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Course Highlights
Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Statistics & Probability for Data Science & Machine Learning course has been specially designed to help learners gain a good command of Statistics & Probability for Data Science & Machine Learning, providing them with a solid foundation of knowledge to become a qualified professional.
Through this Statistics & Probability for Data Science & Machine Learning course, you will gain both practical and theoretical understanding of Statistics & Probability for Data Science & Machine Learning that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.
Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.
Learning outcome
- Familiar yourself with the recent development and updates of the relevant industry
- Know how to use your theoretical knowledge to adapt in any working environment
- Get help from our expert tutors anytime you need
- Access to course contents that are designed and prepared by industry professionals
- Study at your convenient time and from wherever you want
Course media
Why should I take this course?
- Affordable premium-quality E-learning content, you can learn at your own pace.
- You will receive a completion certificate upon completing the course.
- Internationally recognized Accredited Qualification will boost up your resume.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
- No formal qualifications required, anyone from any academic background can take this course.
- Access to a computer or digital device with internet connectivity.
Course Curriculum
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Welcome!
00:02:00 -
What will you learn in this course?
00:06:00
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How can you get the most out of it?
00:06:00
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Intro
00:03:00
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Mean
00:06:00
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Median
00:05:00
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Mode
00:04:00
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Mean or Median?
00:08:00
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Skewness
00:08:00
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Practice: Skewness
00:01:00
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Solution: Skewness
00:03:00
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Range & IQR
00:10:00
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Sample vs. Population
00:05:00
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Variance & Standard deviation
00:11:00
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Impact of Scaling & Shifting
00:19:00
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Statistical moments
00:06:00
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What is a distribution?
00:10:00
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Normal distribution
00:09:00
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Z-Scores
00:13:00
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Practice: Normal distribution
00:04:00
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Solution: Normal distribution
00:07:00
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Intro
00:01:00
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Probability Basics
00:10:00
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Calculating simple Probabilities
00:05:00
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Practice: Simple Probabilities
00:01:00
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Quick solution: Simple Probabilities
00:01:00
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Detailed solution: Simple Probabilities
00:06:00
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Rule of addition
00:13:00
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Practice: Rule of addition
00:02:00
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Quick solution: Rule of addition
00:01:00
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Detailed solution: Rule of addition
00:07:00
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Rule of multiplication
00:11:00
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Practice: Rule of multiplication
00:01:00
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Solution: Rule of multiplication
00:03:00
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Bayes Theorem
00:10:00
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Bayes Theorem – Practical example
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Expected value
00:11:00
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Practice: Expected value
00:01:00
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Solution: Expected value
00:03:00
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Law of Large Numbers
00:08:00
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Central Limit Theorem – Theory
00:10:00
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Central Limit Theorem – Intuition
00:08:00
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Central Limit Theorem – Challenge
00:11:00
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Central Limit Theorem – Exercise
00:02:00
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Central Limit Theorem – Solution
00:14:00
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Binomial distribution
00:16:00
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Poisson distribution
00:17:00
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Real life problems
00:15:00
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Intro
00:02:00
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What is a hypothesis?
00:19:00
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Significance level and p-value
00:06:00
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Type I and Type II errors
00:05:00
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Confidence intervals and margin of error
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Excursion: Calculating sample size & power
00:11:00
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Performing the hypothesis test
00:20:00
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Practice: Hypothesis test
00:01:00
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Solution: Hypothesis test
00:06:00
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T-test and t-distribution
00:13:00
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Proportion testing
00:10:00
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Important p-z pairs
00:08:00
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Intro
00:01:00
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Linear Regression
00:11:00
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Correlation coefficient
00:10:00
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Practice: Correlation
00:02:00
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Solution: Correlation
00:08:00
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Practice: Linear Regression
00:01:00
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Solution: Linear Regression
00:07:00
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Residual, MSE & MAE
00:08:00
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Practice: MSE & MAE
00:01:00
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Solution: MSE & MAE
00:03:00
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Coefficient of determination
00:12:00
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Root Mean Square Error
00:06:00
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Practice: RMSE
00:01:00
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Solution: RMSE
00:02:00
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Multiple Linear Regression
00:16:00
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Overfitting
00:05:00
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Polynomial Regression
00:13:00
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Logistic Regression
00:09:00
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Decision Trees
00:21:00
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Regression Trees
00:14:00
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Random Forests
00:13:00
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Dealing with missing data
00:10:00
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ANOVA – Basics & Assumptions
00:06:00
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One-way ANOVA
00:12:00
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F-Distribution
00:10:00
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Two-way ANOVA – Sum of Squares
00:16:00
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Two-way ANOVA – F-ratio & conclusions
00:11:00
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Wrap up
00:01:00
Offer Ends in
All
Courses for £49-
Duration:11 hours, 5 minutes
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Access:1 Year
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Units:88
