Brochure
Enquire
- Outline and Motivation
- Where to get the Code and Data
- All Data is the Same
- Plug-and-Play
- How to Succeed in This Course
Ensemble Machine Learning in Python: Random Forest, AdaBoost
Quick Facts
| particular | details | |||
|---|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course overview
Ensemble Machine Learning in Python: Random Forest, AdaBoost course is an online course that could be taken by learners who are keen to learn the Ensemble Machine Learning. The curriculum will explore different Ensemble Methods of Python, namely, Boosting, Bagging, Bootstrap, and Statistical Machine Learning for Data Science. The course is relevant as Machine Learning is embedded in all walks of life and all industries.
Ensemble Machine Learning in Python: Random Forest, AdaBoost online course, provided by Udemy, trains the learners in ensemble methods of Machine learning which is to combine the available models of machine learning and come with a powerful model outdoing the shortcomings. The participants will go through ways to bring together models like decision trees and logistic regression to build more outstanding models. The programme specifically focuses on studying the Random Forest and AdaBoost algorithms.
Ensemble Machine Learning in Python: Random Forest, AdaBoost certification demands certain prerequisites including Calculus, Probability, Object-oriented programming, Numpy coding, Python coding and Simple machine learning models. The short training also enables the participants to engage in hands-on practical aspects through many experiments to use these algorithms on real datasets. The interested learners could enrol on the course by paying the fee. Early birds will be given a discount.
The highlights
- Online course
- Free materials
- English videos with subtitle
- Shareable certificate
- Full lifetime access
- Access on mobile and TV
- 30-Day Money-Back Guarantee
Program offerings
- Full lifetime access
- Access on mobile and tv
- Certificate of completion
- Flexible deadlines
- 5.5 hours on-demand video
- English videos subtitle
Course and certificate fees
Fees information
₹ 1,199
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Knowledge of python
At the end of the Ensemble Machine Learning in Python: Random Forest, AdaBoost online certification, the participating learners will be able to understand the bias-variance decomposition, the method of bootstrap and its application to bagging and the role of bagging in improving the classification and regression performance. Plus, the learner will be able to gain knowledge on Random Forest and its application.
The syllabus
Get Started
Bias-Variance Trade-Off
- Bias-Variance Key Terms
- Bias-Variance Trade-Off
- Bias-Variance Decomposition
- Polynomial Regression Demo
- K-Nearest Neighbor and Decision Tree Demo
- Cross-Validation as a Method for Optimizing Model Complexity
- Suggestion Box
Bootstrap Estimates and Bagging
- Bootstrap Estimation
- Bootstrap Demo
- Bagging
- Bagging Regression Trees
- Bagging Classification Trees
- Stacking
Random Forest
- Random Forest Algorithm
- Random Forest Regressor
- Random Forest Classifier
- Random Forest vs Bagging Trees
- Implementing a "Not as Random" Forest
- Connection to Deep Learning: Dropout
AdaBoost
- AdaBoost Algorithm
- Additive Modeling
- AdaBoost Loss Function: Exponential Loss
- AdaBoost Implementation
- Comparison to Stacking
- Connection to Deep Learning
- Summary and What's Next
Background Review
- Confidence Intervals
Setting Up Your Environment (FAQ by Student Request)
- Anaconda Environment Setup
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix / FAQ Finale
- What is the Appendix?
- BONUS
Articles
Popular Articles
prev
next
