Lecture 1 - Python Prerequisites for Data Science | Data Science with

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[Demo] Python Essentials for Data science and Artificial Intelligence

Session 1 - Introduction (55:57)
Session 2 - Introduction to python and libraries (38:58)
Session 3 - Python libraries introduction (57:56)
Session 4 - Python (32:42)
Session 5 - Python Basics for Machine Learning (36:06)

[Demo] Apache Spark

Spark Lecture 1 - Introduction (65:33)
Spark Lecture 2 - spark vs other hadoop ecosystems comparisons (30:52)
Spark Lecture 3- Introduction to spark components part 1 (48:37)
Spark Lecture 4 - Spark components part 2 (47:44)
Spark Lecture 5 - Introduction to Spark Streaming (38:09)

[Demo] Data Science With Artificial Intelligence

Lecture 1 - Python Prerequisites for Data Science (52:14)
Lecture 2 - Python Prerequisites for Data Science (31:18)

[Demo] Machine Learning

Lecture 1 - Machine Learning introduction part 1 - difference Between_2 (69:38)
Lecture 2 - What is Machine Learning - Introduction to Supervised (48:22)
Lecture 3 - M L Introduction to Reinforcement learning . how traffic board (39:17)
Lecture 4 - M L 3 types of data sets used in Machine Learning . And 3 approaches to create Train , Validation, test data sets as part of data preperation (39:00)
Lecture 5 - M L Life cycle part 1 --- introduction to data extractions . And more details on online, batch, data streaming systems. (48:46)

[Demo] Natural Language Processing (NLP)

Lecture 1- Importance of NLP in Machine Learning (56:39)
Lecture 2 - NLP different preprocessing activities of text (44:59)
Lecture 3 - NLP feature extractions (42:11)
Lecture 4 - NLP TFIDF (38:48)

[Demo] Deep Learning

Lesson 1 - Deep Learning (39:24)
Lesson 2 - Deep Learning (9:17)
Lesson 4 - Deep Learning (its audio, video will be posted soon) (38:48)

[Demo] Reinforcement Learning

Lesson 1 - RL 1 - Reinforcement Learning (52:17)
Lesson 2 - RL 2 - Reinforcement Learning (57:02)
Lesson 3 - RL 3 - Reinforcement Learning (51:34)

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Lecture 1 - Python Prerequisites for Data Science

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