pythonaa

-Chapter 1: Math for Data science and AI
-Chapter 2: Numerical Computing with NumPy
-Chapter 3: Data Collection (web scraping, Automation, …)/ Data Manipulation/ Data Cleaning
-Chapter 4: Exploratory Data Analysis (EDA)
-Chapter 5: Features Engineering - Features Selection
-Chapter 6: AI Subdomains / Machine Learning (supervised/unsupervised)
-Chapter 7: Projects (link AI models with mobile/web apps, make predictions with numerical/image/time series data)

Introduction _ Math for AI and Data Science (Part 1)

01:29:09
Math for AI and Data Science (Part 2) _ Numerical Computing with NumPy (Part 1)

01:33:13
Numerical Computing with NumPy (Part 2) _ Data Collection (Part 1)

01:24:15
Data Collection (Part 2)

01:14:44
Data Manipulation (Part 1)

01:15:46
Data Manipulation (Part 2)

01:39:17
Data Manipulation (Part 3)

01:26:59
Exploratory Data Analysis (EDA)

01:40:35
Data Cleaning

01:42:57
Features Engineering _ Features Selection

01:22:45
Machine Learning Algorithms (Part 1)

01:27:11
Machine Learning Algorithms (Part 2)

01:39:34
Machine Learning Algorithms (Part 3)

01:11:41
Machine Learning Algorithms (Part 4)

01:36:52
Evaluate Models

01:33:23
Bagging / Boosting / Stacking / Pipelines / Saving Models

01:26:47
Link Models with apps / Time Series Data

01:40:54
Machine Learning Projects (Part 1)

01:34:38
Machine Learning Projects (Part 2)

01:03:36
Machine Learning Projects (Part 3)

01:32:04
Machine Learning Projects (part 4)

01:33:41
Machine Learning Projects (part 5)

01:18:30
Machine Learning Projects (part 6)

01:10:02
Machine Learning Projects (part 7)

01:29:37
Machine Learning Projects (part 8)

01:41:03
Machine Learning Projects (part 9)

01:22:28
Machine Learning Projects (part 10)

01:18:59
Machine Learning Projects (part 11)

01:17:08
Machine Learning Projects (part 12)

01:27:59

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