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
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 | 00:00:00 | ||
| 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 | 00:02:00 | ||
| 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 | 00:00:00 | ||
| 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 | 00:01:00 | ||
| 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 | ||
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