Get $1 credit for every $25 spent!

The Complete Machine Learning Bundle

Ending In:
Add to Cart - $39
Add to Cart ($39)
$780
95% off
wishlist
(1)
Courses
10
Lessons
406
Enrolled
8,794

What's Included

Video icon Video Overview

Product Details

Access
Lifetime
Content
11 hours
Lessons
64

Quant Trading Using Machine Learning

Play the Markets Like a Pro After 11 Hours of Integrating Machine Learning into Your Investment Strategies

By Loonycorn | in Online Courses

Financial markets are fickle beasts that can be extremely difficult to navigate for the average investor. This course will introduce you to machine learning, a field of study that gives computers the ability to learn without being explicitly programmed, while teaching you how to apply these techniques to quantitative trading. Using Python libraries, you'll discover how to build sophisticated financial models that will better inform your investing decisions. Ideally, this one will buy itself back and then some!

  • Access 64 lectures & 11 hours of content 24/7
  • Get a crash course in quantitative trading from stocks & indices to momentum investing & backtesting
  • Discover machine learning principles like decision trees, ensemble learning, random forests & more
  • Set up a historical price database in MySQL using Python
  • Learn Python libraries like Pandas, Scikit-Learn, XGBoost & Hyperopt
  • Access source code any time as a continuing resource
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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. For more details on the course and instructor, click here.

Details & Requirements

  • Length of time users can access this course: lifetime access
  • 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 working knowledge of Python would be helpful

Compatibility

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us (2:00)
  • Setting up your Development Environment
    • Installing Anaconda for Python (9:00)
    • Installing Pycharm - a Python IDE (3:55)
    • MySQL Introduced and Installed (Mac OS X) (7:03)
    • MySQL Server Configuration and MySQL Workbench (Mac OS X) (17:32)
    • MySQL Installation (Windows) (6:31)
  • Introduction to Quant Trading
    • Financial Markets - Who are the players? (16:38)
    • What is a Stock Market Index? (3:13)
    • The Mechanics of Trading - Long vs Short positions (11:56)
    • Futures Contracts (14:25)
    • Evaluating Trading Strategies - Risk And Return (16:22)
    • Evaluating Trading Strategies - The Sharpe Ratio (10:16)
    • The 2 Step process - Modeling and Backtesting (3:48)
  • Developing Trading Strategies in Excel
    • Are markets efficient or inefficient? (10:27)
    • Momentum Investing (11:31)
    • Mean Reversion (6:30)
    • Developing a Trading Strategy in Excel (11:42)
  • Setting up a Price Database
    • Programmatically Downloading Historical Price Data (6:24)
    • CodeAlong - Dowloading Price data from Yahoo Finance (14:39)
    • CodeAlong - Downloading a URL in Python (7:38)
    • CodeAlong - Downloading Price data from the NSE (13:55)
    • CodeAlong - Unzip and process the downloaded files (5:22)
    • CodeAlong - Download Historical Data for 10 years (6:26)
    • Inserting the Downloaded files into a Database (10:10)
    • CodeAlong - Bulk loading downloaded files into MySQL tables (15:12)
    • Data Preparation (4:16)
    • CodeAlong - Data Preparation (12:43)
    • Adjusting for Corporate Actions (8:41)
    • CodeAlong - Adjusting for Corporate Actions 1 (15:29)
    • CodeAlong - Adjusting for Corporate Actions 2 (8:47)
    • CodeAlong - Inserting Index prices into MySQL (5:40)
    • CodeAlong = Constructing a Calendar Features table in MySQL (6:53)
  • Decision Trees, Ensemble Learning and Random Forests
    • Planting the seed - What are Decision Trees? (17:00)
    • Growing the Tree - Decision Tree Learning (18:03)
    • Branching out - Information Gain (18:51)
    • Decision Tree Algorithms (7:50)
    • Overfitting - The Bane of Machine Learning (19:03)
    • Overfitting Continued (1:42)
    • Cross Validation (18:55)
    • Regularization (7:18)
    • The Wisdom Of Crowds - Ensemble Learning (16:39)
    • Ensemble Learning continued - Bagging, Boosting and Stacking (18:03)
    • Random Forests - Much more than trees (12:28)
  • A Trading Strategy as Machine Learning Classification
    • Defining the problem - Machine Learning Classification (15:51)
  • Feature Engineering
    • Know the basics - A Pandas tutorial (11:42)
    • CodeAlong - Fetching Data from MySQL (18:34)
    • CodeAlong - Constructing some simple features (7:27)
    • CodeAlong - Constructing a Momentum Feature (8:42)
    • CodeAlong - Constructing a Jump Feature (5:52)
    • CodeAlong - Assigning Labels (3:12)
    • CodeAlong - Putting it all together (18:08)
    • CodeAlong - Include support features from other tickers (6:34)
  • Engineering a Complex Feature - A Categorical Variable with Past Trends
    • Engineering a Categorical Variable (3:49)
    • CodeAlong - Engineering a Categorical Variable (6:46)
  • Building a Machine Learning Classifier in Python
    • Introducing Scikit-Learn (3:33)
    • Introducing RandomForestClassifier (9:25)
    • Training and Testing a Machine Learning Classifier (15:01)
    • Compare Results from different Strategies (5:44)
    • Using Class probabilities for predictions (3:11)
  • Nearest Neighbors Classifier
    • A Nearest Neighbors Classifier (6:49)
    • CodeAlong - A nearest neighbors Classifier (4:16)
  • Gradient Boosted Trees
    • What are Gradient Boosted Trees? (12:38)
    • Introducing XGBoost - A python library for GBT (11:51)
    • CodeAlong - Parameter Tuning for Gradient Boosted Classifiers (9:21)

View Full Curriculum


Access
Lifetime
Content
9 hours
Lessons
82

Learn By Example: Statistics and Data Science in R

Use Real-Life Examples & Case Studies to Understand the R Programming Language

By Loonycorn | in Online Courses

R is a programming language and software environment for statistical computing and graphics that is widely used among statisticians and data miners for data analysis. In this course, you'll get a thorough run-through of how R works and how it's applied to data science. Before you know it, you'll be crunching numbers like a pro, and be better qualified for many lucrative careers.

  • Access 82 lectures & 9 hours of content 24/7
  • Cover basic statistical principles like mean, median, range, etc.
  • Learn theoretical aspects of statistical concepts
  • Discover datatypes & data structures in R, vectors, arrays, matrices & more
  • Understand Linear Regression
  • Visualize data in R using a variety of charts & graphs
  • Delve into descriptive & inferential statistics
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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.

Details & Requirements

  • Length of time users can access this course: lifetime access
  • 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

Compatibility

  • Internet required

Course Outline

  • Introduction
    • You, This course and Us (2:32)
    • Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data (12:58)
    • R and RStudio installed (5:10)
  • The 10 second answer : Descriptive Statistics
    • Descriptive Statistics : Mean, Median, Mode (10:07)
    • Our first foray into R : Frequency Distributions (6:07)
    • Draw your first plot : A Histogram (3:11)
    • Computing Mean, Median, Mode in R (2:21)
    • What is IQR (Inter-quartile Range)? (8:08)
    • Box and Whisker Plots (3:11)
    • The Standard Deviation (10:24)
    • Computing IQR and Standard Deviation in R (6:06)
  • Inferential Statistics
    • Drawing inferences from data (3:25)
    • Random Variables are ubiquitous (16:54)
    • The Normal Probability Distribution (9:31)
    • Sampling is like fishing (6:14)
    • Sample Statistics and Sampling Distributions (9:25)
  • Case studies in Inferential Statistics
    • Case Study 1 : Football Players (Estimating Population Mean from a Sample) (6:49)
    • Case Study 2 : Election Polling (Estimating Population Proportion from a Sample) (7:51)
    • Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean) (13:53)
    • Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion) (9:49)
    • Case Study 5: A/B Testing (Comparing the means of two populations) (17:18)
    • Case Study 6: Customer Analysis (Comparing the proportions of 2 populations) (11:50)
  • Diving into R
    • Harnessing the power of R (7:26)
    • Assigning Variables (8:48)
    • Printing an output (13:03)
    • Numbers are of type numeric (5:25)
    • Characters and Dates (7:30)
    • Logicals (3:24)
  • Vectors
    • Data Structures are the building blocks of R (8:24)
    • Creating a Vector (2:23)
    • The Mode of a Vector (4:18)
    • Vectors are Atomic (2:24)
    • Doing something with each element of a Vector (3:09)
    • Aggregating Vectors (1:28)
    • Operations between vectors of the same length (5:39)
    • Operations between vectors of different length (5:30)
    • Generating Sequences (6:25)
    • Using conditions with Vectors (2:04)
    • Find the lengths of multiple strings using Vectors (2:22)
    • Generate a complex sequence (using recycling) (2:49)
    • Vector Indexing (using numbers) (6:56)
    • Vector Indexing (using conditions) (6:18)
    • Vector Indexing (using names) (2:27)
  • Arrays
    • Creating an Array (11:36)
    • Indexing an Array (7:38)
    • Operations between 2 Arrays (2:09)
    • Operations between an Array and a Vector (2:45)
    • Outer Products (6:23)
  • Matrices
    • A Matrix is a 2-Dimensional Array (7:59)
    • Creating a Matrix (2:00)
    • Matrix Multiplication (2:49)
    • Merging Matrices (2:06)
    • Solving a set of linear equations (2:06)
  • Factors
    • What is a factor? (6:48)
    • Find the distinct values in a dataset (using factors) (1:28)
    • Replace the levels of a factor (2:18)
    • Aggregate factors with table() (1:40)
    • Aggregate factors with tapply() (5:07)
  • Lists and Data Frames
    • Introducing Lists (5:11)
    • Introducing Data Frames (4:28)
    • Reading Data from files (4:52)
    • Indexing a Data Frame (5:38)
    • Aggregating and Sorting a Data Frame (6:28)
    • Merging Data Frames (3:30)
  • Regression quantifies relationships between variables
    • Introducing Regression (12:22)
    • What is Linear Regression? (16:06)
    • A Regression Case Study : The Capital Asset Pricing Model (CAPM) (6:34)
  • Linear Regression in Excel
    • Linear Regression in Excel : Preparing the data (9:53)
    • Linear Regression in Excel : Using LINEST() (16:48)
  • Linear Regression in R
    • Linear Regression in R : Preparing the data (13:05)
    • Linear Regression in R : lm() and summary() (16:04)
    • Multiple Linear Regression (12:16)
    • Adding Categorical Variables to a linear model (7:44)
    • Robust Regression in R : rlm() (3:14)
    • Parsing Regression Diagnostic Plots (12:10)
  • Data Visualization in R
    • Data Visualization (6:23)
    • The plot() function in R (3:42)
    • Control color palettes with RColorbrewer (4:15)
    • Drawing barplots (5:25)
    • Drawing a heatmap (2:52)
    • Drawing a Scatterplot Matrix (3:41)
    • Plot a line chart with ggplot2 (8:19)

View Full Curriculum


Access
Lifetime
Content
13 hours
Lessons
71

Learn By Example: Hadoop & MapReduce for Big Data Problems

Discover Mass Data Processing Methods by Using the Leading Data Frameworks

By Loonycorn | in Online Courses

Big Data sounds pretty daunting doesn't it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you'll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.

  • Access 71 lectures & 13 hours of content 24/7
  • Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud
  • Understand HDFS, MapReduce & YARN & their interaction
  • Use MapReduce to recommend friends in a social network, build search engines & generate bigrams
  • Chain multiple MapReduce jobs together
  • Write your own customized partitioner
  • Learn to globally sort a large amount of data by sampling input files
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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.

Details & Requirements

  • Length of time users can access this course: lifetime access
  • 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

Compatibility

  • Internet required

Course Outline

  • Introduction
    • You, this course and Us (1:52)
  • Why is Big Data a Big Deal
    • The Big Data Paradigm (14:20)
    • Serial vs Distributed Computing (8:37)
    • What is Hadoop? (7:25)
    • HDFS or the Hadoop Distributed File System (11:01)
    • MapReduce Introduced (11:39)
    • YARN or Yet Another Resource Negotiator (4:00)
  • Installing Hadoop in a Local Environment
    • Hadoop Install Modes (8:32)
    • Setup a Virtual Linux Instance (For Windows users) (15:31)
    • Hadoop Standalone mode Install (9:33)
    • Hadoop Pseudo-Distributed mode Install (14:25)
  • The MapReduce "Hello World"
    • The basic philosophy underlying MapReduce (8:49)
    • MapReduce - Visualized And Explained (9:03)
    • MapReduce - Digging a little deeper at every step (10:21)
    • "Hello World" in MapReduce (10:29)
    • The Mapper (9:48)
    • The Reducer (7:46)
    • The Job (12:28)
  • Run a MapReduce Job
    • Get comfortable with HDFS (10:59)
    • Run your first MapReduce Job (14:30)
  • Juicing your MapReduce - Combiners, Shuffle and Sort and The Streaming API
    • Parallelize the reduce phase - use the Combiner (14:40)
    • Not all Reducers are Combiners (14:31)
    • How many mappers and reducers does your MapReduce have? (8:23)
    • Parallelizing reduce using Shuffle And Sort (14:55)
    • MapReduce is not limited to the Java language - Introducing the Streaming API (5:05)
    • Python for MapReduce (12:19)
  • HDFS and Yarn
    • HDFS - Protecting against data loss using replication (15:32)
    • HDFS - Name nodes and why they're critical (6:48)
    • HDFS - Checkpointing to backup name node information (11:10)
    • Yarn - Basic components (8:33)
    • Yarn - Submitting a job to Yarn (13:10)
    • Yarn - Plug in scheduling policies (14:21)
    • Yarn - Configure the scheduler (12:26)
  • Setting up a Hadoop Cluster
    • Manually configuring a Hadoop cluster (Linux VMs) (13:50)
    • Getting started with Amazon Web Servicies (6:25)
    • Start a Hadoop Cluster with Cloudera Manager on AWS (13:04)
  • MapReduce Customizations For Finer Grained Control
    • Setting up your MapReduce to accept command line arguments (13:47)
    • The Tool, ToolRunner and GenericOptionsParser (12:36)
    • Configuring properties of the Job object (10:41)
    • Customizing the Partitioner, Sort Comparator, and Group Comparator (15:16)
  • The Inverted Index, Custom Data Types for Keys, Bigram Counts and Unit Tests!
    • The heart of search engines - The Inverted Index (14:41)
    • Generating the inverted index using MapReduce (10:25)
    • Custom data types for keys - The Writable Interface (10:23)
    • Represent a Bigram using a WritableComparable (13:13)
    • MapReduce to count the Bigrams in input text (8:26)
    • Test your MapReduce job using MRUnit (13:41)
  • Input and Output Formats and Customized Partitioning
    • Introducing the File Input Format (12:48)
    • Text And Sequence File Formats (10:21)
    • Data partitioning using a custom partitioner (7:11)
    • Make the custom partitioner real in code (10:25)
    • Total Order Partitioning (10:10)
    • Input Sampling, Distribution, Partitioning and configuring these (9:04)
    • Secondary Sort (14:34)
  • Recommendation Systems using Collaborative Filtering
    • Introduction to Collaborative Filtering (7:25)
    • Friend recommendations using chained MR jobs (17:15)
    • Get common friends for every pair of users - the first MapReduce (14:50)
    • Top 10 friend recommendation for every user - the second MapReduce (13:46)
  • Hadoop as a Database
    • Structured data in Hadoop (14:08)
    • Running an SQL Select with MapReduce (15:31)
    • Running an SQL Group By with MapReduce (14:02)
    • A MapReduce Join - The Map Side (14:20)
    • A MapReduce Join - The Reduce Side (13:08)
    • A MapReduce Join - Sorting and Partitioning (8:49)
    • A MapReduce Join - Putting it all together (13:46)
  • K-Means Clustering
    • What is K-Means Clustering? (14:04)
    • A MapReduce job for K-Means Clustering (16:33)
    • K-Means Clustering - Measuring the distance between points (13:52)
    • K-Means Clustering - Custom Writables for Input/Output (8:26)
    • K-Means Clustering - Configuring the Job (10:50)
    • K-Means Clustering - The Mapper and Reducer (11:23)
    • K-Means Clustering : The Iterative MapReduce Job (3:40)

View Full Curriculum


Access
Lifetime
Content
7 hours
Lessons
35

Byte Size Chunks: Java Object-Oriented Programming & Design

Conquer Java with Just 7 Hours of Premium Instruction

By Loonycorn | in Online Courses

Java seems an appropriate name for a language that seems so dense, you may need a cuppa joe after 10 minutes of self-study. Luckily, you can learn all you need to know in this short course. You'll scale the behemoth that is object-oriented programming, mastering classes, objects, and more to conquer a language that powers everything from online games to chat platforms.

  • Learn Java inside & out w/ 35 lectures & 7 hours of content
  • Master object-oriented (OO) programming w/ classes, objects & more
  • Understand the mechanics of OO: access modifiers, dynamic dispatch, etc.
  • Dive into the underlying principles of OO: encapsulation, abstraction & polymorphism
  • Comprehend how information is organized w/ packages & jars
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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. For more details on the course and instructor, click here.

Details & Requirements

  • Length of time users can access this course: lifetime access
  • 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 basic knowledge of Java is suggested

Compatibility

  • Internet required

Course Outline

  • Start Here
    • Introduction (2:11)
    • Installing Java and Setting up an IDE (12:43)
  • An Object-Oriented State of Mind
    • Objects are puppies
    • Classes and Objects: An Object-Oriented State of Mind
    • Static Variables and Methods
    • Access Modifiers
    • Classes and Objects: A Simple Example I
    • Classes and Objects: A Simple Example II
    • Is-A Inheritance - setting up a class hierarchy
    • Is-A Inheritance - parent class, child class relationship
    • Runtime polymorphism
    • The Object Base Class
    • Interface: Introduction
    • Interfaces vs Abstract Base Class
    • Interfaces in Detail
    • Interface Default Methods: Avoid Backward Compatibility Nightmares
    • Interfaces and Inheritance in Action
  • Java Language Constructs: The Best Thing Since Sliced Bread
    • Exceptions
    • The Immutability of Strings
    • Object Identity: == and .equals()
    • Generics: Type Safety and Code Re-use
    • Collections: Containers for all purposes
    • Generic Containers - much cooler
    • Inner Classes: Horses for Courses - Static vs Non-Static
    • Inner Classes: Horses for Courses - Anonymous and Local
  • A serious drill with lots of code:-)
    • A File is like a Barrel
    • A Serious Java Application: Our First
    • A Serious Java Application: Parsing Stock Ticker Data - I
    • A Serious Java Application: Parsing Stock Ticker Data - II
    • A Serious Java Application: Parsing Stock Ticker Data - III
    • A Serious Java Application: Parsing Stock Ticker Data - IV
    • A Serious Java Application: Parsing Stock Ticker Data - V
  • Packages and Jars
    • Jars: Not As Boring as They Might Seem
    • Packages
    • Packages and Jars in Action
  • Some Object-Oriented Design Principles
    • Design Principle #1: Rely on Interfaces, not Implementations
    • Design Principle #2: The Open/Closed Principle
    • Design Principle #3: The Principle of Least Knowledge
    • Design Principle #4,5: Dependency Inversion and the Hollywood Principle

View Full Curriculum


Access
Lifetime
Content
8.5 hours
Lessons
38

An Introduction to Machine Learning & NLP in Python

Start Studying Machine Learning Techniques & Put Them into Action Today

By Loonycorn | in Online Courses

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
  • Study natural language processing & sentiment analysis w/ Python
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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. For more details on the course and instructor, click here.

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, but some knowledge of Python is suggested

Compatibility

  • Internet required

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)

View Full Curriculum


Access
Lifetime
Content
4 hours
Lessons
14

Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)

Use Python & The Twitter API to Build Your Own Sentiment Analyzer

By Loonycorn | in Online Courses

Sentiment Analysis or Opinion Mining is a field of Neuro-linguistic Programming (NLP) that aims to extract subjective information like positive/negative, like/dislike, emotional reactions, and the like. It's an essential component to Machine Learning as it provides valuable training data to a machine. Over this course, you'll learn real examples why Sentiment Analysis is important and how to approach specific problems using Sentiment Analysis.

  • Access 19 lectures & 4 hours of content 24/7
  • Learn Rule-Based & Machine Learning-Based approaches to solving Sentiment Analysis problems
  • Understand Sentiment Lexicons & Regular Expressions
  • Design & implement a Sentiment Analysis measurement system in Python
  • Grasp the underlying Sentiment Analysis theory & its relation to binary classification
  • Identify use-cases for Sentiment Analysis
  • Perform a real Twitter Sentiment Analysis
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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. For more details on the course and instructor, click here.

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, but some experience with Python is suggested

Compatibility

  • Internet required

Course Outline

  • What Are You Feeling Like?
    • A Sneak Peek at what's coming up (2:36)
    • Sentiment Analysis - What's all the fuss about? (17:17)
    • Using VADER for Sentiment Analysis (5:42)
    • Sentiment Analysis as an Machine Learning Classification Problem (5:01)
    • Machine Learning used for Sentiment Analysis (3:54)
    • ML Solutions for Sentiment Analysis - the devil is in the details (19:57)
    • Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet) (18:49)
    • Installing Python - Anaconda and Pip (9:00)
    • Back to Basics : Numpy in Python (18:06)
    • Back to Basics : Numpy and Scipy in Python (14:19)
    • Regular Expressions (17:53)
    • Regular Expressions in Python (5:41)
    • Put it to work : Twitter Sentiment Analysis (17:48)
    • Twitter Sentiment Analysis - Work the API (20:00)
    • Twitter Sentiment Analysis - Regular Expressions for Preprocessing (12:24)
    • Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet (19:40)
    • Using Bigrams as a Feature in Classification (3:46)
    • Identifying words with negation (3:08)
    • Wrapping up and further reading (1:38)

View Full Curriculum


Access
Lifetime
Content
4.5 hours
Lessons
19

Byte-Sized-Chunks: Decision Trees and Random Forests

Learn Intuitive Machine Learning Techniques by Exploring a Classic Problem

By Loonycorn | in Online Courses

Decision trees and random forests are two intuitive and extremely effective Machine Learning techniques that allow you to better predict outcomes from a selected input. Both methods are commonly used in business, and knowing how to implement them can put you ahead of your peers. In this course, you'll learn these techniques by exploring a famous (but morbid) Machine Learning problem: predicting the survival of a passenger on the Titanic.

  • Access 19 lectures & 4.5 hours of content 24/7
  • Design & implement a decision tree to predict survival probabilities aboard the Titanic
  • Understand the risks of overfitting & how random forests help overcome them
  • Identify the use-cases for decision trees & random forests
  • Use provided source code to build decision trees & random forests
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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. For more details on the course and instructor, click here.

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

  • Internet required

Course Outline

  • Decision Fatigue, And Decision Trees
    • You, This Course, and Us! (2:02)
    • Planting the seed - What are Decision Trees? (17:00)
    • Growing the Tree - Decision Tree Learning (18:03)
    • Branching out - Information Gain (18:51)
    • Decision Tree Algorithms (7:50)
    • Installing Python - Anaconda and Pip (9:00)
    • Back to Basics : Numpy in Python (18:06)
    • Back to Basics : Numpy and Scipy in Python (14:19)
    • Titanic : Decision Trees predict Survival (Kaggle) - I (19:21)
    • Titanic : Decision Trees predict Survival (Kaggle) - II (14:16)
    • Titanic : Decision Trees predict Survival (Kaggle) - III (13:00)
  • A Few Useful Things to Know About Overfitting
    • Overfitting - The Bane of Machine Learning (19:03)
    • Overfitting Continued (11:19)
    • Cross-Validation (18:55)
    • Simplicity is a virtue - Regularization (7:18)
    • The Wisdom Of Crowds - Ensemble Learning (16:39)
    • Ensemble Learning continued - Bagging, Boosting and Stacking (18:03)
  • Random Forests
    • Random Forests - Much more than trees (12:28)
    • Back on the Titanic - Cross Validation and Random Forests (20:03)

View Full Curriculum


Access
Lifetime
Content
2 hours
Lessons
9

An Introduction To Deep Learning & Computer Vision

Discover Core Machine Learning Concepts & Build An Artificial Neural Network

By Loonycorn | in Online Courses

Deep Learning is an exciting branch of Machine Learning that provide solutions for processing the high-dimensional data produced by Computer Vision. This introductory course brings you into the complex, abstract world of Computer Vision and artificial neural networks. By the end, you'll have a solid foundation in a core principle of Machine Learning.

  • Access 9 lectures & 2 hours of content 24/7
  • Design & implement a simple computer vision use-case: digit recognition
  • Train a neural network to classify handwritten digits in Python
  • Build a neural network & specify the training process
  • Grasp the central theory underlying Deep Learning & Computer Vision
  • Understand use-cases for Computer Vision & Deep Learning
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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.

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, but some knowledge of Python is suggested

Compatibility

  • Internet required

Course Outline

  • Look Long, Look Deep
    • You, This Course, and Us! (1:52)
    • Artificial Neural Networks:Perceptrons Introduced (11:18)
    • Computer Vision - An Introduction (18:08)
    • Perceptron Revisited (16:00)
    • Deep Learning Networks Introduced (17:01)
    • Installing Python - Anaconda and Pip (9:00)
    • Code Along - Handwritten Digit Recognition -I (14:29)
    • Code Along - Handwritten Digit Recognition - II (17:35)
    • Code Along - Handwritten Digit Recognition - III (6:01)

View Full Curriculum


Access
Lifetime
Content
4.5 hours
Lessons
20

Byte-Sized-Chunks: Recommendation Systems

Understand How Online Recommendations Work by Building a Movie App

By Loonycorn | in Online Courses

Assuming you're an internet user (which seems likely), you use or encounter recommendation systems all the time. Whenever you see an ad or product that seems eerily in tune with whatever you were just thinking about, it's because of a recommendation system. In this course, you'll learn how to build a variety of these systems using Python, and be well on your way to a high-paying career.

  • Access 20 lectures & 4.5 hours of content 24/7
  • Build Recommendation Engines that use content based filtering to find products that are most relevant to users
  • Discover Collaborative Filtering, the most popular approach to recommendations
  • Identify similar users using neighborhood models like Euclidean Distance, Pearson Correlation & Cosine
  • Use Matrix Factorization to identify latent factor methods
  • Learn recommendation systems by building a movie-recommending app in Python
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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. For more details on the course and instructor, click here.

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, but some knowledge of Python is suggested

Compatibility

  • Internet required

Course Outline

  • Would You Recommend To A Friend?
    • You, This Course, and Us! (1:38)
    • What do Amazon and Netflix have in common? (16:43)
    • Recommendation Engines - A look inside (10:45)
    • What are you made of? - Content-Based Filtering (13:35)
    • With a little help from friends - Collaborative Filtering (10:26)
    • A Neighbourhood Model for Collaborative Filtering (17:50)
    • Top Picks for You! - Recommendations with Neighbourhood Models (9:41)
    • Discover the Underlying Truth - Latent Factor Collaborative Filtering (20:13)
    • Latent Factor Collaborative Filtering contd. (12:09)
    • Gray Sheep and Shillings - Challenges with Collaborative Filtering (8:12)
    • The Apriori Algorithm for Association Rules (18:31)
  • Recommendation Systems in Python
    • Installing Python - Anaconda and Pip (9:00)
    • Back to Basics : Numpy in Python (18:05)
    • Back to Basics : Numpy and Scipy in Python (14:19)
    • Movielens and Pandas (16:45)
    • Code Along - What's my favorite movie? - Data Analysis with Pandas (6:18)
    • Code Along - Movie Recommendation with Nearest Neighbour CF (18:10)
    • Code Along - Top Movie Picks (Nearest Neighbour CF) (6:16)
    • Code Along - Movie Recommendations with Matrix Factorization (17:55)
    • Code Along - Association Rules with the Apriori Algorithm (9:51)

View Full Curriculum


Access
Lifetime
Content
10.5 hours
Lessons
54

From 0 to 1: Learn Python Programming

Make Quick Work of This Popular, Powerful Programming Language in 10.5 Hours

By Loonycorn | in Online Courses

Python's one of the easiest yet most powerful programming languages you can learn, and it's proven its utility at top companies like Dropbox and Pinterest. In this quick and dirty course, you'll learn to write clean, efficient Python code, learning to expedite your workflow by automating manual work, implementing machine learning techniques, and much more.

  • Dive into Python w/ 10.5 hours of content
  • Acquire the database knowledge you need to effectively manipulate data
  • Eliminate manual work by creating auto-generating spreadsheets w/ xlsxwriter
  • Master machine learning techniques like sk-learn
  • Utilize tools for text processing, including nltk
  • Learn how to scrape websites like the NYTimes & Washington Post using Beautiful Soup
  • Complete drills to consolidate your newly acquired knowledge
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. 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.

Details & Requirements

  • Length of time users can access this course: lifetime access
  • 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

Compatibility

  • Internet required

Course Outline

  • What is coding? - It's a lot like cooking!
    • Introduction
    • Coding is like Cooking
    • Variables are like containers
    • Anaconda and Pip
  • Don't Jump Through Hoops, Use Dictionaries, Lists and Loops
    • A List is a list
    • Fun with Lists!
    • Dictionaries and If-Else
    • Don't Jump Through Hoops, Use Loops
    • Doing stuff with loops
    • Everything in life is a list - Strings as lists
  • Our First Serious Program
    • Modules are cool for code-reuse
    • Our first serious program : Downloading a webpage
    • A few details - Conditionals
    • A few details - Exception Handling in Python
  • Doing Stuff with Files
    • A File is like a barrel
    • Autogenerating Spreadsheets with Python
    • Autogenerating Spreadsheets - Download and Unzip
    • Autogenerating Spreadsheets - Parsing CSV files
    • Autogenerating Spreadsheets with XLSXwriter
  • Functions are like Foodprocessors
    • Functions are like Foodprocessors
    • Argument Passing in Functions
    • Writing your first function
    • Recursion
    • Recursion in Action
  • Databases - Data in rows and columns
    • 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
    • Build a database of Stock Movements - I
    • Build a database of Stock Movements - II
    • Build a database of Stock Movements - III
  • An Object Oriented State of Mind
    • Objects are like puppies!
    • A class is a type of variable
    • An Interface drives behaviour
  • 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
    • Autosummarize News Articles - I
    • Autosummarize News Articles - II
    • Autosummarize News Articles - III
  • Machine Learning and Python
    • Machine Learning - Jump on the Bandwagon
    • Plunging In - Machine Learning Approaches to Spam Detection
    • Spam Detection with Machine Learning Continued
    • News Article Classification using K-Nearest Neighbors
    • News Article Classification using Naive Bayes
    • Code Along - Scraping News Websites
    • Code Along - Feature Extraction from News articles
    • Code Along - Classification with K-Nearest Neighbours
    • Code Along - Classification with Naive Bayes
    • Document Distance using TF-IDF
    • News Article Clustering with K-Means and TF-IDF
    • Code Along - Clustering with K-Means

View Full Curriculum



Terms

  • Instant digital redemption

15-Day Satisfaction Guarantee

We want you to be happy with every course you purchase! If you're unsatisfied for any reason, we will issue a store credit refund within 15 days of purchase.