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Deep Learning Model Deployment
Duration: 1h 9m | .MP4 1920×1080 30 fps(r) | AAC, 48000 Hz, 2ch | 168.76 MB
Genre: eLearning | Language: English​

Turn your trained models into production services. This course will teach you how to deploy, serve, and monitor deep learning models in real-world applications.

More Info:
Deep Learning Model Deployment

Deep Learning : Convolutional Neural Networks With Python
Published 3/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.41 GB | Duration: 4h 13m​

Deep Learning and Computer Vision using Convolutional Neural Networks with Python, Pytorch. Train, Test, Deploy Models

What you’ll learn

Deep Convolutional Neural Networks with Python and Pytorch Basics to Expert

Introduction to Deep Learning and its Building Blocks Artificial Neurons

Define Convolutional Neural Network Architecture from Scratch with Python and Pytorch

Hyperparameters Optimization For Convolutional Neural Networks to Improve Model Performance

Custom Datasets with Augmentations to Increase Image Data Variability

Training and Testing Convolutional Neural Network using Pytorch

Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Advanced CNNs for Segmentation, Object tracking, and Pose Estimation.

Pretrained Convolutional Neural Networks and their Applications

Transfer Learning using Convolutional Neural Networks Models

Convolutional Neural Networks Encoder Decoder Architectures

YOLO Convolutional Neural Networks for Computer Vision Tasks

Region-based Convolutional Neural Networks for Object Detection

Requirements

A Google Gmail account is required to get started with Google Colab to write Python Code

Python Programming experience is an advantage but not required

Description

Are you ready to unlock the power of deep learning and revolutionize your career? Dive into the captivating realm of Deep Learning with our comprehensive course Deep Learning: Convolutional Neural Networks (CNNs) using Python and Pytorch. Discover the power and versatility of CNNs, a cutting-edge technology revolutionizing the field of artificial intelligence. With hands-on Python tutorials, you’ll unravel the intricacies of CNN architectures, mastering their design, implementation, and optimization. One of the key advantages of deep CNN is its ability to automatically learn features at different levels of abstraction. Lower layers of the network learn low-level features, such as edges or textures, while higher layers learn more complex and abstract features. This hierarchical representation allows deep learning models to capture and understand complex patterns in the data, enabling them to excel in tasks such as image recognition, natural language processing, speech recognition, and many others.Introducing our comprehensive deep CNNs with python course, where you’ll dive deep into Convolutional Neural Networks and emerge with the skills you need to succeed in the modern era of AI. Computer Vision refers to AI algorithms designed to extract knowledge from images or videos. Computer vision is a field of artificial intelligence (AI) that enables computers to understand and interpret visual information from digital images or videos. It involves developing deep learning algorithms and techniques that allow machines to analyze, process, and extract meaningful insights from visual data, much like the human visual system. Convolutional Neural Networks (CNNs) are most commonly used Deep Learning technique for computer vision tasks. CNNs are well-suited for processing grid-like input data, such as images, due to their ability to capture spatial hierarchies and local patterns.In today’s data-driven world, Convolutional Neural Networks stand at the forefront of image recognition, object detection, and visual understanding tasks. Understanding CNNs is not only essential for aspiring data scientists and machine learning engineers but also for professionals seeking to leverage state-of-the-art technology to drive innovation in various domains. From self-driving cars and medical imaging to facial recognition and augmented reality, CNNs find applications across diverse industries. Whether you’re interested in revolutionizing healthcare, enhancing autonomous systems, or developing cutting-edge computer vision applications, this course equips you with the knowledge and skills to excel in any CNN-related endeavor.Course Key Learning Outcomeseep Convolutional Neural Networks with Python and Pytorch Basics to ExpertIntroduction to Deep Learning and its Building Blocks Artificial NeuronsDefine Convolutional Neural Network Architecture from Scratch with Python and PytorchHyperparameters Optimization For Convolutional Neural Networks to Improve Model PerformanceCustom Datasets with Augmentations to Increase Image Data VariabilityTraining and Testing Convolutional Neural Network using PytorchPerformance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNsVisualize Confusion Matrix and Calculate Precision, Recall, and F1 ScoreAdvanced CNNs for Segmentation, Object tracking, and Pose Estimation.Pretrained Convolutional Neural Networks and their ApplicationsTransfer Learning using Convolutional Neural Networks ModelsConvolutional Neural Networks Encoder Decoder ArchitecturesYOLO Convolutional Neural Networks for Computer Vision TasksRegion-based Convolutional Neural Networks for Object DetectionIn this comprehensive course you will start from building Deep Convolutional Neural Networks architecture from scratch with Dataset Augmentation with different transformations to increase image variability , HyperParameteres Optimization before training the model to improve performance, Model validation on Test Images, Performance metrics calculation including Accuracy, Precision, Recall, F1 score and Confusion matrix visualization to see detailed insights into the model’s performance, beyond simple metrics. Then you will move forward to advanced CNN Architectures Including RESNT, ALEXNET for Images Classification, UNET, PSPNET encoder decoder Architectures for semantic segmentation, Region based CNN for OD and YOLO CNNs for real time object Detection, classification instance segmentation, object tracking, and pose estimation.Join us on this exciting journey, where you’ll not only grasp the core concepts but also unlock the door to advanced CNN architectures, equipping yourself with the skills needed to conquer the most challenging computer vision tasks with confidence and expertise. You will follow a complete pipeline to deep dive into CNN for real world applications. I will provide you the complete python code to build, train, test, and deploy CNN from scratch for different Artificial Intelligence tasks.Don’t miss out on this incredible opportunity to take your skills to the next level. Enroll now and join the thousands of students who’ve already transformed their careers with our courses. " Thank you and see you inside the class" !

Overview

Section 1: Introduction to Course

Lecture 1 Introduction

Section 2: Artificial Neurons – The building blocks of Deep Learning

Lecture 2 Introduction to Deep Learning and Artificial Neurons

Section 3: Introduction to Convolutional Neural Networks (CNNs)

Lecture 3 Introduction to Convolutional Neural Networks (CNNs)

Section 4: Google Colab Environment Set-up for Writing Python Code

Lecture 4 Google Colab Environment for Writing Python and Pytorch Code

Section 5: Convolutional Neural Networks from Scratch using Python

Lecture 5 Define Convolutional Neural Network Architecture from Scratch using Python

Section 6: Dataset and its Augmentation

Lecture 6 Dataset and its Augmentation

Section 7: Hyperparameters Optimization For Convolutional Neural Networks

Lecture 7 Hyperparameters Optimization For Training Models

Section 8: Training Convolutional Neural Network from Scratch

Lecture 8 Training Convolutional Neural Network from Scratch

Section 9: Validating Convolutional Neural Network on Test Images

Lecture 9 Validating Convolutional Neural Network on Test Images

Section 10: Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Lecture 10 Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Section 11: Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Lecture 11 Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Section 12: Resources: Python Code for Convolutional Neural Networks from Scratch

Lecture 12 Resources: Python Code for Convolutional Neural Networks from Scratch

Section 13: Pretrained Convolutional Neural Networks

Lecture 13 Pretrained Convolutional Neural Networks with Python

Lecture 14 Python Code to use the Pretrained CNN Models

Section 14: Transfer Learning using Convolutional Neural Networks

Lecture 15 What is Transfer Learning

Lecture 16 Transfer Learning by Fine Tuning CNNs Models

Lecture 17 Transfer Learning with CNNs Models as Fixed Feature Extractor

Lecture 18 Transfer Learning Python, Pytorch Code and Dataset

Section 15: Convolutional Neural Networks Encoder Decoder Architectures

Lecture 19 Convolutional Neural Networks Based Encoders

Lecture 20 Convolutional Neural Networks Based Decoders

Lecture 21 Multi-Task Contextual Encoder Decoder Network

Section 16: YOLO Convolutional Neural Networks

Lecture 22 YOLO Convolutional Neural Networks Architecture

Lecture 23 How YOLO Works to Identify Objects

Section 17: Region-based Convolutional Neural Networks

Lecture 24 Region-based Convolutional Neural Networks (RCNN, FAST RCNN, FASTER RCNN)

Lecture 25 Detectron2 for Ojbect Detection with PyTorch

Lecture 26 Perform Object Detection using Detectron2 Models

Lecture 27 Resources: Python and PyTorch Code for Object Detection

This course is designed for individuals with a keen interest in Deep Learning and Convolutional Neural Networks (CNNs) with Python and Pytorch to solve Real-World AI Problems.,Whether you’re a beginner looking to build a strong foundation in Computer Vision, Object Tracking, Segmentation, Pose Estimation, Classification, Object Detection or an experienced professional aiming to enhance your skills, this course provides valuable insights and hands-on experience with CNNs.

Deep Learning – Recurrent Neural Networks with TensorFlow
English | 2023 | h264, yuv420p, 1920×1080 | 48000 Hz, 2channels | Duration: 4h 6m | 729 MB​

Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions.
TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs.
In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will apply LSTMs to stock "price" predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do and how not to make the same mistakes that most blogs and courses make when predicting stocks.
By the end of this course, you will be able to build your own build RNNs with TensorFlow 2.
What You Will Learn
Learn about simple RNNs (Elman unit)
Covers GRU (gated recurrent unit)
Learn how to use LSTM (long short-term memory unit)
Learn how to preform time series forecasting
Learn how to predict stock price and stock return with LSTM
Learn how to apply RNNs to NLP
Audience
This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.

Debugging and Error Handling in ASP.NET Core 10
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 51m | Size: 296 MB​

This course will teach you how to leverage error handling and debugging skills to make your code comprehensible and resilient.

What you’ll learn
You won’t get very far working with ASP.NET before you’ll have to plan for trouble and use tools to track problems down. In this course, Debugging and Error Handling in ASP.NET Core 10, you’ll gain the ability to debug effectively and how to structure your code to handle problems. First, you’ll explore debugging with an attached debugger. Next, you’ll discover troubleshooting your end-user interface with client-side debugging. Finally, you’ll learn how to use the ASP.NET Core middleware pipeline to automate your error handling. When you’re finished with this course, you’ll have the skills and knowledge of debugging and error handling needed to make your code comprehensible and resilient.

Free Download Debating 101 Master the Art of Arguing, Rebuttal & Winning
Published 4/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 16m | Size: 1.25 GB
Think Fast, Speak Smart, Win Arguments with Confidence, Logic, and Powerful Rebuttals!

What you’ll learn
Build clear, structured, and persuasive arguments from scratch
Understand the fundamentals of logic, reasoning, and critical thinking
Deliver confident and impactful speeches in debates
Master the art of crafting powerful rebuttals in real time
Analyze opponents’ arguments and find weaknesses
Adapt debating style for different audiences and situations
understand different debate formats and rules
Handle objections and counterarguments with confidence
Build credibility and authority while speaking
Improve body language, tone, and delivery for maximum impact
Apply debating skills in real-life situations (work, school, social settings)
Build confidence in expressing opinions clearly and logically
Turn complex ideas into simple, convincing points
Develop lifelong communication and critical thinking skills
Develop quick-thinking skills for impromptu debates
Requirements
No Prerequisites Required
Description
This course contains the use of artificial Intelligence.
Debating 101: Master the Art of Arguing, Rebuttal & Winning is a practical, step-by-step course designed to help you become a confident, clear, and persuasive communicator. Whether you are a beginner or someone looking to sharpen your speaking skills, this course will teach you how to think critically, structure powerful arguments, and respond effectively in any discussion.
You will learn how to break down complex ideas into simple, convincing points, use logic and evidence to support your views, and identify weaknesses in opposing arguments. The course also focuses heavily on rebuttal techniques, helping you think on your feet and respond with clarity and confidence under pressure.
Beyond theory, this course is highly practical. You will explore real-world examples, proven frameworks, and actionable techniques that can be applied in debates, workplace discussions, interviews, negotiations, and everyday conversations. You will also develop essential skills such as public speaking, active listening, persuasion, and critical thinking.
By the end of this course, you will be able to present your ideas with confidence, challenge others respectfully, and handle any argument with calmness and control. You will not just learn how to debate, but how to communicate effectively, influence others, and stand out in any conversation.
Who this course is for
Anyone who wants to learn how to win arguments flawlessly

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Davinci Resolve 20 Masterclass
Published 9/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.99 GB | Duration: 4h 56m​

The Complete Video Editing & Color Grading Course

What you’ll learn

Project Setup & Media Management

Editing Essentials: Navigate the Edit Page, cut and trim clips, work with multiple timelines, and use dynamic project switching

Transitions, Effects & Keyframes

Titles, Subtitles & Text Animation

Audio Editing & AI Tools

Color Correction & Grading

Fusion Visual Effects

Exporting for Every Platform

Requirements

No prior video editing experience

Description

DaVinci Resolve 20 Masterclass – The Complete Video Editing & Color Grading CourseUnlock the full potential of DaVinci Resolve 20, the industry-leading free editing software used by filmmakers, YouTubers, and content creators worldwide. Whether you are just starting your editing journey or looking to refine your professional workflow, this step-by-step masterclass will take you from beginner to advanced editor.What You Will LearnProject Setup & Media Management: Import footage, organize projects efficiently, and use smart bins and power bins for faster editing.Editing Essentials: Navigate the Edit Page, cut and trim clips, work with multiple timelines, and use dynamic project switching.Transitions, Effects & Keyframes: Create smooth edits with speed ramping, stabilization, lens correction, and advanced keyframing techniques.Titles, Subtitles & Text Animation: Build cinematic titles, animated captions, and designer subtitles with the latest multi-text tools.Audio Editing & AI Tools: Clean audio with AI Voice Isolation, balance dialogue, remix music with AI, and explore advanced Fairlight features.Color Correction & Grading: Use scopes, node trees, LUTs, vignetting, film grain, and noise reduction to achieve cinematic visuals.Fusion Visual Effects: Apply green screen keying, motion graphics, and map animations for professional visual effects.Exporting for Every Platform: Deliver projects in the best formats for YouTube, TikTok, Instagram, and client work.Who This Course Is ForBeginners who want a clear, step-by-step introduction to video editing.Intermediate creators ready to level up with advanced editing and grading techniques.Professionals aiming to unlock the full potential of DaVinci Resolve 20.Why Enroll in This MasterclassWith 48 focused chapters, this course takes you through the complete post-production workflow: editing, effects, audio, color grading, and exporting. As an online teacher having taught more than 50,000 students worldwide, I bring a practical, hands-on approach that ensures you finish the course with real, applicable skills.What You NeedA computer with DaVinci Resolve 20 (Free or Studio version).Either the provided sample footage or your own video clips to practice with.Get Started TodayTransform your footage into cinematic videos and refine your creative workflow. Whether you are working on your very first project or producing professional client work, this course is designed to help you master DaVinci Resolve 20 with confidence.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Class Overview

Section 2: User Interface

Lecture 3 Project Settings

Lecture 4 User Interface

Lecture 5 Media Page Introduction

Section 3: Edit Page

Lecture 6 Edit Page Introduction

Lecture 7 How to Use Shortcuts

Lecture 8 Editing Video Sequence – Part 1

Lecture 9 Editing Video Sequence – Part 2

Lecture 10 Inspector Panel – Video

Lecture 11 Inspector Panel – Audio

Lecture 12 Keyframing

Lecture 13 Video Effects

Lecture 14 Compound Clips

Lecture 15 Voice Over on Timeline

Lecture 16 Titles

Lecture 17 Titles And Animations

Lecture 18 Proxy Files

Lecture 19 Timeline Organisation

Lecture 20 AI Audio Features

Lecture 21 Subtitles

Lecture 22 AI Audio Transcription

Section 4: Fairlight Page

Lecture 23 Fairlight Page Introduction

Section 5: Color Grading in DaVinci Resolve

Lecture 24 Color Grading Page Overview

Lecture 25 What is a Node Tree

Lecture 0 Color Corrections Basics

Lecture 26 Color Grading using Custom Curves

Lecture 0 Qualifier Tool

Lecture 27 Color Slice Tool

Lecture 28 Windows Tool

Lecture 29 Keyframing in Color Grading

Lecture 30 Color Grading Using LUTs

Lecture 0 Log Video Color Grading

Lecture 31 Film Look LUTs

Lecture 32 Powergrade Tool

Lecture 0 Magic Mask

Lecture 33 Halation Tool

Lecture 34 Noise Reduction

Lecture 35 Film Grain

Section 6: Fusion Page

Lecture 36 Fusion Page Overview

Lecture 0 Attach Text To Objects

Lecture 37 Locked On Stablisation

Lecture 0 Attach Text On A Layer

Lecture 38 Green Screen Effect

Lecture 0 Travel Map Animation

Lecture 0 Cutout Effect

Lecture 0 Best Export Settings

Lecture 39 Thank you

Video editors,Content Creators,Youtubers,Filmmakers,Videographers

Free Download Databricks for Data Analysts
Released 4/2026
With Baraa Khatib Salkini
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 31m | Size: 317 MB

Learn how to use Databricks as a data analyst, from Databricks SQL exploration to dashboards, KPIs, and Genie AI.
Course details
Databricks is becoming a core platform for modern analytics, but many data analysts are unsure how to use it in daily work. This course introduces Databricks from a data analyst perspective, based on real industry experience and real company workflows. You will learn Databricks SQL, practical analysis patterns, and how to build trusted KPIs using clear, easy-to-follow animated sketches that explain concepts step by step. The course covers navigating the Databricks workspace, querying curated tables, building dashboards, and understanding how Databricks fits alongside Power BI. You will also see how Genie AI can accelerate analysis while keeping results accurate and explainable.
Skills covered
Azure Databricks, Data Analysis
Homepage

Databricks for Data Analysts Online Class | LinkedIn Learning, formerly Lynda.com
Learn how to use Databricks as a data analyst, from Databricks SQL exploration to dashboards, KPIs, and Genie AI.

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Databricks Certified Data Engineer Associate – Bootcamp
Published 7/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.71 GB | Duration: 15h 43m​

Databricks Data Engineer [2025] | Strong Hands-on for Databricks Certified Data Engineer Associate Certification Exam

What you’ll learn

All the important topics you need to PASS the Certification – with deep hands-on practice

Understand key concepts like Lakehouse Federation, Lakeflow Connect, and the Medallion Architecture

Strong hands-on with Unity Catalog, Volumes, Metastore, Catalog UDFs, and utils

PySpark Big Data Crash Course – from basics to real-world use cases

Master Spark Structured Streaming using Auto Loader for INCREMENTAL real-time data ingestion

Learn the complete Delta Lake Architecture, its benefits, and how to implement & tune it for performance

Deploy and manage Databricks SQL Warehouses with parameterized queries, alerts, and query caching

Build streaming pipelines using Streaming Tables, Materialized Views, and Lakeflow Declarative Pipelines

Implement Slowly Changing Dimensions (SCDs) and add Data Quality checks using Delta Live Tables

Master Lakeflow Jobs to orchestrate your ETL pipelines like a pro

Understand and apply Row-Level Security, Data Masking, and Delta Sharing for secure data access

Learn Data Versioning, Time Travel, ZORDERING, Cloning, and Liquid Clustering

Requirements

Basic SQL knowledge will be required

Basic Python programming knowledge will be required

No DATABRICKS knowledge is required – Everything is covered from SCRATCH

Description

Are you ready to become a DATABRICKS DATA ENGINEER?Whether you’re a beginner or a working professional who wants to level up, this course will guide you step by step with a hands-on, practical, and engaging approach.GAIN STRONG HANDS-ON WITH:Lakehouse Architecture, Lakehouse Federation, and Lakeflow Connect – Understand how Databricks handles structured and unstructured data, and how Lakehouse Federation lets you query external sources seamlessly.Unity Catalog, Metastore, Volumes, and UDFs – Learn how to manage data, permissions, and catalogs efficiently using Databricks’ built-in governance features.PySpark for Big Data – Master PySpark with real use cases, transformations, actions, joins, and more – all from a Data Engineer’s point of view.Structured Streaming + Autoloader – Build real-time pipelines using Spark Streaming and learn how Autoloader handles files in cloud storage.Delta Lake Architecture – Dive deep into Delta’s features like ACID transactions, time travel, schema evolution, and performance tuning.Databricks SQL Warehouses – Learn how to write parameterized queries, schedule dashboards, and set alerts using SQL Warehousing.LakeFlow Declarative Pipelines – Work with Streaming Tables, Materialized Views, and build low-code data pipelines.Delta Live Tables (DLT) – Build robust pipelines with SCD implementation, data quality checks, expectations, and monitoring.Orchestrate ETL with LakeFlow Jobs – Schedule, monitor, and manage your pipelines using LakeFlow Jobs end-to-end.Security and Sharing – Apply row-level security, data masking, and explore Delta Sharing for secure and scalable collaboration.What Makes This Course Different?Super Engaging Lectures – No boring theory here! I explain every concept in a clear and beginner-friendly way using real-life examples and visuals.Deep Dive into Every Topic – I don’t just scratch the surface. You’ll understand the "why" and "how" behind every feature .Strong Hands-On Focus – Learn by doing! From pipelines to notebooks to warehouse, you’ll build real solutions step-by-step, just like a Databricks Data Engineer does.DISCLAIMER : This course is independently created and not affiliated with or endorsed by Databricks Inc. All content, including explanations and practice materials, is original and intended solely for educational purposes. It does not include any actual certification exam questions and is based on publicly available documentation, real-world scenarios, and personal experience. Product names, logos, and trademarks used are the property of their respective owners and are included only for identification and learning. Always refer to official Databricks documentation for the latest and most accurate information.

Overview

Section 1: Course Introduction & Resources

Lecture 1 Introduction

Lecture 2 Disclaimer

Lecture 3 Resources – Zipped_Folder (Main)

Lecture 4 Resources – Raw Data

Lecture 5 Resources – Course Slides

Lecture 6 Resources – Scripts, Notebook, & Files

Lecture 7 Resources – Databricks Source Code

Section 2: Lakehouse Core Fundamentals

Lecture 8 What is Lakehouse?

Lecture 9 Delta Lake – Backbone of Lakehouse

Lecture 10 What is Databricks? & Why Databricks?

Lecture 11 Databricks Architecture

Lecture 12 What is Medallion Architecture?

Section 3: Get Started With Databricks

Lecture 13 Databricks Free Edition – Newly Announced

Lecture 14 Databricks Overview

Lecture 15 Refer Resources Within Databricks

Lecture 16 Databricks Compute – All The Types

Section 4: Unity Catalog – The Complete Guide

Lecture 17 What is Unity Catalog?

Lecture 18 Evolution With Unity Catalog

Lecture 19 Managed VS External Tables

Lecture 20 Unity Catalog Volumes

Lecture 21 Managed and External Volumes

Lecture 22 DBUTILS – Databricks OS Module

Section 5: Big Data With Apache Spark

Lecture 23 PySpark Transformations – Silver Layer

Lecture 24 Structured Queries With SparkSQL

Lecture 25 PySpark UDF For Custom Functions

Section 6: Databricks Lakehouse Foreign Objects

Lecture 26 Work with Databricks Files

Lecture 27 Databricks Lakehouse Federation

Lecture 28 Foreign Catalogs and Foreign Tables

Section 7: AUTOLOADER – Spark Structured Streaming

Lecture 29 What is Spark Streaming?

Lecture 30 Autoloader – what’s that?

Lecture 31 Autoloader Complete Architecture

Lecture 32 The power of Autoloader

Lecture 33 Idempotency with Autoloader

Lecture 34 Schema Evolution with Autoloader – Rescued Data

Lecture 35 Schema Evolution with Autoloader – AddNewColumns

Lecture 36 Incremental Load with COPY INTO

Lecture 37 The power of COPY INTO

Section 8: Databricks SQL Warehouse

Lecture 38 Databricks SQL : a sneak-peek

Lecture 39 What is Databricks SQL Warehouse?

Lecture 40 Databricks SQL Editor

Lecture 41 Parametrized Queries in Databricks SQL

Lecture 42 SQL Query Snippets – Reuse your code

Lecture 43 Common Table Expressions – CTEs

Lecture 44 Query Scheduling

Lecture 45 Query Profile for Monitoring

Lecture 46 Query Cache – Boost Query Performance

Lecture 47 SQL Alerts – Triggers

Lecture 48 Databricks Genie

Lecture 49 Databricks SQL Dashboards

Section 9: Orchestration With Lakeflow Jobs

Lecture 50 What are Lakeflow Jobs?

Lecture 51 Build your first Lakeflow Job

Lecture 52 Conditionals in Jobs

Lecture 53 Control Flow using ForEach Task

Lecture 54 Dynamic Value Reference and Tasks Values

Lecture 55 Quota Limit Exceed Error

Lecture 56 Set and Get Values in Jobs

Lecture 57 SQL rows as output

Lecture 58 SQL First Row as output

Lecture 59 ForEach with SQL Rows

Lecture 60 Passing Large Array With Notebook

Lecture 61 Large Array With SQL Table

Lecture 62 Schedules and Triggers

Lecture 63 Computes For Jobs

Lecture 64 Jobs Monitoring

Lecture 65 Jobs Notification

Section 10: DELTA LIVE TABLES – Lakeflow Declarative Pipelines

Lecture 66 What is DLT – Delta Live Tables?

Lecture 67 DLT Code Editor – New Environment

Lecture 68 Create Streaming Table

Lecture 69 Create Materialized View

Lecture 70 Create Streaming Views

Lecture 71 Build your first DLT pipeline

Lecture 72 Delta Live Table with Autoloader

Lecture 73 DLT Append Flow API

Lecture 74 DLT Auto CDC Flow API

Lecture 75 Slowly Changing Dimension Type 1

Lecture 76 Slowly Changing Dimension Type 2

Lecture 77 Data Quality with DLT Expectations

Lecture 78 Filter out Corrupted Data

Lecture 79 Parametrize your DLT Pipelines

Lecture 80 DLT End-To-End Project

Lecture 81 DLT Pipeline Modes

Lecture 82 Orchestrate your DLT Pipelines

Lecture 83 Monitor your DLT pipelines

Section 11: Unity Catalog User Defined Functions

Lecture 84 What are Unity Catalog Functions?

Lecture 85 What are Scalar Functions?

Lecture 86 Scalar Functions with SQL

Lecture 87 Scalar Functions with Python

Lecture 88 User Defined Table Functions – UDTFs

Lecture 89 Create custom UDTFs

Section 12: Data Access Control and Governance

Lecture 90 Why do we need Data Governance?

Lecture 91 Data Discoverability

Lecture 92 Track Data Quality

Lecture 93 Share your Data with Delta Sharing

Lecture 94 Data Access Control – Non negotiable

Lecture 95 Dynamic Data Masking

Lecture 96 Row Level Security – RLS

Section 13: Build Databricks Applications

Lecture 97 What is Databricks Apps?

Lecture 98 Get started with your first Databricks App

Section 14: Delta Lake Optimization

Lecture 99 Let’s talk about this

Lecture 100 OPTIMIZE Command – Coalesce partitions

Lecture 101 ZORDERING – Collocate your data

Lecture 102 Liquid Clustering – Say Good Bye To Partitions

Lecture 103 Data Versioning with Delta Lake

Lecture 104 Time Traveling with Delta Lake – UNDO

Lecture 105 VACUUM Command – Clear the MESS

Lecture 106 CTAS – Copy Table As Select

Lecture 107 Deep Clone Your Data

Lecture 108 Shallow Clone is also GOOD

Section 15: YOUR NEXT STEPS

Lecture 109 All The Best

Anyone who WANTS to become a DATABRICKS DATA ENGINEER

Free Download Data-Driven Maintenance Using Excel and CMMS Data
Published 4/2026
Created by Tim Chui
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 11 Lectures ( 3h 5m ) | Size: 4.37 GB

Analyzing your maintenance and CMMS Work Order Data Using Excel
What you’ll learn
✓ Learn how to turn raw maintenance and CMMS data into meaningful insights
✓ Learn some key maintenance matrices or KPI
✓ Learn how to use Excel Pivot Table to transform your maintenance data
✓ Learn to build your own maintenance performance dashboards
✓ You will be able to make smarter, data-backed maintenance decisions
Requirements
● Some knowledge in maintenance management system and some basics in Microsoft Excel
Description
Many maintenance teams today collect large amounts of data-whether through manual logs, Excel spreadsheets, or Computerized Maintenance Management Systems (CMMS). However, this data is often underutilized. Standard CMMS reports may not provide the specific insights needed, leaving maintenance personnel unable to fully analyze equipment performance, downtime trends, or failure patterns.
This course, Data-Driven Maintenance Using Excel and CMMS Data, is designed to bridge that gap. It equips participants with practical skills to transform raw maintenance data into meaningful insights using Microsoft Excel, especially Pivot Tables. Instead of relying solely on predefined CMMS reports, learners will gain the ability to customize their own analysis based on operational needs. This will maximize the use of your data to enhance decision making and performance management in your maintenance management system.
By the end of the course, participants will be able to organize and clean CMMS data, analyze equipment downtime, calculate key performance indicators such as MTBF (Mean Time Between Failure) and MTTR (Mean Time to Repair), and perform failure mode analysis. They will also learn how to build simple, management-friendly dashboards to support data-driven decision-making.
The course emphasizes hands-on learning with real-world datasets, enabling participants to apply techniques immediately in their workplace and improve maintenance performance effectively.
Who this course is for
■ Maintenance professionals who want to maximize their maintenance data
Homepage
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https://www.udemy.com/course/data-driven-maintenance-using-excel-and-cmms-data

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