Intellipaat Data Science Course is a free Data Science tutorial for beginners as well as professionals to learn Data Science and its algorithms in a comprehensive crash course. This complete Data Science course masters you to learn the Data Science principles, tools, algorithms, and methodologies that make it a powerful medium to solve business problems.
Below are the topics covered in this Data Science for Beginners Course video:
01:45 – Who is a Data Scientist?
03:03 – Where do Data Scientist come from?
05:34 – What does a Data Scientist do?
09:00 – Prerequisites to become a Data Scientist
13:12 – Data Scientist Roles and Responsibilities
19:17 – Data Scientist Salary
21:05 – Who’s hiring Data Scientist?
22:51 – Need of Data Science
24:13 – What is Data science?
25:14 – Understanding Different Techniques
25:21 – Data Visualization
25:39 – Data manipulation
26:05 – Statistical Analysis
26:25 – Machine Learning
28:09 – Life Cycle of Data Science
28:23 – Data Acquisition
28:50 – Data Preprocessing
29:06 – Model building
29:22 – Pattern Evaluation
29:44 – Knowledge Representation
30:03 – Chat-Bots
30:27 – Self driving Cars
30:45 – Sentiment Analysis
31:08 – Image – Tagging
31:25 – What is Numpy?
33:21 – How to Create Numpy Array?
36:10 – 2d Arrays in Numpy
39:37 – Numpy Array Initialization
47:00 – Numpy Array Inspection
01:06:50 – Numpy Broadcasting
01:09:40 – Indexing and Slicing in Python
01:15:23 – Array Manipulation in Python
01:36:30 – Advantages of Numpy over List
01:45:56 – Pandas Module
01:46:50 – What is Pandas?
01:48:24 – Where did the name pandas come from?
01:49:55 – Features of Pandas
01:56:20 – Pandas vs Numpy
01:58:24 – How to import Pandas in Python?
01:59:09 – Data Structure in Pandas
02:00:29 – What is series object?
02:03:00 – How to change the Index name?
02:05:16 – What is a DataFrame?
02:06:03 – Features of DataFrame
02:07:27 – How to Create DataFrame?
02:18:40 – Merge, Join and Concatenate
02:56:00 – Quiz
02:58:45 – Basics of Data Visualization
03:05:45 – Plotting the graphs (Matplotlib Demo)
03:13:33 – Data Visualization Libraries
03:15:35 – What is Matplotlib?
03:16:20 – Why choose Matplotlib?
03:21:45 – Types of Plots and Demo on Matplotlib
04:29:55 – Introduction to Machine Learning
04:41:46 – What is Machine Learning?
04:43:30 – How does Machine Learn?
04:45:16 – Machine Learning popular Myth!
04:47:31 – Types of Machine Learning
04:48:05 – Supervised Learning
04:51:26 – Unsupervised Learning
04:59:36 – Reinforcement Learning
05:11:26 – Types of Regression
06:23:45 – Logistic Regression
06:44:27 – Spam Email Classifier
07:55:55 – What is Classification
07:56:47 – Types of Classification
08:08:11 – Visualizing a Decision Tree
08:10:00 – Decision Tree Terminology
08:14:35 – Creating a Decision Tree
08:16:00 – How do we split a Tree?
08:17:55 – Calculating Entropy
08:33:56 – Confusion Matrix
08:36:00 – Decision Tree Example Hands-on
08:45:10 – Understanding Naive Bayes Classifier
09:02:44 – Clustering algorithms
09:03:38 – What is Clustering?
09:04:57 – Examples of Clustering
09:08:20 – Types of Clustering
09:12:40 – K means Clustering
09:12:04 – What us k-means Clustering?
09:15:24 – Business Application of K-Means
09:16:36 – Understanding K-means Algorithm
09:34:27 – Python Project
10:33:50 – Python Interview Questions
11:21:57 – Machine Learning with Python
11:30:27 – Data Science Project
12:11:30 – Quiz
12:11:50 – Data Science Interview Questions
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1. 24*7 Life time Access & Support
2. Flexible Class Schedule
3. Job Assistance
4. Mentors with +14 yrs
5. Industry Oriented Course ware
6. Life time free Course Upgrade
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Source. First Published on YouTube
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