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What you'll learn?
- Learn the Main Concepts of Inferential Statistics to use in Machine Learning
Course Overview
In this course, you will take the first step in your Data Science journey by learning Inferential Statistics.
Data Science Professionals in Machine Learning, Artificial Intelligence, and all professionals in several fields like Finance, Psychology, and the Medical Field, all require an understanding of Statistics. It is the core language of all these fields when it comes to Data Analysis.
You will be able to understand and master Machine Learning concepts when you understand the key foundations behind them. These come from mastering: Statistics and Mathematical Modelling.
Course Outline:
1. Master the Inferential Statistics Terminology and Concepts
Random Variables, Random Samples, the 4 types of Data, NOIR, Experiments vs Trials and Events.
2. Master the Discrete and Continuous Distributions and their Sub-Functions so you can know when and how to use them
Binomial, Bernoulli, Negative Binomial, Geometric, Poisson, Exponential, Uniform, Normal, T-Student, Chi-Squared, and F-Distribution.
3. Master Conversions from any Distribution to the Normal Distribution
From N to Z, from T to Z, from Chi-Squared to Z
4. Learn how to conduct Hypothesis Tests
1-Tailed and 2-Tailed, how to use any Statistical Table, Find Critical Values, compare to calculated test statistics, and make Conclusions.
5. Learn how to indicate conclusions based on percentages.
6. Learn how to build Confidence Intervals for a Population Parameter
7. Learn how to calculate Population Estimators using Advanced Statistics Techniques
Ordinary Least Squares (OLS), Method of Moments Estimator (MME), and Maximum Likelihood Estimator(MLE)
Pre-requisites
- No prior knowledge needed. You will learn everything you need to know
Target Audience
- Beginner Data Science students and professionals
Curriculum 32 Lectures 01:04:14
-
Section 1 : Introduction
- Lecture 2 :
- What is the purpose of Statistics
-
Section 2 : Key Terminology
- Lecture 1 :
- Population vs Sample
- Lecture 2 :
- Inferential Approaches: Estimation
- Lecture 3 :
- Inferential Approaches: Hypothesis Testing
- Lecture 4 :
- Population Parameters and Sample Statistics
- Lecture 5 :
- Data Types(Qualitative and Quantitative), Samples
- Lecture 6 :
- Trials, Experiments, Events, Independence and Likelihood
-
Section 3 : Distributions: key properties and theorems
- Lecture 1 :
- What is a distribution?
- Lecture 2 :
- Discrete: Binomial and Bernoulli
- Lecture 3 :
- Discrete: Negative Binomial and Geometric
- Lecture 4 :
- Discrete: Poisson
- Lecture 5 :
- Continuous: Exponential
- Lecture 6 :
- Continuous: Uniform
- Lecture 7 :
- Continuous: Normal and Central Limit Theorem
- Lecture 8 :
- T-Student Distribution
- Lecture 9 :
- Standardization Z-Score
- Lecture 10 :
- When to use N vs T-Student Distribution
- Lecture 11 :
- Continuous: Chi-Squared
- Lecture 12 :
- F-Distribution and ANOVA
- Lecture 13 :
- Probability Functions Revisited: Tables, PMF, PDF and CDF
-
Section 4 : Learn about the main distributions and how to use them
- Lecture 1 :
- What is an Hypothesis test, procedures and errors
- Lecture 2 :
- Estimators and Main Techniques
- Lecture 3 :
- Estimators: OLS
- Lecture 4 :
- Estimators: MME
- Lecture 5 :
- Estimators: MLE
-
Section 5 : Section I Summary
- Lecture 1 :
- Section I Summary
-
Section 6 : PART 2: Exercises: Hypothesis Testing
- Lecture 1 :
- 1-WAY ANOVA Exercises
- Lecture 2 :
- 2-WAY ANOVA Exercises
-
Section 7 : PART 2: Exercises: Confidence Intervals
- Lecture 1 :
- How to build a confidence interval
- Lecture 2 :
- find k, E(X) and Var(X)
- Lecture 3 :
- Course Summary
Our learners work at
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