Python Full Course for Beginners to Advanced with AI (2026)

The iScale · Beginner ·📊 Data Analytics & Business Intelligence ·3mo ago

Key Takeaways

Teaches Python programming from beginner to advanced level with AI applications

Original Description

Learn Python from Beginner to Advanced with AI in this complete course! Master Python step-by-step with real-world projects and no prior experience. 📥 Download the Full Python Course Notes (Free): https://www.theiscale.com/DataAnalytics/python-full-course-with-ai-beginners-to-advance-notes In this video, you will learn: ✅ Python basics (variables, loops, functions) ✅ Advanced concepts (OOP, modules, projects) ✅ How to use AI tools with Python ✅ Real-world projects step-by-step 💡 This course is perfect for: - Beginners with no coding experience - Students & developers - Anyone who wants to learn Python with AI ⏱️ Timestamps: 00:00:00 – Intro: Python in AI ecosystem. 00:01:24 – Roadmap: Salary metrics & Tech roles (DA, DS, AI). 00:02:13 – Resources: Manual, Codebase & Datasets. 00:05:08 – Python vs LLMs: Scripting power vs AI limitations. 00:06:22 – Demo 1: Local File System (OS) operations. 00:08:18 – Demo 2: Hardware access (Peripherals control). 00:09:50 – Demo 3: Automated Web Scraping script. 00:14:51 – Anaconda: GUI environment setup. 00:16:26 – Installation: Step-by-step 64-bit config. 00:19:12 – Jupyter: Launching kernel 00:21:01 – Jupyter Mastery: Markdown vs Code cells. 00:21:52 – Unit 1: "Hello World" implementation. 00:22:51 – System Check: Runtime versioning via sys. 00:23:52 – Comments: Single-line (#) & Multi-line strings ("""). 00:26:46 – Unit 2: Variables & E-commerce data modeling. 00:28:47 – Naming Rules: 00:31:09 – Sensitivity: Case-sensitive variable auditing. 00:33:41 – Primitives: int vs float precision. 00:34:39 – Advanced Primitives: str & complex numbers. 00:35:39 – Collections: Initializing list, tuple, dict & set. 00:38:14 – Arithmetic Ops: Binary operators (+, -, *). 00:40:42 – Division/Modulus: / quotient vs % remainder. 00:41:58 – Exponentiation: Power calculation using **. 00:46:23 – Unit 3: Built-in function reusability logic. 00:47:38 – eval(): 00:50:12 – abs(): 00:53:01 – sum(): 00:55:00 – pow(): 00:56:00 – input(): 00:58
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Chapters (29)

Intro: Python in AI ecosystem.
1:24 Roadmap: Salary metrics & Tech roles (DA, DS, AI).
2:13 Resources: Manual, Codebase & Datasets.
5:08 Python vs LLMs: Scripting power vs AI limitations.
6:22 Demo 1: Local File System (OS) operations.
8:18 Demo 2: Hardware access (Peripherals control).
9:50 Demo 3: Automated Web Scraping script.
14:51 Anaconda: GUI environment setup.
16:26 Installation: Step-by-step 64-bit config.
19:12 Jupyter: Launching kernel
21:01 Jupyter Mastery: Markdown vs Code cells.
21:52 Unit 1: "Hello World" implementation.
22:51 System Check: Runtime versioning via sys.
23:52 Comments: Single-line (#) & Multi-line strings (""").
26:46 Unit 2: Variables & E-commerce data modeling.
28:47 Naming Rules:
31:09 Sensitivity: Case-sensitive variable auditing.
33:41 Primitives: int vs float precision.
34:39 Advanced Primitives: str & complex numbers.
35:39 Collections: Initializing list, tuple, dict & set.
38:14 Arithmetic Ops: Binary operators (+, -, *).
40:42 Division/Modulus: / quotient vs % remainder.
41:58 Exponentiation: Power calculation using **.
46:23 Unit 3: Built-in function reusability logic.
47:38 eval():
50:12 abs():
53:01 sum():
55:00 pow():
56:00 input():
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