and Outliners

Chapter 7: Standard Deviation in Statistics

Lecture 1: Problem of Standard Deviation

Lecture 2: Standard Deviation and Variance

Chapter 8: Covariance and Correlation

Lecture 1: Covariance in Statistics

Lecture 2: Correlation and Example PART 1

Lecture 3: Correlation and Example PART 2

Lecture 4: Skewness in Statistics

Chapter 9: Summary of SESSION 1

Lecture 1: Activities and Homework

Lecture 2: Queries by the Students

Lecture 3: Last Riddle- Guess!!

Chapter 10: INTRODUCTION TO SESSION 2

Lecture 1: Summary of SESSION 1

Lecture 2: INTRODUCTION

Lecture 3: Introduction to Probability Basics PART 1

Lecture 4: Introduction to Probability Basics PART 2

Chapter 11: Probability in Statistics

Lecture 1: A Random Experiment

Lecture 2: Sample Space in Probability

Lecture 3: Event in Probability PART 1

Lecture 4: Event in Probability PART 2

Lecture 5: Trial in Probability

Lecture 6: Riddle- Guess!!

Chapter 12: Probability in Statistics

Lecture 1: Activity- Lets Solve

Lecture 2: Probability Possibility

Lecture 3: Lets Solve- Activities

Chapter 13: Conditional Probability in Statistics

Lecture 1: Conditional Probability

Lecture 2: Example 1 and Formulas

Lecture 3: Example 2 and Formulas

Lecture 4: Riddle- Guess!!!

Chapter 14: Random Variable in Probability

Lecture 1: Random Variable

Lecture 2: Example 1 of Random Variable PART 1 (Explanation)

Lecture 3: Example 1 of Random Variable PART 2 (Solving)

Lecture 4: Example 2 of Random Variable

Lecture 5: Homework for Practice

Lecture 6: Doubts in Example 2

Lecture 7: Last Riddle of SESSION 2

Chapter 15: INTRODUCTION TO SESSION 3

Lecture 1: Introduction

Lecture 2: Topics we will cover in SESSION 3

Lecture 3: Riddle- Guesss!!!

Chapter 16: UNIFORM DISTRIBUTION in Probability Distribution

Lecture 1: Uniform Distribution

Lecture 2: Types of Uniform Distribution

Lecture 3: Formula for Uniform Distribution and How to Apply?

Lecture 4: Lets Solve- Uniform Distribution PART 1

Lecture 5: Lets Solve- Uniform Distribution PART 2

Chapter 17: BINOMIAL DISTRIBUTION in Probability Distribution

Lecture 1: Binomial Distribution

Lecture 2: Formula for Binomial Distribution and How to apply it?

Lecture 3: Lets Solve 1- Binomial Distribution

Lecture 4: Lets Solve 2- Binomial Distribution

Chapter 18: NORMAL DISTRIBUTION in Probability Distribution

Lecture 1: Normal Distribution and its Formula

Lecture 2: Lets Solve- Normal Distribution

Lecture 3: Normal Distribution- Importance

Lecture 4: Q/A with the Students PART 1

Lecture 5: Q/A with the Students PART 2

Lecture 6: Doubts about Normal Distribution

Chapter 19: POISSON DISTRIBUTION in Probability Distribution

Lecture 1: Poisson distribution and its Formula

Lecture 2: Lets Solve- Poisson Distribution

Lecture 3: Q/A with Poisson Distribution

Chapter 20: EXPONENTIAL DISTRIBUTION in Probability Distribution

Lecture 1: Exponential Distribution and its Formula

Lecture 2: Lets Solve- Exponential Distribution and its doubts

Lecture 3: SUMMARY of this Session and some doubts

Instructors

Peter Alkema
Business | Technology | Self Development

Regenesys Business School
Regenesys Business School

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