Python Zero to Hero Covering Web Development and Machine Learning + [Capstone Project From Scratch Included]
One of the most exclusive courses available at last moment tuitions
Features of this program:
1. You will be given access to pre-recorded videos.
2. You will be developing 3 mini-projects using python and 1 industrial level project using python + machine learning + flask throughout the whole program.
3. You will get a certificate on completion of this program.
The price for this Course is Rs. 5000
For further doubts and queries ask your question at the bottom of this form else contact us at:7038604912
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Course Features
- Lectures 86
- Quiz 0
- Duration 60 weeks
- Skill level All levels
- Students 17
- Assessments Yes
Curriculum
- 16 Sections
- 86 Lessons
- 60 Weeks
Expand all sectionsCollapse all sections
- Python ~ Section 01 : Introduction And Getting The Right Tools!1
- Python ~ Section 02 : Basic I/O, Operators & Using IDE5
- Python ~ Section 03 : Conditional Statements & Looping !5
- Python ~ Section 04 : OOPS! Functions, Classes & Exception Handling4
- Python ~ Section 05 : Python Modules & Experiencing Jupyter !5
- Python ~ Section 06 : Tkinter, SQL in Python & File Management3
- Web Development ~ Section 01 : Flask Introduction2
- Web Development ~ Section 02 : Routing & Redirecting2
- Web Development ~ Section 03 : Templating, Forms & Making API's With CRUD DB Operations17
- 9.13.1 Template Prerequisites12 Minutes
- 9.23.2 Bootstrap12 Minutes
- 9.33.3 Flask File Hierarchy6 Minutes
- 9.43.4 Rendering Template5 Minutes
- 9.53.5 Jinja Templating5 Minutes
- 9.63.6 Conditions in Jinja7 Minutes
- 9.73.7 Enumeration In Jinja4 Minutes
- 9.83.8 Static Directory6 Minutes
- 9.93.9 Methods5 Minutes
- 9.103.10 Requests6 Minutes
- 9.113.11 Flash10 Minutes
- 9.123.12 Forms-Phase-19 Minutes
- 9.133.13 Forms-Phase-27 Minutes
- 9.143.14 Forms-Phase-310 Minutes
- 9.153.15 Forms-Phase-411 Minutes
- 9.163.16 Forms-Phase-56 Minutes
- 9.173.17 Forms-Phase-612 Minutes
- Machine Learning ~ Section 01 : Introduction & Supervised / Unsupervised Learning3
- Machine Learning ~ Section 02 : Regression [Models, Implementation]13
- 11.12.1 Linear Regression Introduction10 Minutes
- 11.22.2 Linear Regression Mathematics13 Minutes
- 11.32.3 Linear Regression Implementation13 Minutes
- 11.42.4 Regression Using Karl Pearson Coefficient5 Minutes
- 11.52.5 Linear Regression using Karl Pearson Coefficient Implementation5 Minutes
- 11.62.6 Linear Regression Library Implementation5 Minutes
- 11.72.7 Loss Analysis Using MSE8 Minutes
- 11.82.8 Mean Squared Error4 Minutes
- 11.92.9 Goodness Of Fit10 Minutes
- 11.102.10 R-Squared Implementation3 Minutes
- 11.112.11 R-Squared Using Karl Pearson Coefficient5 Minutes
- 11.122.12 R-Squared Using Karl Pearson5 Minutes
- 11.132.13 Library Implementation of Metrics3 Minutes
- Machine Learning ~ Section 03 : Data Processing & Pandas4
- Machine Learning ~ Section 04 : Classification, Score Analysis & [Bonus] Google Colabration9
- 13.14.1 Classification Models8 Minutes
- 13.24.2 Logistic Regression5 Minutes
- 13.34.3 Loss For Classification Models3 Minutes
- 13.44.4 Log Loss Implementation7 Minutes
- 13.54.5 Score Analysis Basics5 Minutes
- 13.64.6 Confusion Matrix Implementation8 Minutes
- 13.74.7 Precision & Recall7 Minutes
- 13.84.8 F1 Score5 Minutes
- 13.9[Bonus] Google Colabration9 Minutes
- Machine Learning ~ Section 05 : K-Means Clustering, Decision Tree Classifier, Support Vector Information6
- Hands-On : Capstone Project + Source Code3
- [Bonus] Python Hands-On Projects + Source Code4
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