Machine Learning A-Z : Become Kaggle Master

https://www.udemy.com/machinelearning-kaggle/

Master Machine Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights. Created by Teclov Pvt Ltd Last updated 14/12/2018 English Course content Expand all 257 lectures 36:19:26 文件大小: 13.97GB What you’ll learn Master Machine Learning on Python Learn to use MatplotLib for Python Plotting Learn to use Numpy and Pandas for Data Analysis Learn to use Seaborn for Statistical Plots Learn All the Mathmatics Required to understand Machine Learning Algorithms Implement Machine Learning Algorithms along with Mathematic intutions Projects of Kaggle Level are included with Complete Solutions Learning End to End Data Science Solutions All Advanced Level Machine Learning Algorithms and Techniques like Regularisations , Boosting , Bagging and many more included Learn All Statistical concepts To Make You Ninza in Machine Learning Real World Case Studies Model Performance Metrics Deep Learning Model Selection Requirements Any Beginner Can Start this Course 2+2 knowledge is more than sufficient as we have covered almost everything from scratch. Description Want to become a good Data Scientist? Then this is a right course for you. This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advance level. We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites. We have covered following topics in detail in this course: 1. Python Fundamentals 2. Numpy 3. Pandas 4. Some Fun with Maths 5. Inferential Statistics 6. Hypothesis Testing 7. Data Visualisation 8. EDA 9. Simple Linear Regression 10. Multiple Linear regression 11. Hotstar/ Netflix: Case Study 12. Gradient Descent 13. KNN 14. Model Performance Metrics 15. Model Selection 16. Naive Bayes 17. Logistic Regression 18. SVM 19. Decision Tree 20. Ensembles – Bagging / Boosting 21. Unsupervised Learning 22. Dimension Reduction 23. Advance ML Algorithms 24. Deep Learning