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The Complete Python Data Science Bundle

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Content
2.5 hours
Lessons
23

Learn By Example: Pandas

Complete Your Data Science Toolbox with this Essential Python Library

By Loonycorn | in Online Courses

It's no secret that data scientists stand to make a pretty penny in today's data-driven world; but if you're keen on becoming one, you'll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

  • Access 23 lectures & 2.5 hours of content 24/7
  • Explore Panda's built-in functions for common data manipulation techniques
  • Learn how to work with data frames & manage data
  • Deepen your understanding w/ example-driven lessons

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:02
    • Course Materials
  • Introduction
    • Introduction To Pandas - 7:23
    • Series - 4:54
    • Series - continued - 12:49
    • Dataframes - 10:53
    • Creating Dataframes - 6:18
    • Addition And Deletion - 10:00
  • Selecting, Indexing, Reshaping And Other Operations
    • Selection And Indexing - 9:39
    • Selection And Indexing - continued - 3:58
    • Iterating Over Dataframes - 3:56
    • Reshaping Using Pivot - 12:01
    • Export To CSV Excel Text - 4:44
    • Stack Unstack - 4:25
    • Sorting - 5:17
  • Missing Data, MultiIndex, Group By, Concat And Other Operations
    • Handling Missing Data - 11:36
    • MultiIndex DataFrames - 4:47
    • GroupBy - 7:16
    • Concat And Merge - 12:18
    • SQL - 7:17
    • Explore Data Using Pandas - 10:57
    • Pandas For TimeSeries Data - 4:58
    • Summary - 1:18

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27

Learn By Example: NumPy

Start Working with Multidimensional Data as You Dive Into This Python Library

By Loonycorn | in Online Courses

Today's companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy's basic operations, universal functions, and more as you learn from hands-on examples.

  • Access 27 lectures & 2.5 hours of content 24/7
  • Familiarize yourself w/ NumPy's basic operations & universal functions
  • Learn how to properly manage data w/ hands-on examples
  • Validate your training w/ a certificate of completion

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:48
    • Course Materials
  • NumPy for Multi-dimensional Arrays
    • Overview - 4:31
    • Array Creation - 10:06
    • Printing Arrays - 4:34
    • Basic Operations - 10:42
    • Universal Functions - 9:36
    • Iterating Over Arrays - 5:35
    • Reshaping Arrays - 6:57
    • Indexing And Slicing - 7:02
    • Splitting Arrays - 8:02
    • Automatic Reshaping - 4:41
    • Copying Arrays - 3:21
  • Complex Indexing
    • Indexing Arrays Using Other Arrays - 3:50
    • Fancy Indexing - 2:58
    • Conditional Evaluation - 4:09
    • Structured Data In Arrays - 6:25
    • Broadcasting - 8:19
    • Array Broadcasting - 3:27
    • Images As 3D Arrays - 6:21
    • Image Manipulation - 10:48
  • Miscellaneous Operations
    • Vector Stacking - 5:35
    • Useful Functions - 5:52
    • Vectorization - 2:23
    • SciPy Integration - 5:12
    • Pandas Integration - 5:27
    • Summary - 1:38

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16

Learn By Example: Seaborn

Go From Mounds of Data to Detailed Insights Using This Powerful Visualization Tool

By Loonycorn | in Online Courses

From tech to medicine and finance, data plays a pivotal role in guiding today's businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That's where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

  • Access 16 lectures & 1.5 hours of content 24/7
  • Familiarize yourself w/ Seaborn via hands-on examples
  • Discover Seaborn's enhanced data visualization capabilities
  • Explore histograms, linear relationships & more visualization concepts

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:21
    • Course Materials
  • Introduction
    • Overview - 2:15
    • Installing Seaborn And Exploring Pokemon Dataset - 3:32
    • Matplotlib and Seaborn - 6:31
  • Distributions And Relationships
    • Kernal Density Estimation (KDE) - 5:08
    • Visualizing Distribution To Find Patterns - 14:47
    • Linear Relationships - 6:31
    • Categorical Data And Multipanel Data - 11:47
  • Trellis Plots
    • The FacetGrid - 10:28
    • Customizing The FacetGrid - 4:07
    • The PairGrid - 4:59
  • Aesthetics, Styles, Colors
    • Themes And Figure Styles - 3:51
    • Color Palettes - 9:03
    • Figure Aesthetics - 2:40
    • Summary - 1:24

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Lessons
30

Learn By Example: Matplotlib

Build Professional Graphs & Plots with this Essential Visualization Tool

By Loonycorn | in Online Courses

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

  • Access 30 lectures & 3 hours of content 24/7
  • Explore the anatomy of a Matplotlib figure & its customizable parts
  • Dive into figures, axes, subplots & more components
  • Learn how to draw statistical insights from data
  • Understand different ways of conveying statistical information

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:39
    • Course Materials
  • Matplotlib and Pyplot
    • Overview - 2:24
    • Importance of Visualization - 5:32
    • Object Hierarchy - 4:02
    • Anatomy Of A Figure - 1:59
    • Non-Interactive Mode - 5:48
    • Interactive Mode - 4:39
    • Getting Started - 6:24
    • Lines And Markers - 6:36
    • Figures And Axes - 10:59
    • Figures And Subplots - 9:36
    • Watermarks - 10:13
    • Putting It Together - 7:37
  • Varieties of Plots
    • Shapes - 11:32
    • Polygon and Arrows - 3:37
    • Bezier Curves - 4:38
    • Curves - 9:21
    • Annotations - 11:11
    • Scales - 7:04
    • Twin Axis - 5:01
  • Statistical Data
    • Boxplots And Violinplots - 9:43
    • Visualize Corn Data With Box And Violin Plots - 9:46
    • Histograms - 7:55
    • Pie Charts - 8:58
    • Stacked Plots - 7:31
    • Color Maps - 8:00
    • Autocorrelation - 4:02
    • Autocorrelation continued - 4:51
    • Summary - 1:55

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27

Learn By Example: Spark 2.x

Come to Grips with Spark & Pull Valuable Insights from Your Data

By Loonycorn | in Online Courses

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist's toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you'll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

  • Access 27 lectures & 3 hours of content 24/7
  • Explore what Spark 2.x can do via hands-on projects
  • Learn how to analyze data at scale & extract insights w/ Spark transformations and actions
  • Deepen your understanding of data frames & Resilient Distributed Datasets

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:45
    • Course Materials
  • Spark 2.x vs. Spark 1.x
    • Overview - 3:31
    • Distributed Computing - 13:08
    • Spark - 2:57
    • Resilient Distributed Datasets (RDDs) - 13:29
    • RDDs And Dataframes - 3:54
    • Installation And Setup - 6:00
    • Introducing Spark 2.x - 7:57
    • Complex DataTypes In Dataframes - 9:14
    • Creating A Dataframe Directly Using The SQL Context - 13:05
    • Spark Dataframes And Pandas Dataframes - 5:59
  • Exploring and Analyzing Data
    • Zomato Restaurants - 7:44
    • Operations: Aggregations, GroupBy, Sampling - 10:48
    • Operations: Aggregations, GroupBy, Sampling - continued - 5:40
    • Architecture Overview And Project Tungsten - 7:38
    • Olympic History - 9:35
    • Accumulators and Broadcast Variables: Introduction - 7:12
    • Accumulators And Broadcast Variables: Joins - 5:59
    • Accumulators And Broadcast Variables: Joins - continued - 10:12
    • Accumulators And Broadcast Variables: Custom - 7:20
  • Querying Data Using Spark SQL
    • Spark SQL - 5:02
    • FIFA World Cup - 6:47
    • Inferred And Explicit Schemas - 5:02
    • Windowing Functions - 4:39
    • Windowing Functions - continued - 5:46
    • Summary - 1:24

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27

Learn By Example: Plotly

Create Insightful Charts & Graphs with Minimal Programming Know-How

By Loonycorn | in Online Courses

You don't need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

  • Access 27 lectures & 2 hours of content 24/7
  • Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations
  • Explore visualizing data in more than two dimensions
  • Discover how to add motion to your graphs
  • Work w/ plots on your local machine or share them via the Plotly Cloud

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:12
    • Course Materials
  • Introduction
    • Overview - 1:33
    • Introduction - 6:43
    • Offline Plots - Not Connected To The Cloud - 5:04
    • Online Plots - Connected To The Cloud - 3:41
  • Basics
    • Line Charts - 8:26
    • Bar Charts - 5:04
    • Histograms - 6:41
    • Pie Charts - 3:48
    • Inset Plots - 5:06
    • Box Plots - 5:56
    • Scatter Plots - 5:19
  • Advanced Plots
    • Bubble Charts - 5:25
    • Bubble Maps - 5:16
    • Time Series - 6:16
    • Heat Maps - 4:04
    • Candlestick Charts - 1:55
    • Candlestick Charts - continued - 2:59
    • SVG Path - 3:44
    • Introducing Shapes - 5:51
    • Funnel Charts - 8:01
    • Funnel Charts - continued - 2:13
  • 3D And Interactivity
    • Gantt Charts - 6:54
    • 3D Scatter Plots - 3:31
    • 3D Surface Plots - 2:41
    • Animations - 8:12
    • Summary - 2:28

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Lessons
36

Learn By Example: Spark Streaming 2.x

Handle Continuous Data Like a Pro as You Learn From Real-World Examples

By Loonycorn | in Online Courses

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You'll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

  • Access 36 lectures & 2.5 hours of content 24/7
  • Familiarize yourself w/ Spark 2.x & its support for continuous applications
  • Learn how to analyze data from real-world streams
  • Analyze data from restaurants listed on Zomato & real-time Twitter data

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:09
    • Course Materials
  • Streaming API in Spark 2.x
    • Overview - 2:59
    • Resilient Distributed Datasets (RDDs) With Streaming Data - 4:46
    • Streaming Architecture - 10:06
    • DStreams In Spark 1.x - 4:05
    • Structured Streaming in Spark 2.x - 5:15
    • Installation and setup - 5:10
    • What Are Continuous Applications? - 6:57
    • Triggers And Output Modes - 8:54
    • Netcat - 7:57
  • Streaming Pipelines
    • Append Mode - 6:50
    • Complete Mode - 3:48
    • Average Aggregations - 3:21
    • SQL Queries - 4:46
    • Timestamps - 3:03
    • Groupby Timestamp - 2:38
    • Window Transformations - 4:27
    • Tumbling And Sliding Windows - 3:37
    • Event, Ingestion And Processing Time - 6:13
    • Windowing - 5:43
    • Watermarks - 7:12
    • Twitter Keys And Access Tokens - 5:35
    • Twitter Streaming - 4:25
    • Count Hashtags - 4:26
    • Count Hashtags: Windows - 3:32
    • Joins - 2:44
    • Aggregate Joins - 2:23
    • Aggregate Score By Enrollment - 2:07
    • Windowed Joins - 2:56
  • Spark + Kafka
    • Kafka - 4:30
    • Producer-Consumer - 4:06
    • Hashtag Producer - 4:39
    • German to English Conversion - 3:44
    • Tweet Producer - 3:08
    • Summary - 2:12

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Lessons
41

Learn By Example: PyTorch

Explore & Create the Building Blocks That Power Today's AI with PyTorch

By Loonycorn | in Online Courses

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you'll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

  • Access 41 lectures & 3.5 hours of content 24/7
  • Understand neurons & neural networks and how they factor into machine learning
  • Explore the basic steps involved in training a neural network
  • Familiarize yourself w/ PyTorch & Python 3
  • Analyze air quality data, salary data & more real-world datasets

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:46
    • Course Materials
  • Introduction To PyTorch And Neural Networks
    • Overview - 2:23
    • Neurons And Neural Networks - 8:35
    • Introducing PyTorch - 6:43
    • Installation And Setup - 1:41
    • The Computation Graph - 4:06
    • Gradient Descent - 4:37
    • Forward And Backward Passes - 1:59
  • PyTorch Tensors
    • PyTorch Tensors - 2:57
    • PyTorch Tensors Implementation - I - 5:56
    • PyTorch Tensors Implementation - II - 4:13
    • PyTorch Tensors Implementation - III - 10:15
  • Gradient Descent And Autograd
    • Gradients, A Vector Of Partial Derivatives - 5:50
    • Autograd - 4:43
    • Reverse Mode Auto Differentiation - 9:51
    • Linear Regression Using Autograd - 7:00
  • Regression and Classification
    • Regression To Predict Air Quality - 7:13
    • Regression To Predict Air Quality - continued - 6:37
    • Optimizers - 2:34
    • Neural Networks For Classification - 4:45
    • Classification To Categorize Salary Categories - 5:57
    • Classification To Categorize Salary Categories - continued - 7:39
  • Convolutional Neural Networks In PyTorch
    • Viewing An Image - 2:11
    • Convolution - 6:47
    • Pooling - 2:35
    • CNN Architectures - 2:25
    • Batch Normalization - 3:55
    • Neural Networks To Classify House Numbers - 4:44
    • Neural Networks To Classify House Numbers - continued - 7:24
  • Recurrent Neural Networks In PyTorch
    • Recurrent Neurons - 4:59
    • Layers In An RNN - 3:01
    • Long/Short Term Memory - 2:08
    • Language Prediction Using RNNs - 5:06
    • Recurrent Neural Networks To Predict Languages Associated With Names - 11:52
    • Confusion Matrix - 2:22
    • Confusion Matrix For Classification - 2:54
  • Transfer Learning And Pre-trained Models
    • Transfer Learning - 5:20
    • Resnet-18 Model To Classify Fruits - 6:45
    • Resnet-18 Model To Classify Fruits - continued - 9:18
    • Summary - 2:16

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31

Learn By Example: Apache MXNet

Build Fast & Flexible Machine Learning Apps Once You Master This Intuitive Framework

By Loonycorn | in Online Courses

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

  • Access 31 lectures & 2 hours of content 24/7
  • Explore neurons & neural networks and how they factor into machine learning
  • Walk through the basic steps of training a neural network
  • Dive into building neural networks for classifying images & voices
  • Refine your training w/ real-world examples & datasets

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises 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 and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:24
    • Course Materials
  • Introduction to Neural Networks and Apache MXNet
    • Overview - 3:46
    • Neurons And Neural Networks - 5:43
    • Apache MXNet - 4:43
    • Installing And Setup - 3:06
    • Symbolic vs, Imperative Programming - 8:35
    • Gradient Descent - 3:26
    • Forward And Backward Passes - 2:05
  • NDArrays
    • NDArrays - 2:55
    • NDArrays Implementations - 6:43
    • NDArrays Implementations - continued - 4:49
  • Symbol API and Module API
    • Symbol API - 3:48
    • Symbol API - Computation Graphs - 7:58
    • Data Iterators - 4:41
    • Module API - 4:25
  • Voice Recognition Neural Networks With The Symbol And Module API
    • The Voice Recognition Dataset - 5:43
    • Setting Up The NN - 4:09
    • Setting Up The NN - continued - 1:36
  • Convolutional Neural Networks With The Gluon API
    • Introducing The Gluon API - 4:25
    • Autograd - 7:09
    • Autograd Implementation - 2:10
    • Convolutional Neural Networks - 5:10
    • Image Preprocessing - 2:03
    • The Shapes Dataset - 5:21
    • Building And Training A CNN - 5:43
    • Hybridize Your NN For Symbolic Execution - 3:31
  • Transfer Learning And The Gluon Model Zoo
    • Transfer Learning - 2:25
    • The Gluon Model Zoo - 1:45
    • Image Classification Using A Pretrained Model - 5:17
    • Summary - 3:09

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28

An Easy Introduction to Python

Become a Python Programmer in Just a Few Hours

By Loonycorn | in Online Courses

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you've never even thought about coding, this course will serve as your diving board to jump right in.

  • Access 28 lectures & 3 hours of content 24/7
  • Gain a fundamental understanding of Python loops, data structures, functions, classes, & more
  • Learn how to solve basic programming tasks
  • Apply your skills confidently to solve real problems
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and 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 streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us (1:56)
    • Source Code
  • Getting Set Up
    • Install Anaconda (2:21)
  • Introducing Python
    • Saying Hello World in Python (5:23)
    • The If-Else Statement (10:32)
    • For Loops (9:45)
  • Data Structures
    • Lists: An Introduction (8:46)
    • Lists: Manipulating Lists with Slicing (9:58)
    • Dictionaries: Storing Key-Value Pairs (6:05)
    • Dictionaries: The setdefault Method, Dictionary of Dictionaries (6:40)
    • Sets and Tuples (4:36)
  • Define your own Functions, Modules and Classes
    • Functions (9:49)
    • Modules: Wrap your Functions into a Module (9:05)
    • Classes: The init Method and Class Variables (7:54)
    • Classes: Enhancing our Class with Decorators and Operator Overloading (7:57)
  • Getting Real - Writing a Web App
    • Build a Simple Web App using the Flask Web Framework (5:13)
    • Extending our Web App to use Web Templates (5:56)
    • Integrating our Web App with our Custom Module (6:57)
  • Common Programming Tasks
    • Parsing JSON Data (7:18)
    • Files and Exception Handling (10:46)
    • Regular Expressions (8:33)
    • Iterators (8:30)
  • Popular Python Libraries
    • Web Scraping with BeautifulSoup (3:57)
    • Pandas: An Introduction to Data Analysis (7:03)
    • Pandas: Transforming JSON Data into a Pandas Data Frame (4:13)
  • Logging
    • Log File: Logging Requests on our Web App to a file (4:37)
    • Databases: Setting up MariaDB to Store Log Data (6:31)
    • Databases: Logging Requests on our Web App to MariaDB (5:30)
    • Using the With Keyword to Manage our Database Connection (10:12)

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Lifetime
Content
2 hours
Lessons
17

An Easy Introduction To Machine Learning Using Scikit-Learn

Dive Into Automated Decision-Making with Python's Scikit-Learn

By Loonycorn | in Online Courses

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python's scikit-learn. If you're an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

  • Access 17 lectures & 2 hours of content 24/7
  • Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification
  • Learn about support vector machines, decision trees & random forests using real data sets
  • Discover how to use decision trees to get better results

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

Course Outline

  • Introduction
    • You, This Course and Us (1:56)
    • Source Code and PDFs
    • Install Anaconda (2:21)
  • What is ML?
    • What is Machine Learning? (10:42)
    • Types of Machine Learning - Supervised Learning and Linear Regression (10:29)
    • Types of Machine Learning - Logistic Regression and Unsupervised Learning (8:22)
  • Support Vector Machines (SVMs)
    • What is an SVM? How do they work? (6:39)
    • SVM Lab (1): Loading and examining our data set (9:11)
    • SVM Lab (2): Building and tweaking our SVM classification model (9:08)
  • Decision Trees
    • What is a Decision Tree? (6:12)
    • Building a Decision Tree - Decision Tree Learning (7:43)
    • Building a Decision Tree - Information Gain and Gini Impurity (9:16)
    • Decision Trees Lab (1): Building our first Decision Tree (5:20)
    • Decision Trees Lab (2): Viewing and tweaking our Decision Tree (5:51)
  • Overfitting - the Bane of Machine Learning
    • What is Overfitting? And Why is it a Problem? (9:26)
    • Avoiding Overfitted Models - Cross Validation and Regularization (8:17)
  • Ensemble Learning and Random Forests
    • Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting (9:03)
    • Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results (4:48)

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Lifetime
Content
8.5 hours
Lessons
62

An Easy Introduction To AI And Deep Learning

Get Your Feet Wet with the Backbone to Siri, Self-Driving Cars & More

By Loonycorn | in Online Courses

Deep learning isn't just about helping computers learn from data—it's about helping those machines determine what's important in those datasets. This is what allows for Tesla's Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

  • Access 62 lectures & 8.5 hours of content 24/7
  • Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants
  • Create regression models w/ TensorFlow
  • Learn how to streamline building & evaluating models w/ TensorFlow's estimator API
  • Use deep neural networks to build classification & regression models

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: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us (2:38)
    • Source Code and PDFs
    • Datasets for all Labs
  • Installation
    • Install TensorFlow (6:24)
    • Install Jupyter Notebook (4:38)
    • Running on the GCP vs. Running on your local machine
    • Lab: Setting Up A GCP Account (6:59)
    • Lab: Using The Cloud Shell (6:01)
    • Datalab ~ Jupyter (3:00)
    • Lab: Creating And Working On A Datalab Instance (10:29)
  • Unsupervised Learning
    • Supervised and Unsupervised Learning (11:30)
    • Expressing Attributes as Numbers (5:33)
    • K-Means Clustering (15:14)
    • Lab: K-Means Clustering with 2-Dimensional Points in Space (8:51)
    • Lab: K-Means Clustering with Images (10:19)
    • Patterns in Data (3:19)
    • Principal Components Analysis (13:19)
    • Autoencoders (5:03)
    • Autoencoder Neural Network Architecture (9:04)
    • Lab: PCA on Stock Data - Matplotlib vs Autoencoders (14:15)
    • Stacked Autoencoders (4:27)
    • Lab: Stacked Autoencoder With Dropout (7:51)
    • Lab: Stacked Autoencoder With Regularization and He Initialization (6:14)
    • Denoising Autoencoders (1:26)
    • Lab: Denoising Autoencoder with Gaussian Noise (1:58)
    • Quiz 11: Unsupervised Learning

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Terms

  • Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.