PUBLISHER :- Dipnarayan Das
LANGUAGE :- English
PRIZE :-
117.23$0$
Fast track to ML, Data Science and Steganography
WHO CAN ACCESS THIS COURSE :-
- Data Science Enthusiast
- Machine Learning Enthusiast
- Image Processing Enthusiast
- Cryptography Enthusiast
- Statistics enthusiast
WHAT ARE YOUR REQUIREMENTS FOR THIS COURSE ?
- For Machine Learning metrices basics of ML should be cleared
- For Steganography, basic Cryptography and Image Processing should be known
WHAT YOU ARE GOING TO LEARN FROM THIS COURSE ?
- General / Statistical Measures
- Data Science measures / metrics
- Machine Learning measures / metrics
- Steganography measures / metrics
DESCRIPTION
Guide:
Aanchal Singhal
This is a brand new Machine Learning, Data Science, and Steganography based course updated with the latest trends and skills!
This is the first course in Udemy which will provide you detailed information about Steganography.
The topics covered in this course are:
– General / Statistical Measures
– Data Science Measures
– Machine Learning Measures / Performance metrics
– Steganography Measures / Metrics
Like every presentation need the final touch, this course will cover your gaps in Data Science, Machine Learning, and also in the newly trending topic Steganography. By the end of this course, you can be a Machine Learning, Data Scientist, Steganography expert, and can be get hired at large companies. This course will straight forward deliver rich information to users. Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.
See you inside the course!
COURSE CONTENT
- 6 sections • 45 lectures • 38m total length
Introduction2 lectures • 2min
- IntroductionPreview01:16
- Course StructurePreview00:20
General | Statistical Measures | Performance metrics19 lectures • 17min
- General / Statistical Measure’s Index00:26
- Confusion Matrix03:32
- Sensitivity00:26
- Precision00:21
- Specificity00:35
- PPV & NPV00:33
- Fall out00:26
- FDR01:26
- Miss Rate00:24
- Accuracy00:28
- F-score02:58
- Centrality measure00:17
- Mean00:56
- Median00:56
- Mode00:27
- Spread / Dispersion Measure00:31
- Range00:38
- Variance00:57
- Standard Deviation00:46
Data Science Measures7 lectures • 6min
- Data Science Measure’s Index00:20
- Grouping00:29
- Cross tab00:42
- Pivot table00:36
- Imputation01:44
- Data filling Algorithm00:20
- Outlier Detection01:35
Machine Learning Performance metrics5 lectures • 6min
- Machine Learning Measure’s Index00:17
- MCC01:15
- Box plot00:51
- Radar plot00:21
- ROC-AUC02:54
Steganography Metrics11 lectures • 8min
- Steganography Measure’s Index00:11
- Histogram00:29
- MSE01:04
- PSNR01:22
- SSIM01:17
- Chi-square00:37
- RS Analysis00:36
- StegExpose00:43
- Embedded Capacity00:18
- FOBP00:36
- Bit Rate00:22
Conclusion1 lecture • 1min
- Conclusion01:13
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