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The Machine Learning and Artificial Intelligence Bundle

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2 hours
Lessons
19

Easy Natural Language Processing (NLP) in Python

Explore & Build Common Applications of Machine Learning in Industry

By Lazy Programmer | in Online Courses

Over this course you will build multiple practical systems using natural language processing (NLP), the branch of machine learning and data science that deals with text and speech. You'll start with a background on NLP before diving in, building a spam detector and a model for sentiment analysis in Python. Learning how to build these practical tools will give you an excellent window into the mechanisms that drive machine learning.

  • Access 19 lectures & 2 hours of content 24/7
  • Build a spam detector & sentiment analysis model that may be used to predict the stock market
  • Learn practical tools & techniques like the natural language toolkit library & latent semantic analysis
  • Create an article spinner from scratch that can be used as an SEO tool
Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of Python and Numpy coding is expected
  • All code for this course is available for download here, in the directory nlp_class

Compatibility

  • Internet required

Course Outline

  • Natural Language Processing - What is it used for?
    • Introduction and Outline (3:04)
    • NLP Applications (6:40)
    • Why is NLP hard? (2:30)
  • Build your own spam detector
    • Build your own spam detector - description of data (2:08)
    • Build your own spam detector - the code (6:16)
    • Other types of features (1:30)
  • Build your own sentiment analyzer
    • Description of Sentiment Analyzer (3:13)
    • Sentiment Analysis in Python (19:48)
  • NLTK Exploration
    • NLTK Exploration: POS Tagging (2:00)
    • NLTK Exploration: Stemming and Lemmatization (2:06)
    • NLTK Exploration: Named Entity Recognition (3:13)
  • Latent Semantic Analysis
    • Latent Semantic Analysis - What does it do? (2:30)
    • PCA and SVD - The underlying math behind LSA (7:59)
    • Latent Semantic Analysis in Python (10:08)
  • Write your own article spinner
    • Article Spinning Introduction (2:43)
    • Trigram Model (2:12)
    • Writing an article spinner in Python (11:33)
  • How to learn more about NLP
    • What we didn't talk about (2:45)
  • Appendix
    • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)

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Lifetime
Content
4.5 hours
Lessons
40

Unsupervised Machine Learning Hidden Markov Models in Python

Decode & Analyze Important Data Sequences & Solve Everyday Problems

By Lazy Programmer | in Online Courses

Data, in many forms, is presented in sequences: stock prices, language, credit scoring, etc. Being able to analyze them, therefore, is of invaluable importance. In this course you'll learn a machine learning algorithm - the Hidden Markov Model - to model sequences effectively. You'll also delve deeper into the many practical applications of Markov Models and Hidden Markov Models.

  • Access 40 lectures & 4.5 hours of content 24/7
  • Use gradient descent to solve for the optimal parameters of a Hidden Markov Model
  • Learn how to work w/ sequences in Theano
  • Calculate models of sickness & health
  • Analyze how people interact w/ a website using Markov Models
  • Explore Google's PageRank algorithm
  • Generate images & discuss smartphone autosuggestions using HMMs
Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of Python and Numpy coding is expected
  • All code for this course is available for download here, in the directory hmm_class

Compatibility

  • Internet required

Course Outline

  • Introduction and Outline
    • Introduction and Outline: Why would you want to use an HMM? (4:04)
    • Unsupervised or Supervised? (2:58)
  • Markov Models
    • The Markov Property (4:39)
    • Markov Models (4:50)
    • The Math of Markov Chains (5:13)
  • Markov Models: Example Problems and Applications
    • Example Problem: Sick or Healthy (3:26)
    • Example Problem: Expected number of continuously sick days (2:53)
    • Example application: SEO and Bounce Rate Optimization (8:53)
    • Example Application: Build a 2nd-order language model and generate phrases (13:06)
    • Example Application: Google’s PageRank algorithm (5:04)
  • Hidden Markov Models for Discrete Observations
    • From Markov Models to Hidden Markov Models (6:02)
    • HMMs are Doubly Embedded (1:59)
    • How can we choose the number of hidden states? (4:22)
    • The Forward-Backward Algorithm (4:27)
    • Visual Intuition for the Forward Algorithm (3:32)
    • The Viterbi Algorithm (2:57)
    • Visual Intuition for the Viterbi Algorithm (3:16)
    • The Baum-Welch Algorithm (2:38)
    • Baum-Welch Explanation and Intuition (6:34)
    • Baum-Welch Updates for Multiple Observations (4:53)
    • Discrete HMM in Code (20:33)
    • The underflow problem and how to solve it (5:05)
    • Discrete HMM Updates in Code with Scaling (11:53)
    • Scaled Viterbi Algorithm in Log Space (3:38)
    • Gradient Descent Tutorial (4:30)
    • Theano Scan Tutorial (12:40)
    • Discrete HMM in Theano (11:42)
  • HMMs for Continuous Observations
    • Gaussian Mixture Models with Hidden Markov Models (4:12)
    • Generating Data from a Real-Valued HMM (6:35)
    • Continuous-Observation HMM in Code (part 1) (18:38)
    • Continuous-Observation HMM in Code (part 2) (5:12)
    • Continuous HMM in Theano (16:32)
  • HMMs for Classification
    • Generative vs. Discriminative Classifiers (2:30)
    • HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe) (10:36)
  • Bonus Example: Parts-of-Speech Tagging
    • Parts-of-Speech Tagging Concepts (5:00)
    • POS Tagging with an HMM (5:58)
  • Appendix
    • Review of Gaussian Mixture Models (3:04)
    • Theano Tutorial (7:47)
    • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)

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Access
Lifetime
Content
1.5 hours
Lessons
22

Cluster Analysis and Unsupervised Machine Learning in Python

Learn the Core Techniques to Clustering, Becoming a Valuable Business Asset in the Process

By Lazy Programmer | in Online Courses

Cluster analysis is a staple of unsupervised machine learning and data science, used extensively for data mining and big data because it automatically finds patterns in data. The real-world applications for this process, then, are vital, making people who can implement cluster analyses a hot commodity in the business world. In this course, you'll become a master of clustering.

  • Access 22 lectures & 1.5 hours of content 24/7
  • Discuss k-means clustering & hierarchical clustering
  • Explore Gaussian mixture models & kernel density estimation
  • Create your own labels on clusters
Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of Python and Numpy coding is expected
  • All code for this course is available for download here, in the directory unsupervised_class

Compatibility

  • Internet required

Course Outline

  • Introduction to Unsupervised Learning
    • Introduction and Outline (2:22)
    • What is unsupervised learning used for? (4:56)
  • K-Means Clustering
    • Visual Walkthrough of the K-Means Clustering Algorithm (2:58)
    • Soft K-Means (2:20)
    • The K-Means Objective Function (1:39)
    • Soft K-Means in Python Code (10:03)
    • Visualizing Each Step of K-Means (2:18)
    • Examples of where K-Means can fail (7:32)
    • Disadvantages of K-Means Clustering (2:13)
    • How to Evaluate a Clustering (Purity, Davies-Bouldin Index) (6:33)
    • Using K-Means on Real Data: MNIST (5:00)
  • Hierarchical Clustering
    • Visual Walkthrough of Agglomerative Hierarchical Clustering (2:35)
    • Agglomerative Clustering Options (3:39)
    • Using Hierarchical Clustering in Python and Interpreting the Dendrogram (4:38)
  • Gaussian Mixture Models (GMMs)
    • Description of the Gaussian Mixture Model and How to Train a GMM (3:04)
    • Comparison between GMM and K-Means (1:44)
    • Write a Gaussian Mixture Model in Python Code (9:59)
    • Practical Issues with GMM / Singular Covariance (2:56)
    • Kernel Density Estimation (2:11)
    • Expectation-Maximization (2:01)
    • Future Unsupervised Learning Algorithms You Will Learn (1:01)
  • Appendix
    • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)

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Access
Lifetime
Content
3 hours
Lessons
33

Data Science: Supervised Machine Learning in Python

Get Started with the Basics of Machine Learning - The Next Great Tech Frontier

By Lazy Programmer | in Online Courses

Machine learning is entering the scientific mainstream faster than ever, being utilized to do tasks as diverse as analyzing medical images, driving cars automatically, and everything in between. Google has even announced that machine learning is one of their top focuses of innovation, making it an invaluable subject to begin studying now. In this course, you'll dive into the basics of machine learning, the theory behind it, and its many practical applications so you can be on the forefront of a new technological wave.

  • Access 33 lectures & 3 hours of content 24/7
  • Discuss the K-Nearest Neighbor algorithm, its concepts, & implement it in code
  • Explore the Naive Bayes Classifier & General Bayes Classifier
  • Learn about Decision Trees
  • Dive into the Perceptron algorithm, the ancestor of neural networks & deep learning
  • Understand more practical machine learning topics like hyperparameters, cross-validation, feature extraction, feature selection, & multiclass classification
Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of Python and Numpy coding is expected
  • All code for this course is available for download here, in the directory unsupervised_class

Compatibility

  • Internet required

Course Outline

  • Introduction and Review
    • Introduction and Outline (4:08)
    • Review of Important Concepts (3:27)
    • Where to get the Code and Data (2:09)
  • K-Nearest Neighbor
    • K-Nearest Neighbor Concepts (5:02)
    • KNN in Code with MNIST (7:41)
    • When KNN Can Fail (3:49)
    • KNN for the XOR Problem (2:05)
    • KNN for the Donut Problem (2:36)
  • Naive Bayes and Bayes Classifiers
    • Naive Bayes (9:00)
    • Naive Bayes Handwritten Example (3:28)
    • Naive Bayes in Code with MNIST (5:56)
    • Non-Naive Bayes (4:04)
    • Bayes Classifier in Code with MNIST (2:03)
    • Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) (6:07)
    • Generative vs Discriminative Models (2:47)
  • Decision Trees
    • Decision Tree Basics (4:58)
    • Information Entropy (3:58)
    • Maximizing Information Gain (7:58)
    • Choosing the Best Split (4:02)
    • Decision Tree in Code (13:10)
  • Perceptrons
    • Perceptron Concepts (7:07)
    • Perceptron in Code (5:26)
    • Perceptron for MNIST and XOR (3:16)
    • Perceptron Loss Function (4:01)
  • Practical Machine Learning
    • Hyperparameters and Cross-Validation (4:15)
    • Feature Extraction and Feature Selection (3:54)
    • Comparison to Deep Learning (4:40)
    • Multiclass Classification (3:20)
    • Sci-Kit Learn (9:02)
    • Regression with Sci-Kit Learn is Easy (5:51)
  • Building a Machine Learning Web Service
    • Building a Machine Learning Web Service Concepts (4:11)
    • Building a Machine Learning Web Service Code (6:12)
  • Conclusion
    • What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest) (2:50)
  • Appendix
    • How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn (17:22)

View Full Curriculum



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