Data Science Full Course in Tamil 2024 | Data Science Tutorial for Beginners in Tamil

Data Science Tutorial for Beginners in Tamil this video offers a comprehensive overview of key concepts in data science, artificial intelligence (AI), and machine learning (ML). It begins by defining data science, AI, and ML, then delves into the differences between these fields, highlighting their significance in the modern world. Data Science Tutorial for Beginners in Tamil the video further explores the roles and responsibilities within data science, essential skills required, and practical steps like installing the Anaconda application.
It also covers the basics of machine learning, including its importance, different types (supervised and unsupervised), and common algorithms. Additionally, Data Science Tutorial for Beginners in Tamil the video provides hands-on guidance, such as importing libraries and datasets, handling missing data, visualizing data, and splitting datasets for training and testing.
It concludes with a detailed look at training models, visualizing results, and evaluating model performance using metrics like mean absolute error, mean squared error, and root mean squared error.
00:00:22 - What is data science?
00:06:28 - What is AI?
00:07:46 - What is ML?
00:11:57 - Difference between Data Science, AI, and ML?
00:16:22 - What is the importance of data science & ML?
00:29:32 - Data science roles & responsibilities?
00:41:05 - Skills required in data science?
00:45:02 - Install Anaconda application?
00:48:32 - What is EDA?
00:50:38 - What is Data Colderon?
01:15:04 - Data understanding?
01:22:46 - Data cleaning?
01:31:56 - Data transformation?
01:33:16 - Group by
01:34:26 - Data visualization?
01:55:14 - What is machine learning?
01:59:50 - Why machine learning?
02:04:44 - Types of machine learning?
02:07:06 - Supervised machine learning?
02:22:22 - Types of classification?
02:39:20 - Unsupervised machine learning?
02:45:50 - Common types of algorithms?
03:10:26 - Importing the libraries
03:11:55 - Importing the dataset
03:13:23 - Taking care of missing data
03:15:00 - Visualizing data
03:25:18 - Splitting the dataset into train set and test set
03:32:41 - Train the model
03:41:35 - Visualize Train Results
03:47:15 - Mean absolute error
04:01:55 - Mean squared error
04:02:56 - Root mean squared error
04:06:41 - Visualize Test Results
04:02:25 - Evaluation Metrics ‎ ✨ About BTree Systems ✨
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