Linkedin Learning - Cisco Networking Foundations IP Addressing
English | Tutorial | Size: 224.51 MB

Linkedin Learning - Cisco CCT Routing and Switching 100-490 Cert Prep 1 General Networking
English | Tutorial | Size: 2.07 GB

Free Download AWS Certified Machine Learning - Specialty (MLS-C01) Cert Prep: 1 Data Engineering
Released 2/2023
MP4 | Video h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 1h 29m | Size: 231 MB
Join MLOps expert and CTO Noah Gift to learn all about data engineering and prepare for the data engineering portion of the AWS Certified Machine Learning – Specialty (MLS-C01) certification. After walking you through the technology prerequisites and the tools you will use, Noah dives into creating data repositories, identifying and implementing data ingestion and transformation solutions, designing and implementing data warehousing solutions, and enabling data access and discovery. He also presents practical demos of AWS Batch, step functions, EMR Spark, and more.

Linkedin Learning Cloud Computing Observability and Operations
English | Tutorial | Size: 330.04 MB

Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.
Duration: 1h 16m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 191 MB
Level: Intermediate | Genre: eLearning | Language: English
Free Download Machine Learning and AI Foundations: Decision Trees with SPSS

Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.
Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more.
All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.
Duration: 3h 22m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 532 MB

Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.
Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Duration: 3h 57m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 627 MB
Level: Intermediate | Genre: eLearning | Language: English

Face recognition is used for everything from automatically tagging pictures to unlocking cell phones. And with recent advancements in deep learning, the accuracy of face recognition has improved. In this course, learn how to develop a face recognition system that can detect faces in images, identify the faces, and even modify faces with "digital makeup" like you've experienced in popular mobile apps. Find out how to set up a development environment. Discover tools you can leverage for face recognition. See how a machine learning model can be trained to analyze images and identify facial landmarks. Learn the steps involved in coding facial feature detection, representing a face as a set of measurements, and encoding faces. Additionally, learn how to repurpose and adjust pre-existing systems.
Duration: 1h 25m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 284 MB
Level: Intermediate | Genre: eLearning | Language: English
Free Download Linkedin - Deep Learning: Face Recognition

javascript developers can use the TensorFlow framework to create a machine learning (ML) project. This course introduces you to ML basics, and demonstrates how to set up and use TensorFlow to train a model and generate live results. Emmanuel Henri shows how to create a new project; how to work with different tensor types, variables, models, and layers; how to import a project and explore datasets; how TensorFlow executes model training; how to convert a saved model for the web; and more.
Duration: 57m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 162 MB
Level: Intermediate | Genre: eLearning | Language: English
Free Download Learning TensorFlow with javascript

TensorFlow is one of the most popular deep learning frameworks available. It's used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model. Finally, he explains how to deploy models locally or in the cloud. When you wrap up this course, you'll be ready to start building and deploying your own models with TensorFlow.
Duration: 1h 46m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 274 MB
Level: Intermediate | Genre: eLearning | Language: English
Free Download Building and Deploying Deep Learning Applications with TensorFlow