access
Lifetime
content
8.5 Hours
Description
If you're at all interested in the technology that powers Siri, self-driving cars, and other AI innovations, you'll need to get comfortable with machine learning. This course features training from Silicon Valley pros with decades of experience under their belts. Even if you've never touched a line of code before, these experts will help you put Machine Learning and Python into action and harness a new level of programming power.- Access 38 lectures & 8.5 hours of content 24/7
- Get introduced to Machine Learning
- Learn from a team w/ decades of practical experience in quant trading, analytics & e-commerce
- Understand complex programming subjects w/ the help of animations
- Discover how to implement natural language processing & machine learning for text classification in Python
Instructor
Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertise at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.Important Details
- Length of time users can access this course: lifetime
- Access options: web
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: basic
Requirements
- 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.
Course Outline
- Introduction
- What this course is about (3:17)
- Jump right in : Machine learning for Spam detection
- Machine Learning: Why should you jump on the bandwagon? (16:31)
- Plunging In - Machine Learning Approaches to Spam Detection (17:01)
- Spam Detection with Machine Learning Continued (17:04)
- Get the Lay of the Land : Types of Machine Learning Problems (17:26)
- Naive Bayes Classifier
- Random Variables (19:53)
- Bayes Theorem (18:53)
- Naive Bayes Classifier (9:11)
- Naive Bayes Classifier : An example (14:18)
- K-Nearest Neighbors
- K-Nearest Neighbors (13:25)
- K-Nearest Neighbors : A few wrinkles (15:19)
- Support Vector Machines
- Support Vector Machines Introduced (8:31)
- Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick (16:40)
- Clustering as a form of Unsupervised learning
- Clustering : Introduction (19:00)
- Clustering : K-Means and DBSCAN (13:42)
- Association Detection
- Association Rules Learning (9:32)
- Dimensionality Reduction
- Dimensionality Reduction (17:39)
- Principal Component Analysis (19:18)
- Artificial Neural Networks
- Artificial Neural Networks I Perceptron introduced(via Support Vector Machines) (18:56)
- Perceptron : How it works (6:46)
- Regression as a form of supervised learning
- Regression Introduced : Linear and Logistic Regression (14:10)
- Bias Variance Trade-off (10:13)
- Natural Language Processing and Python
- Natural Language Processing with NLTK (7:26)
- Natural Language Processing with NLTK - See it in action (14:14)
- Web Scraping with BeautifulSoup (18:09)
- A Serious NLP Application : Text Auto Summarization using Python (12:00)
- Python Drill : Autosummarize News Articles I (18:33)
- Python Drill : Autosummarize News Articles II (11:28)
- Python Drill : Autosummarize News Articles III (10:23)
- NLP and Machine Learning
- Put it to work : News Article Classification using K-Nearest Neighbors (20:01)
- Put it to work : News Article Classification using Naive Bayes Classifier (19:47)
- Python Drill : Scraping News Websites (15:45)
- Python Drill : Feature Extraction with NLTK (18:51)
- Python Drill : Classification with KNN (4:15)
- Python Drill : Classification with Naive Bayes (8:08)
- Document Distance using TF-IDF (11:22)
- Put it to work : News Article Clustering with K-Means and TF-IDF (15:07)
- Python Drill : Clustering with K Means (8:32)
access
Lifetime
content
8.5 Hours
