Statistics & Probability for Data Science & Machine Learning

Statistics & Probability for Data Science & Machine Learning

Course Overview Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no …

Statistics & Probability for Data Science & Machine Learning

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Intermediate

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11 hours, 26 minutes

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Course Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow Statistics & Probability for Data Science & Machine Learning will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course.

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge.

Entry Requirement

- This course is available to all learners of all academic backgrounds.
- Learners should be aged 16 or over to undertake the qualification.
- Some basic understanding of the English language and numeracy.

Certification

After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can, however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £29 or in PDF format at the cost of £19.

- PDF certificate’s turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days

Why choose us?

- - Affordable, engaging & high-quality e-learning study materials;
  - Tutorial videos/materials from the industry-leading experts;
  - Study in a user-friendly, advanced online learning platform;
  - Efficient exam systems for the assessment and instant result;
  - The UK & internationally recognised accredited qualification;
  - Access to course content on mobile, tablet or desktop from anywhere, anytime;
  - The benefit of career advancement opportunities;
  - 24/7 student support via email.

Career path

Develop Your Career Plan is a useful qualification to possess and would be beneficial for any related profession or industry.

Course Curriculum

Section 01: Let's Get Started
	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
Section 02: Descriptive Statistics
	Intro	00:03:00
	Mean	00:06:00
	Median	00:05:00
	Mode	00:04:00
	Mean or Median?	00:07:00
	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
Section 03: Distributions
	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
Section 04: Probability Theory
	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
Section 05: Hypothesis Testing
	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
Section 06: Regressions
	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
Section 07: Advanced Regression & Machine Learning Algorithms
	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
Section 08: ANOVA (Analysis Of Variance)
	ANOVA – Basics & Assumptions	00:06:00
	One-way ANOVA	00:12:00
	F-Distribution	00:10:00
	Two-way ANOVA â€“ Sum of Squares	00:16:00
	Two-way ANOVA â€“ F-ratio & conclusions	00:11:00
Section 09: Wrap Up
	Wrap up	00:01:00

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Statistics & Probability for Data Science & Machine Learning