Your First Machine Learning Project - Crash Course
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(5)
38 learners
What you'll learn
This course includes
- 5.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 20 lessons • 5.5 hours of video
Your First Machine Learning Project - Crash Course
20 lessons
• 5.5 hours
Your First Machine Learning Project - Crash Course
20 lessons
• 5.5 hours
- Build Your FIRST Machine Learning Project (Code + Concepts) 01:46
- Build Your First ML Project part 2: How to FORMULATE Machine Learning Problem 10:47
- Setup Python Environment using ANACONDA 15:30
- Jupyter Notebook Tutorial - How to Install and complete walkthrough 29:17
- What is ML Modeling? (Problem statement and Data) | Build Your First ML Project - Part 5 15:55
- Exploratory Data Analysis (EDA) - Use these 5 tactics on any ML project (..and wow!!) 34:18
- Reduce the memory size of Pandas Dataframe: Do this to make your code run 5X FASTER 12:38
- How to handle missing data for machine learning 33:17
- Multiple Imputation by Chained Equations (MICE) clearly explained 15:25
- How to impute missing data in categorical features (using MICE) 08:41
- How to Detect Outliers with IQR and Boxplot? | Code Walkthrough in Python 11:53
- How to Detect Outliers with Z Score | Clearly Explained 12:44
- Why mahalanobis distance is incredibly powerful for outlier detection 18:48
- Understanding Cooks Distance to detect influential observations 15:40
- Isolation Forest: A Tree based approach for Outlier Detection (Clearly Explained) 18:02
- Feature Encoding in ML: Beyond the Basics 08:15
- Understanding Target Encoding for Categorical Features 14:10
- Bayesian Target Encoding to boost model accuracy - Clearly Explained 28:24
- Feature Scaling Techniques-Avoid this untraceable mistake at all costs 18:04
- Train, Test and Split: This unforgivable mistake will cost your model's credibility 11:14
