Machine Learning A-Z : Become Kaggle Master
Master Machine Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights.
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
- Any Beginner Can Start this Course
- 2+2 knowledge is more than sufficient as we have covered almost everything from scratch.
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
4. Some Fun with Maths
5. Inferential Statistics
6. Hypothesis Testing
7. Data Visualisation
9. Simple Linear Regression
10. Multiple Linear regression
11. Hotstar/ Netflix: Case Study
12. Gradient Descent
14. Model Performance Metrics
15. Model Selection
16. Naive Bayes
17. Logistic Regression
19. Decision Tree
20. Ensembles – Bagging / Boosting
21. Unsupervised Learning
22. Dimension Reduction
23. Advance ML Algorithms
24. Deep Learning
- This course is meant for anyone who wants to become a Data Scientist
Size: 13.97 GB