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This Edureka SQL Full Course video will cover all the topics of Structured Query Language (SQL) starting from scratch. This SQL tutorial for beginners is great for beginners who want to learn SQL and for professionals who want to brush up on their SQL skills. The following topics are covered in this SQL Full Course Tutorial:
00:00:00 Introduction
00:02:23 Agenda
00:02:35 What is SQL
00:16:32 Data & Database
00:23:58 Basic SQL Queries
00:50:51 Normalization in SQL
01:01:44 Triggers in SQL
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05:59:26 DML COmmands
06:18:36 Operators
06:47:54 Joins
06:51:52 Views
07:06:19 DCL Commands
07:15:23 SQL Server
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07:36:50 DDL Commands
08:30:07 Operators
08:55:50 Exception handling
08:59:55 SQL Server Interview Question & Answer

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This Edureka "Machine Learning using Spark MLlib" video will help you to understand what is Machine Learning and what is Data Types. Learn and understand the kinds of problems that are solved using Machine Learning
00:00 Introduction
00:19 Agenda
06:01 What is Machine Learning
08:26 Features of Machine Learning '
13:56 Machine Learning Applications
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18:02 Steps of Machine Learning
49:10 MLlib
53:43 Spark MLlib Tools


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--------------------------------------------

About the Course

Edureka’s PySpark Certification Training is designed to provide you the knowledge and skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). Throughout the PySpark Training, you will get an in-depth knowledge of Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will also get comprehensive knowledge of Python Programming language, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka.

----------------------------------------------

Spark Certification Training is designed by industry experts to make you a Certified Spark Developer. The PySpark Course offers:
Overview of Big Data & Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator)
Comprehensive knowledge of various tools that falls in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming
The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
The power of handling real time data feeds through a publish-subscribe messaging system like Kafka
The exposure to many real-life industry-based projects which will be executed using Edureka’s CloudLab

---------------------------------------------------

Who should go for this course?

Developers and Architects
BI /ETL/DW Professionals
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There are no such prerequisites for Edureka’s PySpark Training Course. However, prior knowledge of Python Programming and SQL will be helpful but is not at all mandatory.

--------------------------------------------------------

For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free)

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Welcome to Intellipaat's Artificial Intelligence Full Course 2024— an in-depth guide designed for absolute beginners looking to master AI and machine learning! This course covers everything you need to kickstart your journey in AI, from foundational concepts in artificial intelligence and machine learning to hands-on tutorials. Throughout this AI course for beginners, you'll explore different types of machine learning, Artificial Neural Networks, Deep Learning Fundamentals, Keras Library for AI, RNNs, GPU in Deep Learning, etc. to excel in today’s AI-driven world. Our course is ideal for anyone who wants to learn artificial intelligence from scratch and gain practical knowledge for real-world AI projects.

📖Below are the topics covered in this 'Artificial Intelligence Full Course 2024' video:
00:00:00 - Introduction to Artificial Intelligence Course
00:03:29 - What is Artificial Intelligence
00:07:17 - Why Artificial Intelligence
00:57:05 - Machine Learning Types
01:07:35 - Introduction to Deep Learning
02:45:16 - Benefits of Using Artificial Neural Network
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03:02:57 - Data Handling with NumPy
03:20:28 - Introduction to TensorFlow
05:23:00 - Understanding Epoch
05:50:38 - Introduction to Keras
06:43:35 - Predefined Neural Network Layers
08:43:11 - Problem With a Fully Connected Network
08:52:56 - Convolutional Neural Network
10:13:25 - Rectified Linear Units
10:45:45 - Artificial Intelligence Interview Questions

✅ Is AI hard to study?
Studying AI can be challenging, as it involves complex topics like programming, mathematics (especially statistics and linear algebra), and algorithms. However, with structured learning paths and accessible resources, it’s possible for beginners to grasp these concepts over time. Consistent practice and hands-on projects help make the learning process easier and more practical. Ultimately, while AI may seem difficult, determination and incremental learning make it achievable.

✅ What is an AI course?
An AI course is a structured learning program designed to teach the fundamentals and advanced concepts of artificial intelligence. It typically covers topics like machine learning, neural networks, data analysis, and programming with Python or other languages. Many AI courses include practical projects, giving learners hands-on experience in building AI models. These courses help individuals gain the skills needed for AI-related careers, such as data science or machine learning engineering.

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In this live session we'll read and discuss two papers:
The illusion of thinking - by Apple
The illusion of the illusion of thinking - by Anthropic
Come join for a fun chat!

In this episode, I sit down with Professor Ras Mic for a beginner-friendly crash course on using Claude Code (and AI coding agents in general) without feeling overwhelmed by the terminal. We break down why your output is only as good as your inputs and how thinking in features + tests turns “vague app ideas” into real, shippable products. Was walks me through a better planning workflow using Claude Code’s Ask User Question Tool, which forces clarity on UI/UX decisions, trade-offs, and technical constraints before you build. We also talk about when not to use “Ralph” automation, why context windows matter, and how taste + audacity are the real differentiators in 2026 software.

Timestamps
00:00 – Intro
01:22 – Claude Code Best Practices
05:31 – Claude Code Plan Mode
09:30 – The Ask User Question Tool
14:52 – Don’t start with Ralph automation (get reps first)
16:36 – What are “Ralph loops” and why plans and documentation matter most
18:41 – Ras’s Ralph setup: progress tracking + tests + linting
23:48 – Tips & tricks: don’t obsess over MCP/skills/plugins
27:44 – Scroll-stopping software wins

Links Mentioned:

Ras's Ralphy AI Agent: https://startup-ideas-pod.link/ras-ralphy

Key Points

* Your results improve fast when you treat AI agents like junior engineers: clear inputs → clean outputs.
* The biggest unlock is planning in features + tests, not broad product descriptions.
* Claude Code’s Ask User Question Tool forces real clarity on workflow, UI/UX, costs, and technical decisions.
* If you haven’t shipped anything, don’t hide behind automation—build manually before using “Ralph.”
* Context management matters: long sessions can degrade quality, so restart earlier than you think.

Numbered Section Summaries

* The Real Reason People Get “AI Slop” I frame the episode around a simple idea: if you feed agents sloppy instructions, you’ll get sloppy output. Ras explains that models are now good enough that the failure mode is usually unclear inputs, not model quality.

* How To Think Like A Product Builder (Features First): Ras pushes a practical mindset: don’t describe “the product,” describe the _features_ that make the product real. If you can list the core features clearly, you can actually direct an agent to build them correctly.

* The Missing Piece: Tests Between Features: We talk about the shift from “generate code” to “build something serious.” The move is writing and running tests after each feature, so you don’t stack feature two on top of a broken feature one.

* Why Default Planning Mode Isn’t Enough: Ras shows the standard flow: open plan mode, ask Claude to write a PRD, and get a basic roadmap. The issue is it leaves too many assumptions—especially around UI/UX and workflow details.

* The Ask User Question Tool (The Planning Upgrade): This is the big unlock. Ras demonstrates how the Ask User Question Tool interrogates you with increasingly specific questions (workflow, cost handling, database/hosting, UI style, storage, etc.) so the plan becomes dramatically more precise.

* Spend Time Upfront Or Pay For It Later: We connect the dots: better planning reduces back-and-forth, reduces token burn, and prevents “I built the app but it’s not what I wanted.” The interview-style planning forces trade-offs early instead of late.

* Don’t Use Ralph Until You’ve Built Without It: Ras makes a strong case for reps: if you can’t ship something end-to-end yet, automation won’t save you—it’ll just move faster in the wrong direction. Build feature-by-feature manually first, then graduate to loops.

* Practical Tips: Context Discipline + Taste Wins: Ras shares a few operational habits: don’t obsess over tools like MCP/plugins, keep context usage under control, and restart sessions before quality degrades. We wrap on a bigger point: in 2026, “audacity + taste” is what makes software stand out.

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X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/

FIND MIC ON SOCIAL
X/Twitter: https://x.com/Rasmic
Youtube: https://www.youtube.com/@rasmic

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Imagine a world where AI isn't just science fiction but a powerful partner in your business success. This video on AI for Business dives into the exciting world of AI for business, showcasing its revolutionary impact across industries. Witness real-world examples of AI driving innovation in marketing, finance, and manufacturing data analysis on steroids. Imagine the massive benefits of AI for business, predict customer behavior, and make smarter decisions. Get a sneak peek into the future of manufacturing with smart factories (Industry 4.0) and the rise of human-AI collaboration (Industry 5.0). We'll dive into how AI is changing industries like marketing, finance, and supply chain. AI isn't magic, it's the future of business.
00:00:00 Introduction
00:02:11 AI for Business
00:07:38 What Is Artificial Intelligence?
00:14:19 Types Of Artificial Intelligence
00:24:33 AI vs Machine Learning vs Deep Learning
00:38:17 What is Deep Learning
00:49:30 What is LLM (Large Language Model)
01:06:00 What Is Generative AI?
01:21:18 What Is AI Ethics
01:29:55 What is Responsible AI
01:38:09 Artificial Intelligence with Python
03:17:04 Artificial Neural Network
03:48:53 Recurrent Neural Networks
04:17:44 Convolutional Neural Network
04:38:19 Introduction to TensorFlow
05:01:57 Prompt Engineering
05:21:21 Prompt Engineering For Code Generation
05:30:40 Building a Chatbot with Prompt Engineering
05:46:44 OpenAI o3-mini Model
05:53:55 What is Agentic AI?
06:04:59 Introduction to Midjourney
06:23:31 How to use Midjourney?
06:31:15 GitHub Copilot
06:48:34 What is Vibe Coding
06:57:44 Top 5 AI Frameworks in 2025
07:12:29 How AI Is Transforming Studio Ghibli-Style Animation?
07:17:56 AI in Web Development
07:43:07 AI in Healthcare
07:51:49 AI in Retail
07:57:57 AI in Automotive
08:07:52 AI for Marketing
08:20:28 AI in Finance
08:29:04 AI for HR
08:35:25 AI in Manufacturing
08:41:53 AI in Cybersecurity
08:51:41 AI for Startup
09:00:15 AI for Testing
09:05:40 AI for Ethical Hacking
09:10:34 AI on Microsoft Azure
09:23:00 AI and Industry 4.0
09:28:01 AI Engineer Learning Path
09:35:13 Top 15 AI Skills You Need to Know
09:43:33 Build AI Applicant Tracking System (ATS)
09:59:07 Artificial Intelligence Interview Questions & Answers
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Learn the basics of computer vision with deep learning and how to implement the algorithms using Tensorflow.

Author: Folefac Martins from Neuralearn.ai
More Courses: www.neuralearn.ai
Link to Code: https://colab.research.google.....com/drive/18u1KDx-96
YouTube Channel: https://www.youtube.com/@neuralearn

⭐️ Contents ⭐️

Introduction
⌨️ (0:00:00) Welcome
⌨️ (0:05:54) Prerequisite
⌨️ (0:06:11) What we shall Learn

Tensors and Variables
⌨️ (0:12:12) Basics
⌨️ (0:19:26) Initialization and Casting
⌨️ (1:07:31) Indexing
⌨️ (1:16:15) Maths Operations
⌨️ (1:55:02) Linear Algebra Operations
⌨️ (2:56:21) Common TensorFlow Functions
⌨️ (3:50:15) Ragged Tensors
⌨️ (4:01:41) Sparse Tensors
⌨️ (4:04:23) String Tensors
⌨️ (4:07:45) Variables

Building Neural Networks with TensorFlow [Car Price Prediction]
⌨️ (4:14:52) Task Understanding
⌨️ (4:19:47) Data Preparation
⌨️ (4:54:47) Linear Regression Model
⌨️ (5:10:18) Error Sanctioning
⌨️ (5:24:53) Training and Optimization
⌨️ (5:41:22) Performance Measurement
⌨️ (5:44:18) Validation and Testing
⌨️ (6:04:30) Corrective Measures

Building Convolutional Neural Networks with TensorFlow [Malaria Diagnosis]
⌨️ (6:28:50) Task Understanding
⌨️ (6:37:40) Data Preparation
⌨️ (6:57:40) Data Visualization
⌨️ (7:00:20) Data Processing
⌨️ (7:08:50) How and Why ConvNets Work
⌨️ (7:56:15) Building Convnets with TensorFlow
⌨️ (8:02:39) Binary Crossentropy Loss
⌨️ (8:10:15) Training Convnets
⌨️ (8:23:33) Model Evaluation and Testing
⌨️ (8:29:15) Loading and Saving Models to Google Drive

Building More Advanced Models in Teno Convolutional Neural Networks with TensorFlow [Malaria Diagnosis]
⌨️ (8:47:10) Functional API
⌨️ (9:03:48) Model Subclassing
⌨️ (9:19:05) Custom Layers

Evaluating Classification Models [Malaria Diagnosis]
⌨️ (9:36:45) Precision, Recall and Accuracy
⌨️ (10:00:35) Confusion Matrix
⌨️ (10:10:10) ROC Plots

Improving Model Performance [Malaria Diagnosis]
⌨️ (10:18:10) TensorFlow Callbacks
⌨️ (10:43:55) Learning Rate Scheduling
⌨️ (11:01:25) Model Checkpointing
⌨️ (11:09:25) Mitigating Overfitting and Underfitting

Data Augmentation [Malaria Diagnosis]
⌨️ (11:38:50) Augmentation with tf.image and Keras Layers
⌨️ (12:38:00) Mixup Augmentation
⌨️ (12:56:35) Cutmix Augmentation
⌨️ (13:38:30) Data Augmentation with Albumentations

Advanced TensorFlow Topics [Malaria Diagnosis]
⌨️ (13:58:35) Custom Loss and Metrics
⌨️ (14:18:30) Eager and Graph Modes
⌨️ (14:31:23) Custom Training Loops

Tensorboard Integration [Malaria Diagnosis]
⌨️ (14:57:00) Data Logging
⌨️ (15:29:00) View Model Graphs
⌨️ (15:31:45) Hyperparameter Tuning
⌨️ (15:52:40) Profiling and Visualizations

MLOps with Weights and Biases [Malaria Diagnosis]
⌨️ (16:00:35) Experiment Tracking
⌨️ (16:55:02) Hyperparameter Tuning
⌨️ (17:17:15) Dataset Versioning
⌨️ (18:00:23) Model Versioning

Human Emotions Detection
⌨️ (18:16:55) Data Preparation
⌨️ (18:45:38) Modeling and Training
⌨️ (19:36:42) Data Augmentation
⌨️ (19:54:30) TensorFlow Records

Modern Convolutional Neural Networks [Human Emotions Detection]
⌨️ (20:31:25) AlexNet
⌨️ (20:48:35) VGGNet
⌨️ (20:59:50) ResNet
⌨️ (21:34:07) Coding ResNet from Scratch
⌨️ (21:56:17) MobileNet
⌨️ (22:20:43) EfficientNet

Transfer Learning [Human Emotions Detection]
⌨️ (22:38:15) Feature Extraction
⌨️ (23:02:25) Finetuning

Understanding the Blackbox [Human Emotions Detection]
⌨️ (23:15:33) Visualizing Intermediate Layers
⌨️ (23:36:20) Gradcam method

Transformers in Vision [Human Emotions Detection]
⌨️ (23:57:35) Understanding ViTs
⌨️ (24:51:17) Building ViTs from Scratch
⌨️ (25:42:39) FineTuning Huggingface ViT
⌨️ (26:05:52) Model Evaluation with Wandb

Model Deployment [Human Emotions Detection]
⌨️ (26:27:13) Converting TensorFlow Model to Onnx format
⌨️ (26:52:26) Understanding Quantization
⌨️ (27:13:08) Practical Quantization of Onnx Model
⌨️ (27:22:01) Quantization Aware Training
⌨️ (27:39:55) Conversion to TensorFlow Lite
⌨️ (27:58:28) How APIs work
⌨️ (28:18:28) Building an API with FastAPI
⌨️ (29:39:10) Deploying API to the Cloud
⌨️ (29:51:35) Load Testing with Locust

Object Detection with YOLO
⌨️ (30:05:29) Introduction to Object Detection
⌨️ (30:11:39) Understanding YOLO Algorithm
⌨️ (31:15:17) Dataset Preparation
⌨️ (31:58:27) YOLO Loss
⌨️ (33:02:58) Data Augmentation
⌨️ (33:27:33) Testing

Image Generation
⌨️ (33:59:28) Introduction to Image Generation
⌨️ (34:03:18) Understanding Variational Autoencoders
⌨️ (34:20:46) VAE Training and Digit Generation
⌨️ (35:06:05) Latent Space Visualization
⌨️ (35:21:36) How GANs work
⌨️ (35:43:30) The GAN Loss
⌨️ (36:01:38) Improving GAN Training
⌨️ (36:25:02) Face Generation with GANs

Conclusion
⌨️ (37:15:45) What's Next

What’s up gadget-heads and welcome to another episode of TechJoint! Today, we’re bringing you the 10 TERRIFYING Military Robots That Really Exist! For the most part, robots exist to do good things. We’ve built them to vacuum our floors, work on assembly lines and even play fetch with our dogs. But because the military has always been at the cutting edge of technology, it should come as no surprise that some of the most advanced robots in the world are being built with military applications in mind. From tiny spy robots to pure death machines, let’s look at some of these most terrifying military robots. Before we begin, make sure you hit that subscribe button and turn on notifications so you never miss out on the latest and greatest tech.

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MIT 14.02 Principles of Macroeconomics, Spring 2023
Instructor: Ricardo J. Caballero

View the complete course: https://ocw.mit.edu/courses/14....-02-principles-of-ma
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In this lecture, Prof. Caballero discusses basic macroeconomic concepts such as aggregate output, the unemployment rate, and the inflation rate.


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