Machine Learning Full Course in Hindi - Part 1 | Basics of Machine Learning in 9 Hours | iScale

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Timestamps:
00:00 - Introduction of Machine Learning Full Course - Part 1
08:30 - Introduction & Type of ML
01:48:26 - Batch | Model | Instance-Based ML
03:47:51 - MLDLC | CSV / JSON / SQL Data Gathering
06:23:55 - Framing ML Problem | Fetching Data from an API
07:53:58 - Web Scraping for Data Gathering

Topic Content:

Day: 1 Introduction & Type of ML

 What is Machine Learning
 How has Machine Learning evolved? The History of ML
 ML vs DL vs AI
 Data Science Vs Data Analytics Vs ML/AI/DL
 Types of machine learning

Day:2 Batch | Model | Instance Based ML

 What is Batch / Offline Machine Learning?
 What is Online Machine Learning?
 Difference Between Online Vs Offline Machine Learning?
 Instance Based Machine Learning.
 Model Based Machine Learning.
 Instance Based Vs Model Based Machine Learning.
 Challenges in Machine Learning.
 Application of Machine Learning.
 Machine Learning Development Life Cycle.

Day:3 MLDLC | CSV / JSON / SQL Data Gathering

 Machine Learning Development Life Cycle (MLDLC/MLDC):
 Data science life cycle (DSLC):
 Tools used in Machine Learning? Installing: Anaconda | Jupiter Notebook (IDEs)
 Optional Tools: Spyder | PyCharm | Noteable | Google Colab | Kaggle Notebooks |
Microsoft Azure Notebooks | Apache Zeplin | Count.co and Many More
 How to import dataset and download data files?
 How we create virtual environment
 Data Gathering
 Working with CSV Files
 Working with JSON/SQL

Day:4 Framing ML Problem | Fetching Data from an API

 Framing a Machine Learning Problem
 Data Gathering
- Fetching data from an API
- Fetching data using web scraping

Day: 5 Web Scraping for Data Gathering

 Fetching data using web scraping

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