Machine Learning A-Z: Become Kaggle Master

Rating 4.45 out of 5 (480 ratings in Udemy)

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 …

Rating 4.45 out of 5 (480 ratings in Udemy)

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

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

Duration 36 Hours 58 Minutes

Paid

Self paced

All Levels

English (US)

2952

Rating 4.45 out of 5 (480 ratings in Udemy)

Go to the Course
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