This course is designed for those interested to learn the basics of machine learning, as well as simple linear regression, logistic regression, unsupervised learning, and additional machine learning in R.
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About This Course
Who this course is for:
- Anyone who is looking to understand more about Machine Learning with a view to break into a career in Data Science.Â
- Equally, you may be a professional working in tech or finance and want to learn new analytical techniques that can enhance your overall skill set.
What you’ll learn:Â
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Decision Tree
- Random Tree
- Model Ensembling And Unsupervised Learning
- Data Cleaning
- Additional Machine Learning in R
Requirements:Â
- No prior knowledge is required to take this course
This course is jam-packed with everything you need to get started with machine learning. We will look at various algorithm types, such as linear regression, logistic regression, decision trees, random forest, ensembled models, and beyond. Each model type will have lectures devoted to conceptualizing its mechanics, with some easy-to-follow math as well.Â
In addition to conceptual lectures, there will be accompanying videos on key data science theory to improve accuracy and avoid common model pitfalls. Finally, each model type will have a coding video in Python to demonstrate the algorithm in action. We will also discuss common tips and tricks for cleaning data in preparation for machine learning, as well as an overview of machine learning in R.
Our Promise to You
By the end of this course, you will have learned about machine learning.
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Get started today and learn more about machine learning.
Course Curriculum
| Section 1 - Simple Linear Regression | |||
| Simple Linear Regression – Theory | 00:00:00 | ||
| Simple Linear Regression – Code | 00:00:00 | ||
| Section 2 - Multiple Linear Regression | |||
| Multiple Linear Regression – Theory | 00:00:00 | ||
| Multiple Linear Regression – Code | 00:00:00 | ||
| Assumptions of Linear Regression – Theory | 00:00:00 | ||
| Curse of Dimensionality – TheoryV | 00:00:00 | ||
| Model Accuracy and Train Test Split – Code | 00:00:00 | ||
| Section 3 - Logistic Regression | |||
| Logistic Regression – Theory | 00:00:00 | ||
| Logistic Regression – Code | 00:00:00 | ||
| Section 4 - Decision Tree | |||
| Decision Tree – Theory | 00:00:00 | ||
| Cross-Validation And Hyperparameter Tuning – Theory | 00:00:00 | ||
| Decision Tree Classifier – Code | 00:00:00 | ||
| Decision Tree Classifier with Hyperparameter Tuning – Code | 00:00:00 | ||
| Decision Tree Regressor – Code | 00:00:00 | ||
| Decision Tree Regressor with Hyperparameter Tuning – Code | 00:00:00 | ||
| Section 5 - Random Forest | |||
| Random Forest – Theory | 00:00:00 | ||
| Random Forest Classifier – Code | 00:00:00 | ||
| Random Forest Classifier with Hyperparameter Tuning – Code | 00:00:00 | ||
| Random Forest Regressor – CodeV | 00:00:00 | ||
| Random Forest Regressor with Hyperparameter Tuning – Code | 00:00:00 | ||
| Section 6 - Model Ensembling And Unsupervised Learning | |||
| Ensembling Models – Theory | 00:00:00 | ||
| Model Ensembling – Code | 00:00:00 | ||
| Unsupervised vs. Supervised Learning + Summary of NLP and Deep Learning – TheoryV | 00:00:00 | ||
| Section 7 - Data Cleaning | |||
| Stepwise Selection – Theory | 00:00:00 | ||
| Stepwise Selection – Code | 00:00:00 | ||
| Label Encoding – Theory | 00:00:00 | ||
| Label Encoding – Code | 00:00:00 | ||
| 5. Dummification – Theory | 00:00:00 | ||
| Dummification – Code | 00:00:00 | ||
| KNN – Theory | 00:00:00 | ||
| KNN – Code | 00:00:00 | ||
| Data Cleaning Walkthrough One (Housing Prices) – Code | 00:00:00 | ||
| Data Cleaning Walkthrough Two (Titanic) – Code | 00:00:00 | ||
| Section 8 - Additional Machine Learning in R | |||
| Simple Linear Regression in R – Code | 00:00:00 | ||
| Simple Linear Regression in R – Code | 00:00:00 | ||
| Logistic Regression in R – Code | 00:00:00 | ||
| Decision Tree in R – Code | 00:00:00 | ||
| Random Forest in R – Code | 00:00:00 | ||
| 6. R Data Cleaning Example Part One – Code | 00:00:00 | ||
| 7. R Data Cleaning Example Part Two – Code | 00:00:00 | ||
Course Review
5.0
2 RatingsRelated Courses
Good Introductory Course
Very informative and well-rounded introductory to the topic.
Great Course
I was curious about Machine learning now I have a better idea of what it’s about. Thank you for this course.