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Statistics & Probability for Data Science & Machine Learning

What Will I Learn? Discover ways in which to work with descriptive statistics Learn the rules and practices for distribution …

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17 enrolled on this course

What Will I Learn?

Discover ways in which to work with descriptive statistics

Learn the rules and practices for distribution

Understand how to calculate and understand probabilities

Explore the principles and use of hypothesis testing

Know how to calculate the coefficient of determination

Gain a unique appreciation for ANOVA (Analysis of Variance)

Course Overview

Discover how to become a data scientist, prove hypotheses, and build complex algorithms with this advanced course on Statistics & Probability for Data Science & Machine Learning. This intuitive training will empower you to manipulate records and understand how to break down the most complex processes in this fascinating field.

This comprehensive Data Science tutorial delivers the ideal way to learn the methodology and principles needed to excel in this sector. You will be given expert tuition in using all the relevant concepts for analysing information, gain a genuine understanding of these concepts, and attain the skills to excel in appropriate IT commercial industries. Complete this training, and you will have a unique advantage to work in such areas as automobile design, banking service, media forecasting, and much more.

This best selling Statistics & Probability for Data Science & Machine Learning has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Statistics & Probability for Data Science & Machine Learning is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.

The Statistics & Probability for Data Science & Machine Learning is CPD-accredited, so you can be confident you’re completing a quality training course will boost your CV and enhance your career potential. The Statistics & Probability for Data Science & Machine Learning is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.

After successfully completing the Statistics & Probability for Data Science & Machine Learning, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Statistics & Probability for Data Science & Machine Learning will help you stand out from the crowd. You can also validate your certification on our website.

We know that you are busy and that time is precious, so we have designed the Statistics & Probability for Data Science & Machine Learning to be completed at your own pace, whether that’s part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.

Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

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Certification

After successfully completing the course, you will be able to get the UK and internationally accepted certificate to share your achievement with potential employers or include it in your CV. The PDF Certificate + Transcript is available at £6.99 (Special Offer - 50% OFF). In addition, you can get a hard copy of your certificate for £12 (Shipping cost inside the UK is free, and outside the UK is £9.99).

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?	00:08:00
	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
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 – Sum of Squares	00:16:00
	Two-way ANOVA – F-ratio & conclusions	00:11:00
Section 09: Wrap up
	Wrap up	00:01:00
Assignment
	Assignment – Statistics & Probability for Data Science & Machine Learning	00:00:00

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Welcome to

Course Cloud

Statistics & Probability for Data Science & Machine Learning

What Will I Learn?

Discover ways in which to work with descriptive statistics

Learn the rules and practices for distribution

Understand how to calculate and understand probabilities

Explore the principles and use of hypothesis testing

Know how to calculate the coefficient of determination

Gain a unique appreciation for ANOVA (Analysis of Variance)

Course Overview

Preview

Certification

Course Curriculum

Course Cloud Endorsed

Benefits:

CPD QS Accredited

Benefits:

Buying more than one course?

Get huge discounts for team training!

Buying for someone special?

Get for Family or Colleagues

This Course Includes

Share this Course:

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