The Machine Learning and Artificial Intelligence Bundle for $39
February 14, 2017 / by Marco / Categories : Business, deals, design, entrepreneur, website
KEY FEATURES
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.
PRODUCT SPECS
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
THE EXPERT
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.
KEY FEATURES
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.
PRODUCT SPECS
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
THE EXPERT
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.
KEY FEATURES
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.
PRODUCT SPECS
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
THE EXPERT
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.
KEY FEATURES
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.
PRODUCT SPECS
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
THE EXPERT
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.
Marco / admin / 30 Nov
Writing day to day ramblings about making money, business, technology, sharing awesome deals and everything else that I know I'll forget. Follow my personal blog https://marcotran.com.au
I've recently also turned Vegan and started this website Veggie Meals - check it out
"When technology speaks for itself, that is art" - MT
Affiliate Compensated: there are some articles with links to products or services that I may receive a commission.
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