Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
19 Hour(s) 37 Minute(s)
Language
English
Taught by
Geekshub Pvt Ltd
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
3 views
Course Overview
Master Machine Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights.
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
Course Content
- 26 section(s)
- 257 lecture(s)
- Section 1 Python Fundamentals
- Section 2 Numpy
- Section 3 Pandas
- Section 4 Some Fun With Maths
- Section 5 Inferential Statistics
- Section 6 Hypothesis Testing
- Section 7 Data Visualisation
- Section 8 Exploratory Data Analysis
- Section 9 Simple Linear Regression
- Section 10 Multiple Linear Regression
- Section 11 Hotstar/Netflix: Real world Case Study for Multiple Linear Regression
- Section 12 Gradient Descent
- Section 13 KNN
- Section 14 Model Performance Metrics
- Section 15 Model Selection Part1
- Section 16 Naive Bayes
- Section 17 Logistic Regression
- Section 18 Support Vector Machine (SVM)
- Section 19 Decision Tree
- Section 20 Ensembling
- Section 21 Model Selection Part2
- Section 22 Unsupervised Learning
- Section 23 Dimension Reduction
- Section 24 Advanced Machine Learning Algorithms
- Section 25 Deep Learning
- Section 26 Project : Kaggle
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
Skills covered in this course
Reviews
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강강성종
Well geared carriculum
-
AAnkit Manoj Chawrai
Support from author is not there. Explanations were good but sample data missing from few section, informed the author but no response.
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YYogesh Vijay Valunj
Good course.. but poor grammar and typos on screen spoil the fun. Sometimes, it feels like ppt deck would have been a better choice instead of black board approach.
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MMay Nejad
The course unfortunately did not offer any help in the Q&A section. Codes were not updated and had many errors where had to set aside and move on to the next section. No one on the other side was available either. Many of the questions asked were not answered. Instructor kept saying to ask in the lecture Q&A, which was in no use. Class notes could be prepared in a way where they could be downloaded for future reference, not with screenshots but with actual reviewable and searchable format, .doc .ppt .ipynb
