Statistics & Probability for Data Science & Machine Learning

Description

This comprehensive course on Statistics & Probability for Data Science & Machine Learning will deepen your understanding on this topic.

After successful completion of this course you can acquire the required skills in this sector. This Statistics & Probability for Data Science & Machine Learning comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.

So enrol in this course today to fast track your career ladder.

You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.

There is no experience or previous qualifications required for enrolment on this Statistics & Probability for Data Science & Machine Learning. It is available to all students, of all academic backgrounds.

Our Statistics & Probability for Data Science & Machine Learning is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.

There is no time limit for completing this course, it can be studied in your own time at your own pace.

Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-

And much more!

10 sections • 89 lectures • 11:27:00 total length

•Welcome!: 00:02:00

•What will you learn in this course?: 00:06:00

•How can you get the most out of it?: 00:06:00

•Intro: 00:03:00

•Mean: 00:06:00

•Median: 00:05:00

•Mode: 00:04:00

•Mean or Median?: 00:08:00

•Skewness: 00:08:00

•Practice: Skewness: 00:01:00

•Solution: Skewness: 00:03:00

•Range & IQR: 00:10:00

•Sample vs. Population: 00:05:00

•Variance & Standard deviation: 00:11:00

•Impact of Scaling & Shifting: 00:19:00

•Statistical moments: 00:06:00

•What is a distribution?: 00:10:00

•Normal distribution: 00:09:00

•Z-Scores: 00:13:00

•Practice: Normal distribution: 00:04:00

•Solution: Normal distribution: 00:07:00

•Intro: 00:01:00

•Probability Basics: 00:10:00

•Calculating simple Probabilities: 00:05:00

•Practice: Simple Probabilities: 00:01:00

•Quick solution: Simple Probabilities: 00:01:00

•Detailed solution: Simple Probabilities: 00:06:00

•Rule of addition: 00:13:00

•Practice: Rule of addition: 00:02:00

•Quick solution: Rule of addition: 00:01:00

•Detailed solution: Rule of addition: 00:07:00

•Rule of multiplication: 00:11:00

•Practice: Rule of multiplication: 00:01:00

•Solution: Rule of multiplication: 00:03:00

•Bayes Theorem: 00:10:00

•Bayes Theorem - Practical example: 00:07:00

•Expected value: 00:11:00

•Practice: Expected value: 00:01:00

•Solution: Expected value: 00:03:00

•Law of Large Numbers: 00:08:00

•Central Limit Theorem - Theory: 00:10:00

•Central Limit Theorem - Intuition: 00:08:00

•Central Limit Theorem - Challenge: 00:11:00

•Central Limit Theorem - Exercise: 00:02:00

•Central Limit Theorem - Solution: 00:14:00

•Binomial distribution: 00:16:00

•Poisson distribution: 00:17:00

•Real life problems: 00:15:00

•Intro: 00:01:00

•What is a hypothesis?: 00:19:00

•Significance level and p-value: 00:06:00

•Type I and Type II errors: 00:05:00

•Confidence intervals and margin of error: 00:15:00

•Excursion: Calculating sample size & power: 00:11:00

•Performing the hypothesis test: 00:20:00

•Practice: Hypothesis test: 00:01:00

•Solution: Hypothesis test: 00:06:00

•T-test and t-distribution: 00:13:00

•Proportion testing: 00:10:00

•Important p-z pairs: 00:08:00

•Intro: 00:02:00

•Linear Regression: 00:11:00

•Correlation coefficient: 00:10:00

•Practice: Correlation: 00:02:00

•Solution: Correlation: 00:08:00

•Practice: Linear Regression: 00:01:00

•Solution: Linear Regression: 00:07:00

•Residual, MSE & MAE: 00:08:00

•Practice: MSE & MAE: 00:01:00

•Solution: MSE & MAE: 00:03:00

•Coefficient of determination: 00:12:00

•Root Mean Square Error: 00:06:00

•Practice: RMSE: 00:01:00

•Solution: RMSE: 00:02:00

•Multiple Linear Regression: 00:16:00

•Overfitting: 00:05:00

•Polynomial Regression: 00:13:00

•Logistic Regression: 00:09:00

•Decision Trees: 00:21:00

•Regression Trees: 00:14:00

•Random Forests: 00:13:00

•Dealing with missing data: 00:10:00

•ANOVA - Basics & Assumptions: 00:06:00

•One-way ANOVA: 00:12:00

•F-Distribution: 00:10:00

•Two-way ANOVA - Sum of Squares: 00:16:00

•Two-way ANOVA - F-ratio & conclusions: 00:11:00

•Wrap up: 00:01:00

•Assignment - Statistics & Probability for Data Science & Machine Learning: 00:00:00