Analytics, Machine Learning & NLP in Python

Name: Analytics, Machine Learning & NLP in Python
Brand: Loonycorn
Price: 45.00 USD
Availability: Discontinued

14.5 Hours

Deal Price$45.00

Suggested Price$49.00

You save 8%

76 Lessons (14.5h)

Introduction
What this course is about
Creating Excel and CSV Files in Python
A File is like a barrel
Autogenerating Spreadsheets with Python
Autogenerating Spreadsheets - Download and Unzip
Autogenerating Spreadsheets - Parsing CSV files
Autogenerating Spreadsheets with XLSXwriter
A Very Quick Run-Through Databases in Python
How would you implement a Bank ATM?
Things you can do with databases - I
Things you can do with databases - II
Interfacing with Databases from Python
SQLite works right out of the box
Building a database of stock movements - I
Building a database of stock movements - II
Building a database of stock movements - III
Jump right in : Machine learning for Spam detection
Machine Learning: Why should you jump on the bandwagon?
Plunging In - Machine Learning Approaches to Spam Detection
Spam Detection with Machine Learning Continued
Get the Lay of the Land : Types of Machine Learning Problems
Naive Bayes Classifier
Random Variables
Bayes Theorem
Naive Bayes Classifier
Naive Bayes Classifier : An example
K-Nearest Neighbors
K-Nearest Neighbors
K-Nearest Neighbors : A few wrinkles
Support Vector Machines
Support Vector Machines Introduced
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
Clustering as a form of Unsupervised learning
Clustering : Introduction
Clustering : K-Means and DBSCAN
Association Detection
Association Rules Learning
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis
Artificial Neural Networks
Artificial Neural Networks I Perceptron introduced(via Support Vector Machines)
Perceptron : How it works
Regression as a form of supervised learning
Regression Introduced : Linear and Logistic Regression
Bias Variance Trade-off
Natural Language Processing and Python
Natural Language Processing with NLTK
Natural Language Processing with NLTK - See it in action
Web Scraping with BeautifulSoup
A Serious NLP Application : Text Auto Summarization using Python
Python Drill : Autosummarize News Articles I
Python Drill : Autosummarize News Articles II
Python Drill : Autosummarize News Articles III
NLP and Machine Learning
Put it to work : News Article Classification using K-Nearest Neighbors
Put it to work : News Article Classification using Naive Bayes Classifier
Python Drill : Scraping News Websites
Python Drill : Feature Extraction with NLTK
Python Drill : Classification with KNN
Python Drill : Classification with Naive Bayes
Document Distance using TF-IDF
Put it to work : News Article Clustering with K-Means and TF-IDF
Python Drill : Clustering with K Means
Sentiment Analysis
A Sneak Peek at what's coming up
Sentiment Analysis - What's all the fuss about?
ML Solutions for Sentiment Analysis - the devil is in the details
Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
Regular Expressions
Regular Expressions in Python
Put it to work : Twitter Sentiment Analysis
Twitter Sentiment Analysis - Work the API
Twitter Sentiment Analysis - Regular Expressions for Preprocessing
Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet
Decision Trees
Planting the seed - What are Decision Trees?
Growing the Tree - Decision Tree Learning
Branching out - Information Gain
Decision Tree Algorithms
Titanic : Decision Trees predict Survival (Kaggle) - I
Titanic : Decision Trees predict Survival (Kaggle) - II
Titanic : Decision Trees predict Survival (Kaggle) - III
A Few Useful Things to Know About Overfitting
Overfitting - the bane of Machine Learning
Overfitting Continued
Cross Validation
Simplicity is a virtue - Regularization
The Wisdom of Crowds - Ensemble Learning
Ensemble Learning continued - Bagging, Boosting and Stacking
Random Forests
Random Forests - Much more than trees
Back on the Titanic - Cross Validation and Random Forests

DescriptionInstructorImportant DetailsRelated Products

Start Studying Machine Learning Techniques & Put Them into Action Today

LoonycornLoonycorn includes Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh. Between the four of them, they have studied at Stanford, IIM Ahmedabad, and the IITs, and have spent years (decades, actually) working in tech around the world.

Janani: seven years at Google (New York, Singapore); studied at Stanford; also worked at Flipkart & Microsoft
Vitthal: worked at Google (Singapore) & studied at Stanford; Flipkart, Credit Suisse & INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad & IIT Madras alum
Navdeep: longtime Flipkart employee & IIT Guwahati alum

This dream team strongly believes they have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way! For more details on this course and instructor, click here. This course is hosted by StackSkills, the premier eLearning destination for discovering top-shelf courses on everything from coding—to business—to fitness, and beyond!

Description

Are you familiar with self-driving cars? Speech recognition technology? These things would not be possible without the help of Machine Learning--the study of pattern recognition and prediction within the field of computer science. This course is taught by Stanford-educated, Silicon Valley experts that have decades of direct experience under their belts. They will teach you, in the simplest way possible (and with major visual techniques), to put Machine Learning and Python into action. With these skills under your belt, your programming skills will take a whole new level of power.

Get introduced to Machine Learning w/ 14.5 hours of instruction
Learn from a team w/ decades of practical experience in quant trading, analytics & e-commerce
Understand complex subjects w/ the help of animations
Use hundreds of lines of source code w/ comments to implement natural language processing & machine learning for text summarization, text classification in Python
Learn in a fun & exciting manner
Study natural language processing w/ Python
Study sentiment analysis

Specs

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Compatibility

Knowledge of Python recommended
Internet required

Terms

Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

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