Python Full Course for Beginners to Advanced with AI (2026)
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/DataAnalyti...
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():
00:58:37 – Type Casting: int(), float() & str() conversion.
01:03:35 – len():
01:07:30 – Unit 4: Conditional logic & Boolean flow.
01:10:30 – if Logic:
01:14:29 – if-else:
01:18:28 – if-elif-else:
01:27:03 – AI Study: Automating summaries via NotebookLM.
01:31:25 – Unit 5: Iterative logic & repetition psychology.
01:34:35 – while Loops
01:38:53 – Safety: Infinite loop recovery
01:41:12 – for Loops
01:42:56 – range(): start, stop, step parameters.
01:44:24 – Iteration:
01:45:32 – break
01:48:27 – continue: Skipping current iteration.
01:52:40 – Unit 6: User-Defined Functions
01:56:05 – def keyword
01:57:33 – Mini-Project
02:04:11 – Parameters: Handling dynamic Arguments.
02:10:46 – Recruiter Quiz: AI-driven technical assessment.
02:13:25 – Strings: Positive/Negative indexing & Slicing.
02:27:16 – String Methods: .upper(), .lower(), .replace(), .find().
02:34:17 – Lists: Mutability, indexing & item assignment.
02:46:48 – List Ops: .append(), .remove() & direct edits.
02:53:35 – Joining: List concatenation via +.
02:55:02 – Tuples: Immutability & List-Conversion hack.
03:05:13 – Dictionaries: Key-Value (JSON) architecture.
03:13:42 – Dict Ops: .update(), .pop() & clearing data.
03:16:28 – Sets: Unordered unique items & .union().
03:31:41 – NumPy: Performance Benchmark vs Lists.
03:41:16 – Arrays: 1D, 2D (Matrices) & 5D tensors.
03:50:50 – Array Ops: Reshaping & Random data generation.
03:55:07 – Pandas: Data Wrangling & DataFrame logic.
03:57:20 – Titanic EDA: CSV I/O implementation.
04:02:03 – Describe(): Statistical health audit (Mean, Min, Max).
04:10:48 – Feature Engineering: Column insertion & dropping.
04:18:49 – Data Cleaning: Mean Imputation for missing values.
04:26:09 – Matplotlib: Linear vs Zig-zag line plotting.
04:34:45 – Charts: Customizing vertical/horizontal Bar plots.
04:40:12 – Pie Charts: Visualizing categorical distribution.
04:41:42 – Seaborn: Statistical Data Visualization (SDV).
04:47:21 – Hue Parameter: Multi-categorical color coding.
04:55:00 – Distributions: Histograms & KDE Density plots.
04:58:38 – Capstone: Conversational Voice-AI prototype.
05:01:11 – Cloud Dev: Google AI Studio implementation.
05:05:51 – Local Dev: Coding Voice Assistant via Python.
05:06:58 – Integrations: Speech Recognition, pyttsx3 & Wiki-API.
05:08:33 – Execution: Live Q&A with Python-built Bot.
05:10:47 – Summary
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