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Course Overview
Learn how to make a genuine difference in your life by taking our popular Statistics & Probability for Data Science & Machine Learning. Our commitment to online learning and our technical experience has been put to excellent use within the content of these educational modules. By enrolling today, you can take your knowledge of Statistics & Probability for Data Science & Machine Learning to a whole new level and quickly reap the rewards of your study in the field you have chosen.
We are confident that you will find the skills and information that you will need to succeed in this area and excel in the eyes of others. Do not rely on substandard training or half-hearted education. Commit to the best, and we will help you reach your full potential whenever and wherever you need us.
Please note that Statistics & Probability for Data Science & Machine Learning provides valuable and significant theoretical training for all. However, it does not offer official qualifications for professional practice. Always check details with the appropriate authorities or management.
Learning Outcomes
- Instant access to verified and reliable information
- Participation in inventive and interactive training exercises
- Quick assessment and guidance for all subjects
- CPD accreditation for proof of acquired skills and knowledge
- Freedom to study in any location and at a pace that suits you
- Expert support from dedicated tutors committed to online learning
Your Path to Success
By completing the training in Statistics & Probability for Data Science & Machine Learning, you will be able to significantly demonstrate your acquired abilities and knowledge of Statistics & Probability for Data Science & Machine Learning. This can give you an advantage in career progression, job applications, and personal mastery in this area.
Is This Course Right for You?
This course is designed to provide an introduction to Statistics & Probability for Data Science & Machine Learning and offers an excellent way to gain the vital skills and confidence to start a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Full-time and part-time learners are equally supported, and the study periods are entirely customisable to your needs.
Assessment Process
Once you have completed all the modules in the Statistics & Probability for Data Science & Machine Learning course, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.
Show off Your New Skills with a Certification of Completion
The learners have to successfully complete the assessment of this Statistics & Probability for Data Science & Machine Learning course to achieve the CPD & IAO accredited certificate. Digital certificates can be ordered for only £10. Learners can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.
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 | 3 weeks, 3 days | ||
COURSE REVIEWS
- 1 year
- Intermediate
- Number of Units89
- Number of Quizzes0
- 3 weeks, 4 days Gift this course
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