Curriculum
- 19 Sections
- 93 Lessons
- 56 Weeks
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- About the Course
Python Zero to Hero Covering Machine Learning and Web Development + [Capstone Project From Scratch ]
Learn to create Industry Level Capstone Project with Machine Learning and Web Development in Python and make yourself future ready- 📚80+ video Lectures
- 👣 Beginner Friendly
- 📌 3 Mini Project in Python
- 📑 Assignment Based Learning
- 👨🏻💻 One Industry Level Project using Python | Machine Learning | Web Development
- 📄 Completion Certificate
- ⭐️ Trusted by 100+ Students
- 🔥 10 Lakh+ Video Views of Youtube
- 💻 Python from Scratch
- 🗣 Machine Learning from Scratch
- 📊 Web Development using flask
- 👨🏻💻 One Industry Level Project using Python | Machine Learning | Web Development
The one-stop destination to Start your Machine Learning and Data Science Journey
So let's dive in - Enroll today, Learn the Fundamentals & get to work with your Dream Company0 - 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
- Python Assignments2
- [Bonus] Python Hands-On Projects + Source Code4
- Machine Learning ~ Section 01 : Introduction & Supervised / Unsupervised Learning3
- Machine Learning ~ Section 02 : Regression [Models, Implementation]13
- 12.12.1 Linear Regression Introduction10 Minutes
- 12.22.2 Linear Regression Mathematics13 Minutes
- 12.32.3 Linear Regression Implementation13 Minutes
- 12.42.4 Regression Using Karl Pearson Coefficient5 Minutes
- 12.52.5 Linear Regression using Karl Pearson Coefficient Implementation6 Minutes
- 12.62.6 Linear Regression Library Implementation5 Minutes
- 12.72.7 Loss Analysis Using MSE8 Minutes
- 12.82.8 Mean Squared Error4 Minutes
- 12.92.9 Goodness Of Fit.10 Minutes
- 12.102.10 R-Squared Implementation3 Minutes
- 12.112.11 R-Squared Using Karl Pearson Coefficient5 Minutes
- 12.122.12 R-Squared Using Karl Pearson5 Minutes
- 12.132.13 Library Implementation of Metrics3 Minutes
- Machine Learning ~ Section 03 : Data Processing & Pandas4
- Machine Learning ~ Section 04 : Classification, Score Analysis & [Bonus] Google Colabration9
- 14.14.1 Classification Models8 Minutes
- 14.24.2 Logistic Regression5 Minutes
- 14.34.3 Loss For Classification Models3 Minutes
- 14.44.4 Log Loss Implementation7 Minutes
- 14.54.5 Score Analysis Basics5 Minutes
- 14.64.6 Confusion Matrix Implementation8 Minutes
- 14.74.7 Precision & Recall7 Minutes
- 14.84.8 F1 Score5 Minutes
- 14.9[Bonus] Google Colabration9 Minutes
- Machine Learning ~ Section 05 : K-Means Clustering, Decision Tree Classifier, Support Vector Information6
- Machine Learning Assignment5
- 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
- 19.13.1 Template Prerequisites12 Minutes
- 19.23.2 Bootstrap12 Minutes
- 19.33.3 Flask File Hierarchy6 Minutes
- 19.43.4 Rendering Template5 Minutes
- 19.53.5 Jinja Templating5 Minutes
- 19.63.6 Conditions in Jinja7 Minutes
- 19.73.7 Enumeration In Jinja4 Minutes
- 19.83.8 Static Directory6 Minutes
- 19.93.9 Methods5 Minutes
- 19.103.10 Requests6 Minutes
- 19.113.11 Flash10 Minutes
- 19.123.12 Forms-Phase-19 Minutes
- 19.133.13 Forms-Phase-27 Minutes
- 19.143.14 Forms-Phase-310 Minutes
- 19.153.15 Forms-Phase-411 Minutes
- 19.163.16 Forms-Phase-56 Minutes
- 19.173.17 Forms-Phase-612 Minutes
- Hands-On : Capstone Project + Source Code3
2.2 Lists and Dictionaries
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