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Python Coding Essentials Bundle - Build Projects and Learn to Code
Intro to HTML (Prerequisite for Flask)
00 How To Become A Web Developer (7:40)
01 HTML Basics (7:26)
02 CSS Basics (5:50)
03 Add Images To Website With HTML (9:13)
04 Link To Pages With HTML Hyperlinks (5:30)
05 Positioning Items On A Webpage With CSS Flexbox (11:32)
06 Spacing Out Items With Flexbox (9:31)
Python and Flask Web Development Bootcamp
00 Project Preview (4:13)
01 What Is Flask (3:45)
02 What You'll Need (1:48)
03 Nrog Update
04 Build Your First Flask App (13:26)
05 Render HTML On Multiple Pages (10:53)
06 Build Page Templates With HTML (9:31)
07 Build Dynamic Page Templates (5:36)
08 Display JSON Data (5:21)
09 Build A Template To Show All Data (9:16)
Source Files
Python and SQLite Bootcamp - Learn to Build Databases
00 What Is Sqlite (4:01)
01 Project And Database Preview (2:37)
02 Create A Database (4:00)
03 Build Database Tables (5:25)
04 Insert Records (9:18)
05 Select Records (5:48)
06 Connect To A Database (3:19)
07 Build A Row From User Input (10:27)
08 Check If Entry Already Exists (5:39)
09 Introduction To SQL Joins (3:53)
10 Build SQL Joins (11:32)
11 What You'll Need (1:54)
Source Files
Creating Your First Movie Recommender System: A Comprehensive Guide to Building Basic Film Suggestion Engines
01 Introduction To Recommender Systems (9:08)
02 How To Evaluate Recommender Systems (14:54)
03 Content Based Recommendations (4:37)
04 Neighborhood Based Collaborative Filtering (2:22)
05 Project Preview (1:59)
06 Load Data As Pandas Dataframes (12:17)
07 Merge Movies And Ratings Dataframes (8:30)
08 Build A Correlation Matrix (6:20)
09 Test The Recommender (6:55)
Source Files
Introduction to Machine Learning
01 What Is Machine Learning (5:26)
02 Types Of Machine Learning Models (12:17)
03 What Is Supervised Learning (11:04)
04 What Is Unsupervised Learning (8:17)
05 How Does A Machine Learning Agent Learn (7:38)
06 What Is Inductive Learning (4:11)
07 Performance Of A Machine Learning Algorithm (4:14)
08 Handle Noise In Data (5:22)
09 Powerful Tools With Machine Learning Libraries (12:11)
Mastering the Basics: Constructing a Movie Recommendation System using Machine Learning
01 Load Data Into Dataframes (6:50)
02 Project Preview (4:51)
03 Find A Recommendation Based On Different Movie Features (16:03)
04 Calculate Distance Between Users (5:59)
05 Find Similar Users With Euclidean Distance (9:26)
06 Define Similarity Between Users (6:29)
07 Find Top Similar Users (8:05)
08 Recommend A Movie With A K Nearest Neighbors Classifier (12:23)
09 What Is K Nearest Neighbours (8:07)
10 Recommend Movies To Sample User (3:08)
11 Create A Sample User For Testing (11:09)
12 Recommend A Movie Based On User Similarity (8:08)
Source Files
Machine Learning User Recommendations with Profiles and Items
01 Load Data For A Neural Network (9:16)
02 Process Data For Machine Learning (11:25)
03 Build Categories (9:31)
04 Regression Introduction (8:58)
05 What Is Regression (19:55)
06 Build A Ridge Regression Model (13:43)
07 Evaluate Model Error (7:04)
08 Visualize Top Features Affecting Rating (11:27)
09 Build A Lasso Regression Model (8:01)
10 Visualize Top Features From Lasso Regression (8:07)
11 Determine Which Model Is Best (3:28)
12 Load Data For Machine Learning (15:14)
13 Build A Singular Value Decomposition Algorithm (10:14)
14 Calculate Model Error (11:27)
Source files
Build a Dense Neural Network to Recommend Movies
01 What Is Deep Learning (7:42)
02 What Is A Neural Network (8:47)
03 What Is Unsupervised Learning (8:17)
04 Build A Neural Network (15:16)
05 Train The Neural Network (12:27)
06 Project Preview (2:38)
07 Load Data Into Dataframes (5:28)
08 Explore Data In Our Dataset (3:49)
09 Build A Rating Pivot Table (5:22)
10 Calculate Average Rating Of A Movie (5:51)
11 Find Rating Averages For Every Movie In The Slice (7:54)
12 Find Ratings For A Movie In Every Slice (6:17)
13 Build An Average Ratings Column (26:50)
Source files
Python Chatbot Bootcamp with Pandas, NumPy and SciKit (Natural Language Processing - Build 2 Chatbots)
01 What Is Natural Language Processing (5:39)
02 Projects Preview (4:49)
03 What Is Text Vectorization (7:34)
04 Train A Vectorizer (8:50)
05 Chat With The User (11:04)
06 Define A Basic Intent Classifier (7:42)
07 Define A Basic Generative Model (4:05)
08 Test The Chatbot (9:33)
Source Files
PySpark - Build DataFrames with Python, Apache Spark and SQL
01 What Is Apache Spark (2:37)
02 Project Preview (2:33)
03 What Are Resilient Distributed Datasets (1:08)
04 What Is A Dataframe (1:47)
05 What You-ll Need (1:47)
06 Start A Spark Session (3:47)
07 Load Data As A CSV (6:02)
08 Perform Basic Dataframe Operations (4:02)
09 Format Dataframe Table (5:14)
10 Perform Dataframe Math Operations (7:32)
11 Perform Dataframe Queries (14:22)
12 Build SQL Queries With Spark (7:24)
Source Files
Scrape the Web - Python and Beautiful Soup Bootcamp
01 What You-ll Need (1:30)
02 What Is Web Scraping (5:39)
03 Build An Html Webpage To Scrape (12:42)
04 Select Data Structures From A Webpage (5:48)
05 Extract Urls And Text (5:24)
06 Work With Tags (8:06)
07 Work With Attributes (5:19)
08 Add Navigation To A String (5:29)
09 Navigate Html Contents (7:16)
10 Find All Filter (4:52)
Source Files
Build Interactive Python Dashboards with Plotly and Dash
01 What Is Plotly And Dash (3:59)
02 Project Preview (1:39)
03 What You-ll Need (2:09)
04 Build A Dash App (11:44)
05 Build A Graph In The Dash App (12:05)
06 Load Data From Vega Datasets (5:33)
07 Build The Layout (10:27)
08 Build A Chart With Altair (11:56)
Source Files
Python Data Analysis Bootcamp with Pandas and NLTK - Natural Language Processing
01 Project Preview (3:38)
02 Convert CSV File To A Python List (13:49)
03 Tokenize Text Data (26:25)
04 Find Most Popular Lemmatized Words (11:36)
05 Build Dataframes Per Part Of Speech (3:56)
06 Plot Word Frequency (9:09)
Source Files
Beginner Data Science and Machine Learning Bootcamp (Sentiment Analysis with Unsupervised Classification with Python, Pandas, NumPy, Matplotlib and SciKit-Learn)
01 Create A Dataset (5:17)
02 Project Preview (3:29)
03 Vectorize Text (16:27)
04 Build A Word Cloud (14:16)
05 Reduce Data Dimensionality With Principal Component Analysis (6:08)
06 Perform Unsupervised Classification With K-Means Clusters (17:33)
Source Files
Data Mining with Python and NumPy - Build a Video Recommender System
01 Project Preview (2:48)
02 Build A Dataset (23:44)
03 Compute Support And Confidence - If A Person Watches X, They Will Watch Y (10:06)
04 Compute Support And Confidence For All Channels (14:21)
05 Determine Which Videos Are Best To Recommend (9:57)
Source Files
12 Recommend A Movie Based On User Similarity
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