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Mastering AWS DeepRacer: Reinforcement Learning for Training, Racing, and Self-Driving

Mastering AWS DeepRacer: Reinforcement Learning for Training, Racing, and Self-Driving

From Search to Success: How AI Transforms Job Hunting and Recommendations

From Search to Success: How AI Transforms Job Hunting and Recommendations

Unlocking Twitter Insights: Master Prompt Engineering with OpenAI GPT

Unlocking Twitter Insights: Master Prompt Engineering with OpenAI GPT

Effortlessly Query 2024 Election Tweets Using MongoDB’s AI-Powered Natural Language Query

Effortlessly Query 2024 Election Tweets Using MongoDB’s AI-Powered Natural Language Query

Build Generative AI Apps in 5 Minutes with AWS Party Rock – No Coding Needed!

Build Generative AI Apps in 5 Minutes with AWS Party Rock – No Coding Needed!

NotebookLM: The AI Notebook That Transforms How You Study

NotebookLM: The AI Notebook That Transforms How You Study

AI-Powered Python: Low-Code Data Analysis in Jupyter Notebooks

AI-Powered Python: Low-Code Data Analysis in Jupyter Notebooks

Create a Full Machine Learning Pipeline Without Coding in AWS SageMaker Canvas

Create a Full Machine Learning Pipeline Without Coding in AWS SageMaker Canvas

SQL Query without Knowing SQL: How to Use ChatGPT for Database Queries

SQL Query without Knowing SQL: How to Use ChatGPT for Database Queries

How to Set Up a PostgreSQL Server and Collect Census Data in Python (AWS RDS + Sagemaker)

How to Set Up a PostgreSQL Server and Collect Census Data in Python (AWS RDS + Sagemaker)

Analyze Tweets with ChatGPT

Analyze Tweets with ChatGPT

Analyze Twitter Data with OpenAI V1

Analyze Twitter Data with OpenAI V1

Lab12. Create ArcGIS Online Dashboard

Lab12. Create ArcGIS Online Dashboard

Lab11 Create ArcGIS Online Map

Lab11 Create ArcGIS Online Map

Lab10. Map Design in ArcGIS Pro

Lab10. Map Design in ArcGIS Pro

Lab9. Create Geoprocessing Tools with ModelBuilder in ArcGIS Pro

Lab9. Create Geoprocessing Tools with ModelBuilder in ArcGIS Pro

Lab8. Raster Data Analysis and Suitability Model in ArcGIS Pro

Lab8. Raster Data Analysis and Suitability Model in ArcGIS Pro

Lab 7. Vector Data Analysis in ArcGIS Pro

Lab 7. Vector Data Analysis in ArcGIS Pro

Lab 6. Geocoding and Data Visualization in ArcGIS Pro

Lab 6. Geocoding and Data Visualization in ArcGIS Pro

Lab 5. Manage and Edit Data in ArcGIS Pro

Lab 5. Manage and Edit Data in ArcGIS Pro

Lab 4. Create 3D Models from LiDAR Data and Explore 3D Models in ArcGIS Pro

Lab 4. Create 3D Models from LiDAR Data and Explore 3D Models in ArcGIS Pro

Lab3. Capture and View GPS Data in ArcGIS Pro

Lab3. Capture and View GPS Data in ArcGIS Pro

Lab 2. Download and Explore Aerial Photos and Satellite Images in ArcGIS Pro

Lab 2. Download and Explore Aerial Photos and Satellite Images in ArcGIS Pro

Lab 1. Create Your First Project in ArcGIS Pro

Lab 1. Create Your First Project in ArcGIS Pro

Lab11 Collect and Visualize Twitter Social Network in Gephi V2

Lab11 Collect and Visualize Twitter Social Network in Gephi V2

A Serverless Dashboard to Analyze and Visualize Social Media Data in Real-time

A Serverless Dashboard to Analyze and Visualize Social Media Data in Real-time

Use AWS to Identify Twitter Topics and Sentiments

Use AWS to Identify Twitter Topics and Sentiments

Python Programming with AI

Python Programming with AI

Demo of Virtual Reality on AWS

Demo of Virtual Reality on AWS

deeplearning hand writing

deeplearning hand writing

Lab 1: Create an RDS Instance on AWS Academy

Lab 1: Create an RDS Instance on AWS Academy

Lec 9.4 Receiver Operating Characteristics Curve (ROC) and Area Under Curve (AUC) #machinelearning

Lec 9.4 Receiver Operating Characteristics Curve (ROC) and Area Under Curve (AUC) #machinelearning

Lec 9.3 Summary of Neural Network and Deep Learning Models #machinelearning

Lec 9.3 Summary of Neural Network and Deep Learning Models #machinelearning

Lec 9.2 The Multilayer Perceptrons model (MLP) #machinelearning

Lec 9.2 The Multilayer Perceptrons model (MLP) #machinelearning

Lec 9.1 Intro to Neural Network Model #machinelearning

Lec 9.1 Intro to Neural Network Model #machinelearning

Lec 8.3 Cross validation #machinelearning

Lec 8.3 Cross validation #machinelearning

Lec 8.2 Random forest and gradient boosting trees models #machinelearning

Lec 8.2 Random forest and gradient boosting trees models #machinelearning

Lec 8.1 Intro to decision trees model #machinelearning

Lec 8.1 Intro to decision trees model #machinelearning

Lec 7.4 Types of Naive Bayes Models #machinelearning

Lec 7.4 Types of Naive Bayes Models #machinelearning

Lec 7.3 Intro to Naive Bayes Model #machinelearning

Lec 7.3 Intro to Naive Bayes Model #machinelearning

Lec 7.2 Intro to Bayes Theory #machinelearning

Lec 7.2 Intro to Bayes Theory #machinelearning

Lec 7.1 Intro to probability theory #machinelearning

Lec 7.1 Intro to probability theory #machinelearning

Lec 6.3 More about K-Nearest Neighbors model #machinelearning

Lec 6.3 More about K-Nearest Neighbors model #machinelearning

Lec 6.2 Intro to K-Nearest Neighbors (KNN) model #machinelearning

Lec 6.2 Intro to K-Nearest Neighbors (KNN) model #machinelearning

Lec 6.1 Intro to linear algebra #machinelearning

Lec 6.1 Intro to linear algebra #machinelearning

Lec 5.4 Support Vector Machine (SVM) Model #machinelearning

Lec 5.4 Support Vector Machine (SVM) Model #machinelearning

Lec 5.3 Regularization of the logistic regression model #machinelearning

Lec 5.3 Regularization of the logistic regression model #machinelearning

Lec 5.2 Intro to logistic regression model #machinelearning

Lec 5.2 Intro to logistic regression model #machinelearning

Lec 5.1 Problem of the OLS in classification #machinelearning

Lec 5.1 Problem of the OLS in classification #machinelearning

Lec 4.5 Model regularization #machinelearning

Lec 4.5 Model regularization #machinelearning

Lec 4.4 Interpreting the multiple linear regression model #machinelearning

Lec 4.4 Interpreting the multiple linear regression model #machinelearning

Lec 4.3 Intro to multiple linear regression model #machinelearning

Lec 4.3 Intro to multiple linear regression model #machinelearning

Lec 4.2 Hypothesis testing #machinelearning

Lec 4.2 Hypothesis testing #machinelearning

Lec 4.1 Review of CDF, PDF and CLT #machinelearning

Lec 4.1 Review of CDF, PDF and CLT #machinelearning

Lec 3.4 GSD and MLE in Simple Linear Regression #machinelearning

Lec 3.4 GSD and MLE in Simple Linear Regression #machinelearning

Lec 3.3 Simple Linear Regression and OLS #machinelearning

Lec 3.3 Simple Linear Regression and OLS #machinelearning

Lec 3.2 Gradient Descent in Detail #machinelearning

Lec 3.2 Gradient Descent in Detail #machinelearning

Lec 3.1 Intro to Gradient Descent #machinelearning

Lec 3.1 Intro to Gradient Descent #machinelearning

Lec 2.2 Basic Data Visualization #machinelearning

Lec 2.2 Basic Data Visualization #machinelearning

Lec 2.1 Classical Descriptive Statistics #machinelearning

Lec 2.1 Classical Descriptive Statistics #machinelearning

Lec 1.4 Training data and testing data #machinelearning

Lec 1.4 Training data and testing data #machinelearning

Lec 1.3 Evaluate the machine learning models #machinelearning

Lec 1.3 Evaluate the machine learning models #machinelearning

Lec 1.2 Types of machine learning models #machinelearning

Lec 1.2 Types of machine learning models #machinelearning

Lec 1.1 Course Introduction #machinelearning

Lec 1.1 Course Introduction #machinelearning

Introduction to Machine Learning and Artificial Intelligence 2020 Fall Syllabus #machinelearning

Introduction to Machine Learning and Artificial Intelligence 2020 Fall Syllabus #machinelearning

Lec 8.5 Terrain analysis and NDVI #gis

Lec 8.5 Terrain analysis and NDVI #gis

Lec 8.4 Neighborhood and Zonal operations #gis

Lec 8.4 Neighborhood and Zonal operations #gis

Lec 8.3 Distance and Raster Overlay #gis

Lec 8.3 Distance and Raster Overlay #gis

Lec 8.2 Raster data reclassification #gis

Lec 8.2 Raster data reclassification #gis

Lec 8.1 Prepare for Raster Data Analysis #gis

Lec 8.1 Prepare for Raster Data Analysis #gis

Lec 7.4 clip, erase, spatial join #gis

Lec 7.4 clip, erase, spatial join #gis

Lec 7.3 Union, merge, intersect #gis

Lec 7.3 Union, merge, intersect #gis

Lec 7.2 buffer, dissolve and select #gis

Lec 7.2 buffer, dissolve and select #gis

Lec 7.1 Intro to geoprocessing #gis

Lec 7.1 Intro to geoprocessing #gis

Lec 6.4 Data classification #gis

Lec 6.4 Data classification #gis

Lec 6.3 Spatial and non-spatial query #gis

Lec 6.3 Spatial and non-spatial query #gis

Lec 6.2 Basic data visualization #gis

Lec 6.2 Basic data visualization #gis

Lec 6.1 Basic descriptive statistics #gis

Lec 6.1 Basic descriptive statistics #gis

Lec 5.4 NoSQL database and data quality #gis

Lec 5.4 NoSQL database and data quality #gis

Lec 5.3 Geodatabase #gis

Lec 5.3 Geodatabase #gis

Lec 5.2 Join and relationship in relational database #gis

Lec 5.2 Join and relationship in relational database #gis

Lec 5.1 Intro to relational database #gis

Lec 5.1 Intro to relational database #gis

Lec 4.3 #LiDAR data collection demo #gis

Lec 4.3 #LiDAR data collection demo #gis

Lec 4.2 Details about #LiDAR #gis

Lec 4.2 Details about #LiDAR #gis

Lec 4.1 Intro to #LiDAR #gis

Lec 4.1 Intro to #LiDAR #gis

Lec 3.2 GPS #gis

Lec 3.2 GPS #gis

Lec 3.1 Vector Data Model #gis

Lec 3.1 Vector Data Model #gis

Lec 2.4 Raster data resolution #gis

Lec 2.4 Raster data resolution #gis

Lec 2.3 Raster data model and color #gis

Lec 2.3 Raster data model and color #gis

Lec 2.2 Spectral Profile #gis

Lec 2.1 Intro to Aerial Photos and Satellite Images #gis

Lec 2.1 Intro to Aerial Photos and Satellite Images #gis

Lec 1.5 Most Common PCS, and GCS vs PCS #gis

Lec 1.5 Most Common PCS, and GCS vs PCS #gis

Lec 1.4 GCS and PCS #gis

Lec 1.4 GCS and PCS #gis

Lec 1.3 Introduction to Coordination System #gis

Lec 1.3 Introduction to Coordination System #gis

Lec 1.2 Vector data vs raster data #gis

Lec 1.2 Vector data vs raster data #gis

Lec 1.1 Introduction to Spatial data #gis

Lec 1.1 Introduction to Spatial data #gis

Lec 4.2 Intro to Redshift #aws #bigdata

Lec 4.2 Intro to Redshift #aws #bigdata

Lec 5.2 Extract, Transform and Load (ETL) #aws #bigdata

Lec 5.2 Extract, Transform and Load (ETL) #aws #bigdata

Lec 11 AWS AI Services #machinelearning

Lec 11 AWS AI Services #machinelearning

Lec 4.1 Types of Information Systems #aws #bigdata

Lec 4.1 Types of Information Systems #aws #bigdata

Lec 9.2 Map elements #gis

Lec 9.2 Map elements #gis

Lec 10.1 Intro to clustering #machinelearning

Lec 10.1 Intro to clustering #machinelearning

Lec 10.2 K-means clustering #machinelearning

Lec 10.2 K-means clustering #machinelearning

Lec 10.2 Using colors #gis

Lec 10.2 Using colors #gis

Lec 9.1 Principles of good visualization #gis

Lec 9.1 Principles of good visualization #gis

Big Data Analytics on AWS Syllabus 2021 Summer #aws #bigdata

Big Data Analytics on AWS Syllabus 2021 Summer #aws #bigdata

Lec 2.1 Intro to relational database #aws #bigdata

Lec 2.1 Intro to relational database #aws #bigdata

Lec 1.3 Intro to data analytics #aws #bigdata

Lec 1.3 Intro to data analytics #aws #bigdata

Lec 4.3 Types of data analytics #aws #bigdata

Lec 4.3 Types of data analytics #aws #bigdata

Lec 2.3 Structured Query Language (SQL) #aws #bigdata

Lec 2.3 Structured Query Language (SQL) #aws #bigdata

Lec 10.3 Principal Component Analysis (PCA) #machinelearning

Lec 10.3 Principal Component Analysis (PCA) #machinelearning

Lec 2.2 Intro to AWS RDS #aws #bigdata

Lec 2.2 Intro to AWS RDS #aws #bigdata

Lec 1.1 Course introduction #aws #bigdata

Lec 1.1 Course introduction #aws #bigdata

Lec 3.3 Data visualization best practices #aws #bigdata

Lec 3.3 Data visualization best practices #aws #bigdata

Lec 9.3 Types of Thematic map #gis

Lec 9.3 Types of Thematic map #gis

Lec 10.1 Color theory #gis

Lec 10.1 Color theory #gis

Lec 4.4 QuickSight ML insights #aws #bigdata

Lec 4.4 QuickSight ML insights #aws #bigdata

Lec 3.4 Intro to QuickSight #aws #bigdata

Lec 3.4 Intro to QuickSight #aws #bigdata

Lec 3.1 Intro to data visualization #aws #bigdata

Lec 3.1 Intro to data visualization #aws #bigdata

Lec 5.3 EMR, AWS Glue and AWS Athena #aws #bigdata

Lec 5.3 EMR, AWS Glue and AWS Athena #aws #bigdata

Lec 1.2 Intro to AWS #aws #bigdata

Lec 1.2 Intro to AWS #aws #bigdata

Lec 10.3 Dashboard design #gis

Lec 10.3 Dashboard design #gis

Lec 3.2 Type of graphs #aws #bigdata

Lec 3.2 Type of graphs #aws #bigdata

Introduction to Geographic Information Science and Cartography Fall 2021 Syllabus

Introduction to Geographic Information Science and Cartography Fall 2021 Syllabus

Lec 10.3 #MongoDB Aggregation pipeline #datamining

Lec 10.3 #MongoDB Aggregation pipeline #datamining

Lec 10.2 #MongoDB Document Query #datamining

Lec 10.2 #MongoDB Document Query #datamining

Lec 10.1 Intro to #MongoDB Query #datamining

Lec 10.1 Intro to #MongoDB Query #datamining

Lec 9.4 View tweets in #Python #datamining

Lec 9.4 View tweets in #Python #datamining

Lec 9.3 #Twitter REST API and Streaming API #datamining

Lec 9.3 #Twitter REST API and Streaming API #datamining

Lec 9.2 Authorize #Twitter API and Connect to #MongoDB #datamining

Lec 9.2 Authorize #Twitter API and Connect to #MongoDB #datamining

Lec 9.1 #Twitter API #datamining

Lec 9.1 #Twitter API #datamining

Demo of #MongoDB Atlas #datamining

Demo of #MongoDB Atlas #datamining

Lec 8.3 #MongoDB Cluster #datamining

Lec 8.3 #MongoDB Cluster #datamining

Lec 8.2 Data Organization in #MongoDB #datamining

Lec 8.2 Data Organization in #MongoDB #datamining

Lec 8.1 Intro to #NoSQL #database #datamining

Lec 8.1 Intro to #NoSQL #database #datamining

Lec 7.4 Intro to #AWS #QuickSight #datamining

Lec 7.4 Intro to #AWS #QuickSight #datamining

Lec 7.3 Data lake #datamining

Lec 7.3 Data lake #datamining

Lec 7.2 Data warehouse #datamining

Lec 7.2 Data warehouse #datamining

Lec 7.1 Cloud database #datamining

Lec 7.1 Cloud database #datamining

Lec 6.4 Nested Structure of #HTML #datamining

Lec 6.4 Nested Structure of #HTML #datamining

Lec 6.3 Inspect #HTML #datamining

Lec 6.3 Inspect #HTML #datamining

Lec 6.2 Parse #HTML #datamining

Lec 6.2 Parse #HTML #datamining

Lec 6.1 Prepare for #HTML extraction #datamining

Lec 6.1 Prepare for #HTML extraction #datamining

Lec 5.4 #HTML class, block and inline elements #datamining

Lec 5.4 #HTML class, block and inline elements #datamining

Lec 5.3 #HTML attributes, list and table #datamining

Lec 5.3 #HTML attributes, list and table #datamining

Lec 5.2 Basics of #HTML #datamining

Lec 5.2 Basics of #HTML #datamining

Lec 5.1 Web crawler and robots #datamining

Lec 5.1 Web crawler and robots #datamining

Lec 4.6 Modify table in #python #datamining

Lec 4.6 Modify table in #python #datamining

Lec 4.5 Use pandas to query and visualize data #datamining

Lec 4.5 Use pandas to query and visualize data #datamining

Lec 4.4 Connect to #database and query data #datamining

Lec 4.4 Connect to #database and query data #datamining

Lec 4.3 Create config file and import #python libraries #datamining

Lec 4.3 Create config file and import #python libraries #datamining

Lec 4.2 Review of #Python and #jupyternotebook #datamining

Lec 4.2 Review of #Python and #jupyternotebook #datamining

Lec 4.1 Setup up #jupyternotebook and #GitHub #datamining

Lec 4.1 Setup up #jupyternotebook and #GitHub #datamining

Lec 3.6 Index and View in #SQL #datamining

Lec 3.6 Index and View in #SQL #datamining

Lec 3.5 Modify data in #SQL #datamining

Lec 3.5 Modify data in #SQL #datamining

Lec 3.4 Group data in #SQL #datamining

Lec 3.4 Group data in #SQL #datamining

Lec 3.3 Join tables in #SQL #datamining

Lec 3.3 Join tables in #SQL #datamining

Lec 3.2 Filter data in #SQL #datamining

Lec 3.2 Filter data in #SQL #datamining

Lec 3.1 Introduction to #SQL #datamining

Lec 3.1 Introduction to #SQL #datamining

Lec 2.4 #database ER Model #datamining

Lec 2.4 #database ER Model #datamining

Lec 2.3 #database Relationship #datamining

Lec 2.3 #database Relationship #datamining

Lec 2.2 #database Normalization Rules #datamining

Lec 2.2 #database Normalization Rules #datamining

Lec 2.1 Intro to Relational #Database Design #datamining

Lec 2.1 Intro to Relational #Database Design #datamining

Lec 1.4 Key and Identifier #database #datamining

Lec 1.4 Key and Identifier #database #datamining

Lec 1.3 #PostgreSQL and Index #datamining

Lec 1.3 #PostgreSQL and Index #datamining

Lec 1.2 Introduction to Relational #Database #datamining

Lec 1.2 Introduction to Relational #Database #datamining

Lec 1.1 Introduction to Data Mining and Database

Lec 1.1 Introduction to Data Mining and Database

Intro to syllabus IA340 Fall2020 #datamining

Intro to syllabus IA340 Fall2020 #datamining

Lec 11.3 centrality measures and connected components #dataviz

Lec 11.3 centrality measures and connected components #dataviz

Lec 11.2 Degree centrality #dataviz

Lec 11.2 Degree centrality #dataviz

Demo of the LBSMA #GIS Tool

Demo of the LBSMA #GIS Tool

Install #Gephi #dataviz

Install #Gephi #dataviz

Lec 11.1 intro to network analysis #dataviz

Lec 11.1 intro to network analysis #dataviz

Lec 10.4 Design examples #dataviz

Lec 10.4 Design examples #dataviz

Lec 10.3 Design consideration #dataviz

Lec 10.3 Design consideration #dataviz

Lec 10.2 Typography 10 rules #dataviz

Lec 10.2 Typography 10 rules #dataviz

Lec 10.1 Intro to Typography #dataviz

Lec 10.1 Intro to Typography #dataviz

Lec 9.4 LOD advanced #tableau #dataviz

Lec 9.4 LOD advanced #tableau #dataviz

Lec 9.3 aggregate and LOD #tableau #dataviz

Lec 9.3 aggregate and LOD #tableau #dataviz

Lec 9.2 table calculation #tableau #dataviz

Lec 9.2 table calculation #tableau #dataviz

Lec 9.1 basic functions #tableau #dataviz

Lec 9.1 basic functions #tableau #dataviz

Lec 8.3 storytelling #dataviz

Lec 8.3 storytelling #dataviz

Lec 8.2 dashboard design principles #dataviz

Lec 8.2 dashboard design principles #dataviz

Lec 8.1 intro to dashboard design #dataviz

Lec 8.1 intro to dashboard design #dataviz

Lec 13.2 type of graphs #python

Lec 13.2 type of graphs #python

Lec 13.1 Intro to matplotlib #python

Lec 13.1 Intro to matplotlib #python

Lec 12.3 simple regression and sentiment analysis in #python

Lec 12.3 simple regression and sentiment analysis in #python

Lec 12.2 pandas dataframe #python

Lec 12.2 pandas dataframe #python

Lec 12.1 Intro to data analysis #python

Lec 12.1 Intro to data analysis #python

Lec 11.3 Read JSON data and data from website #python

Lec 11.3 Read JSON data and data from website #python

Lec 11.2 Counter, list comprehension and string format #python

Lec 11.2 Counter, list comprehension and string format #python

Lec 11.1 Read text files in #Python

Lec 11.1 Read text files in #Python

Lec 10.3 Markdown syntax #python

Lec 10.3 Markdown syntax #python

Lec 10.1 Intro to Jupyter Notebook #python

Lec 10.1 Intro to Jupyter Notebook #python

Lec 10.1Intro to data science #python

Lec 10.1Intro to data science #python

Lec 9.3 module, package and library #python

Lec 9.3 module, package and library #python

Lec 9.2 class argument and method #python

Lec 9.2 class argument and method #python

Lec 9.1 Define a class #python

Lec 9.1 Define a class #python

Lec 8.3 Incremental development #python

Lec 8.3 Incremental development #python

Lec 8.2 function exercises #python

Lec 8.2 function exercises #python