NulledCodeSolutions.online

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
Code:Copy to clipboard

https://www.udemy.com/course/data-driven-maintenance-using-excel-and-cmms-data

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Data Science: Supervised Machine Learning in Python
Last updated 11/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.05 GB | Duration: 6h 24m​

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

What you’ll learn
Understand and implement K-Nearest Neighbors in Python
Understand the limitations of KNN
User KNN to solve several binary and multiclass classification problems
Understand and implement Naive Bayes and General Bayes Classifiers in Python
Understand the limitations of Bayes Classifiers
Understand and implement a Decision Tree in Python
Understand and implement the Perceptron in Python
Understand the limitations of the Perceptron
Understand hyperparameters and how to apply cross-validation
Understand the concepts of feature extraction and feature selection
Understand the pros and cons between classic machine learning methods and deep learning
Use Sci-Kit Learn
Implement a machine learning web service

Requirements
Python, Numpy, and Pandas experience
Probability and statistics (Gaussian distribution)
Strong ability to write algorithms

Description
In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what’s going to drive innovation in the coming years. It’s embedded into all sorts of different products.Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.It’s important to know both the advantages and disadvantages of each algorithm we look at.Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.We’ll do a comparison with deep learning so you understand the pros and cons of each approach.We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about "remembering facts", it’s about "seeing for yourself" via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."If you can’t implement it, you don’t understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times.Suggested Prerequisites:calculus (for some parts)probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)Python coding: if/else, loops, lists, dicts, setsNumpy, Scipy, MatplotlibWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail – email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math – get important details about algorithms that other courses leave out

Data Science: Deep Learning and Neural Networks in Python
Duration: 12h 8m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 3.83 GB
Genre: eLearning | Language: English​

The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code
What you’ll learn

• Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
• Learn how a neural network is built from basic building blocks (the neuron)
• Code a neural network from scratch in Python and numpy
• Code a neural network using Google’s TensorFlow
• Describe different types of neural networks and the different types of problems they are used for
• Derive the backpropagation rule from first principles
• Create a neural network with an output that has K > 2 classes using softmax
• Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
• Install TensorFlow
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Requirements

• Basic math (calculus derivatives, matrix arithmetic, probability)
• Install Numpy and Python
• Don’t worry about installing TensorFlow, we will do that in the lectures.
• Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Description
Ever wondered how AI technologies likeOpenAIChatGPT,GPT-4,DALL-E,Midjourney, andStable Diffusionreally work? In this course, you will learn the foundations of these groundbreaking applications.

This course will get you started in building your FIRSTartificial neural networkusingdeep learningtechniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google’s new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested inmachine learninganddata sciencein general. We go beyond basic models like logistic regression and linear regression and I show you something thatautomatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we’ll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone’s emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks – slightly modified architectures and what they are used for.

NOTE:

If youalreadyknow about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic,Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such asConvolutional Neural Networks,Restricted Boltzmann Machines,Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about "remembering facts", it’s about"seeing for yourself" via experimentation. It will teach you how to visualize what’s happening in the model internally. If you wantmorethan just a superficial look at machine learning models, this course is for you.

"If you can’t implement it, you don’t understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times.

Suggested Prerequisites:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Be familiar with basic linear models such as linear regression and logistic regression

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

• Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course
• Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

More Info

Data Science Methods and Techniques [2026]
2026-01-02
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 11.18 GB | Duration: 21h 45m​

Learn Data Science Methods and Techniques for Data Analysis and Machine Learning [2026]

What you’ll learn
Knowledge about Data Science methods, techniques, theory, best practices, and tasks
Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction models and automatic model creation
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources

Requirements
Basic knowledge of the Python programming language and preferably the Pandas library
The four ways of counting (+-/)
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection

Description
Welcome to the course Data Science Methods and Techniques for Data Analysis and Machine Learning!Data Science is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Data Science Methods and Techniques to develop and optimize all aspects of our lives, businesses, societies, governments, and states.This course will teach you a large selection of Data Science methods and techniques, which will give you an excellent foundation for Data Science jobs and studies. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineer.This is a three-in-one master class video course which will teach you to master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and Unsupervised Learning.You will learn to master Regression, Regression analysis, Prediction and supervised learning. This course has the most complete and fundamental master-level regression content packages on , with hands-on, useful practical theory, and also automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.You will learn to master Classification and supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifiers Ensembles and Voting Classifier Ensembles.You will learn to master Cluster Analysis and unsupervised learning. This part of the course is about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and some useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.You will learnKnowledge about Data Science methods, techniques, theory, best practices, and tasksDeep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidenceDetailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised LearningHands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python librariesAdvanced knowledge of A.I. prediction models and automatic model creationCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resourcesOption: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work lifeAnd much more.This course includesan easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this coursean easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Data Science or coding taskcontent that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineera large collection of unique content, and this course will teach you many new things that only can be learned from this course on a course structure built on a proven and professional framework for learning.a compact course structure and no killing timeThis course is an excellent way to learn to master Regression, Prediction, Classification, and Cluster analysis!These are the most important and useful tools for modeling, AI, and forecasting.Is this course for you?This course is for you, regardless if you are a beginner or an experienced Data ScientistThis course is for you, regardless if you have a Ph.D. or no education or experience at allThis course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and unsupervised learning.Course requirementsBasic knowledge of the Python programming language and preferably the Pandas libraryThe four ways of counting (+-/)Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedEnroll now to receive 15+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!

Who this course is for:
This course is for you, regardless if you are a beginner or an experienced Data Scientist, This course is for you, regardless if you have a Ph.D. or no education or experience at all

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