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Data Science With Artificial Intelligence Specialization Course
Module 1 - Python Prerequisites for Data Science
Lecture 1 (59:36)
Lecture 2 (34:18)
Lecture 3 (58:06)
Lecture 4 (56:26)
Lecture 5 (75:54)
Module 2 - Data Science Introduction
Lecture 1 : Introduction to Data Science (89:02)
Lecture 2 : Introduction to Machine Learning (76:30)
Lecture 3: Difference between Statistics and Machine Learning (56:15)
Module 3 - Natural Language Processing
Lecture 1 - Introduction to NLP with Machine Learning and Neural networks (71:30)
Lecture 2 - Introduction to NLP with Deep Learning (38:55)
Lecture 3 - Text data preprocessing steps such as Sentence Tokenization, Word Punctuation Tokenization, Word Stemming , Word Lemmatization, Removal of Stop Words (63:56)
Lecture 4 - Extracting numeric Features from Text (63:11)
Lecture 5 - TFIDF feature and how it works (51:08)
Lecture 6 - TFIDF feature extraction with Python (61:28)
Lecture 7 - Text Classification Using Logistic Regression with Gradient Descent Algorithm in Python (78:28)
Lecture 8 - Text Classification With Neural Networks (64:30)
Lecture 9 - Text Classification With Neural Networks Code Explained (106:29)
Lecture 10 - Text Classification With Deep Neural Networks (72:26)
Lecture 11 - Text Classification With Deep Learning Python code excecution (35:03)
Lecture 12 - Multinomial Text Classification (Step1 to Step9) Part-1 (48:13)
Lecture 13 - Multinomial Text Classification Part 2 (43:43)
Lecture 14 - Multinomial Text Classification Part-3 with Deep Learning - A full code with Python (61:16)
Lecture 15 - Other Machine Learning Models for Text Classification Part 1 (43:14)
Lecture 16 - Other Machine Learning Models for Text Classification Part 2 - Python Scikit Learn code for NaiveBayes , Decision Tree, Random Forest, Support Vector Machines, Introduction to Non Linearity in Classifications (44:11)
Lecture 17 - Unsupervised Learning on Text Data with K-Means (68:55)
Lecture 16 - Other Machine Learning Models for Text Classification Part 2 - Python Scikit Learn code for NaiveBayes , Decision Tree, Random Forest, Support Vector Machines, Introduction to Non Linearity in Classifications
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