**From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 19h 15m | 7.11 GB

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today. Let’s parse that. The course is down-to-earth: it makes everything as simple as possible - but not simpler. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff. You can put ML to work today: If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is. The course is very visual: most of the techniques are explained with the help of animations to help you understand better. This course is practical as well: There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

What You Will Learn

- Identify situations that call for the use of Machine Learning
- Understand which type of Machine learning problem you are solving and choose the appropriate solution
- Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python

**Table of Contents**

01 You, This Course and Us

02 A sneak peek at what's coming up

03 Solving problems with computers

04 Machine Learning - Why should you jump on the bandwagon

05 Plunging In - Machine Learning Approaches to Spam Detection

06 Spam Detection with Machine Learning Continued

07 Get the Lay of the Land - Types of Machine Learning Problems

08 Solving Classification Problems

09 Random Variables

10 Bayes Theorem

11 Naive Bayes Classifier

12 Naive Bayes Classifier - An example

13 K-Nearest Neighbours

14 K-Nearest Neighbours - A few wrinkles

15 Support Vector Machines Introduced

16 Support Vector Machines - Maximum Margin Hyperplane and Kernel Trick

17 Artificial Neural Networks - Perceptrons Introduced

18 Clustering - Introduction

19 Clustering - K-Means and DBSCAN

20 Association Rules Learning

21 Dimensionality Reduction

22 Principal Component Analysis

23 Regression Introduced - Linear and Logistic Regression

24 Bias Variance Trade-off

25 Applying ML to Natural Language Processing

26 Installing Python - Anaconda and Pip

27 Natural Language Processing with NLTK

28 Natural Language Processing with NLTK - See it in action

29 Web Scraping with BeautifulSoup

30 A Serious NLP Application - Text Auto Summarization using Python

31 Python Drill - Autosummarize News Articles I

32 Python Drill - Autosummarize News Articles II

33 Python Drill - Autosummarize News Articles III

34 Put it to work - News Article Classification using K-Nearest Neighbors

35 Put it to work - News Article Classification using Naive Bayes Classifier

36 Python Drill - Scraping News Websites

37 Python Drill - Feature Extraction with NLTK

38 Python Drill - Classification with KNN

39 Python Drill - Classification with Naive Bayes

40 Document Distance using TF-IDF

41 Put it to work - News Article Clustering with K-Means and TF-IDF

42 Python Drill - Clustering with K Means

43 Solve Sentiment Analysis using Machine Learning

44 Sentiment Analysis - What's all the fuss about

45 ML Solutions for Sentiment Analysis - the devil is in the details

46 Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)

47 Regular Expressions

48 Regular Expressions in Python

49 Put it to work - Twitter Sentiment Analysis

50 Twitter Sentiment Analysis - Work the API

51 Twitter Sentiment Analysis - Regular Expressions for Preprocessing

52 Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet

53 Using Tree Based Models for Classification

54 Planting the seed - What are Decision Trees

55 Growing the Tree - Decision Tree Learning

56 Branching out - Information Gain

57 Decision Tree Algorithms

58 Titanic - Decision Trees predict Survival (Kaggle) – I

59 Titanic - Decision Trees predict Survival (Kaggle) - II

60 Titanic - Decision Trees predict Survival (Kaggle) – III

61 Overfitting - the bane of Machine Learning

62 Overfitting Continued

63 Cross Validation

64 Simplicity is a virtue – Regularization

65 The Wisdom of Crowds - Ensemble Learning

66 Ensemble Learning continued - Bagging, Boosting and Stacking

67 Random Forests - Much more than trees

68 Back on the Titanic - Cross Validation and Random Forests

69 Solving Recommendation Problems

70 What do Amazon and Netflix have in common

71 Recommendation Engines - A look inside

72 What are you made of - Content-Based Filtering

73 With a little help from friends - Collaborative Filtering

74 A Neighbourhood Model for Collaborative Filtering

75 Top Picks for You! - Recommendations with Neighbourhood Models

76 Discover the Underlying Truth - Latent Factor Collaborative Filtering

77 Latent Factor Collaborative Filtering contd.mp4

78 Gray Sheep and Shillings - Challenges with Collaborative Filtering

79 The Apriori Algorithm for Association Rules

80 Back to Basics - Numpy in Python

81 Back to Basics - Numpy and Scipy in Python

82 Movielens and Pandas

83 Code Along - What's my favourite movie - Data Analysis with Pandas

84 Code Along - Movie Recommendation with Nearest Neighbour CF

85 Code Along - Top Movie Picks (Nearest Neighbour CF)

86 Code Along - Movie Recommendations with Matrix Factorization

87 Code Along - Association Rules with the Apriori Algorithm

88 Computer Vision - An Introduction

89 Perceptron Revisited

90 Deep Learning Networks Introduced

91 Code Along - Handwritten Digit Recognition -I

92 Code Along - Handwritten Digit Recognition - II

93 Code Along - Handwritten Digit Recognition – III

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