MIT 15.S21 Nuts and Bolts of Business Plans, IAP 2014
View the complete course: http://ocw.mit.edu/15-S21IAP14
Instructor: Bob Jones

This session will discuss these issues and provide guidance on how to approach the marketing section of your business plan.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15....-773-hands-on-deep-l
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

Covers transfer learning, convolutional neural network (CNN) models, pooling layers, and application examples, including a handbags-shoes classifier.

License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

MIT 8.04 Quantum Physics I, Spring 2013
View the complete course: http://ocw.mit.edu/8-04S13
Instructor: Allan Adams

In this lecture, Prof. Adams begins with a round of multiple choice questions. He then moves on to introduce the concept of expectation values and motivate the fact that momentum is given by a differential operator with Noether's theorem.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

October 14, 2025
This lecture covers adversarial robustness and generative models.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

Sebastian's books: https://sebastianraschka.com/books/
The lecture slides are available at: https://github.com/rasbt/stat4....53-deep-learning-ss2

Covers some of the basics of recurrent neural networks. In particular, this lecture covers

RNNs and Sequence Modeling Tasks: 00:00
Backpropagation Through Time: 20:23
Long-short term memory (LSTM): 31:42
Many-to-one Word RNNs: 45:16
Generating Text with Character RNNs: 50:45
Attention Mechanisms and Transformers: 1:00:09

An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series we recorded at home. You can find all the links from this video below. I hope this was helpful, and I'm looking forward to seeing you when we can get back to doing events in person. Thanks everyone!

Chapters:
0:00 - Intro and outline
1:42 - TensorFlow.js demos + discussion
3:58 - AI vs ML vs DL
7:55 - What’s representation learning?
8:40 - A cartoon neural network (more on this later)
9:20 - What features does a network see?
10:47 - The “deep” in “deep learning”
12:48 - Why tree-based models are still important
13:38 - How your workflow changes with DL
14:02 - A couple illustrative code examples
17:59 - What’s a hyperparameter?
19:44 - The skills that are important in ML
20:48 - An example of applied work in healthcare
21:58 - Families of neural networks + applications
28:55 - Encoder-decoders + more on representation learning
32:45 - Families of neural networks continued
35:50 - Are neural networks opaque?
38:29 - Building up from a neuron to a neural network
49:11 - A demo of representation learning in TF Playground
53:24 - Importance of activation functions
54:36 - What’s a neural network library?
58:43 - Overfitting and underfitting
1:02:38 - Autoencoders (and anomaly detection) screencast and demo
1:12:13 - Book recommendations

Here are three helpful classes you can check out to learn more:

Intro to Deep Learning from MIT → http://goo.gle/3sPj8To
MIT Deep Learning and Artificial Intelligence Lectures → https://goo.gle/3qh7H54
Convolutional Neural Networks for Visual Recognition from Stanford → http://goo.gle/3bbC34I

And here are all the links to demos and code from the video, in the order they appeared:

Face and hand tracking demos → http://goo.gle/2WTCwSc
Teachable machine demo → https://goo.gle/3bSCzCi
What features does a network see? → http://goo.gle/3e2zpA5
DeepDream tutorials → http://goo.gle/3bYIBTp and http://goo.gle/384B6JC
Hyperparameter tuning with Keras Tuner → http://goo.gle/2InBK7J
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → http://goo.gle/309pMY5
Linear (and deep) regression tutorial → http://goo.gle/3sKxkN7
Image classification with a CNN tutorial → http://goo.gle/3qdD2Wb
Audio recognition tutorial → http://goo.gle/3kFpl1j
Transfer learning tutorial → http://goo.gle/3bV7D60
RNN tutorial (sentiment analysis / text classification) → http://goo.gle/3bVM1X7
RNN tutorial (text generation with Shakespeare) → http://goo.gle/3qmnrnz
Timeseries forecasting tutorial (weather) → http://goo.gle/3ecdYg9
Sketch RNN demo (draw together with a neural network) → http://goo.gle/3bbHTTy
Machine translation tutorial (English to Spanish) → http://goo.gle/3e7IJme
Image captioning tutorial → http://goo.gle/3sKFNQz
Autoencoders and anomaly detection tutorial → http://goo.gle/30aD0UA
GANs tutorial (Pix2Pix) → http://goo.gle/3kI1ZrB
A Deep Learning Approach to Antibiotic Discovery → https://goo.gle/3e7ivQD
Integrated gradients tutorial → http://goo.gle/2PxfRtq and http://goo.gle/3sE0bmq
TensorFlow Playground demos → http://goo.gle/2Px6rhB
Introduction to gradients and automatic differentiation → http://goo.gle/3sFVybo
Basic image classification tutorial → http://goo.gle/3c2AF3o
Overfitting and underfitting tutorial → http://goo.gle/3cdA9Qv
Keras early stopping callback → http://goo.gle/308XQUj
Interactive autoencoders demo (anomaly detection) → http://goo.gle/3kPfW7q
Deep Learning with Python, Second Edition → http://goo.gle/3qcQ5Y5
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → http://goo.gle/386DKP4
Deep Learning book → http://goo.gle/3c2VQmd

Find Josh on Twitter → https://goo.gle/308Ve8P

Subscribe to TensorFlow → https://goo.gle/TensorFlow

Non-technical friendly course explaining machine learning and deep learning that everyone can understand.

Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

If you want to master AI agents in 2026 and actually build systems that can reason, plan, and take action on their own- this video is your complete roadmap.

I walk you through exactly what you need to learn, in what order, and which free resources will actually get you there- without wasting months chasing hype.

The space feels overwhelming right now. Everyone's throwing around words like agents, autonomy, multi-agent systems, tool use, planning, memory- and it all sounds exciting, but also kind of chaotic.
You're not too late. But you do need a plan. That's exactly what this video gives you.

Chapters:
00:00 – Why You Need a Roadmap for AI Agents
01:08 – Prerequisites: Python, APIs, ML Fundamentals
03:25 – Month 1: Foundations & Architecture
05:17 – Month 2: Agent Frameworks & Memory
06:55 – Month 3: Tools, APIs & Multi-Agent Systems
08:06 – Month 4: Evaluation, Safety & Deployment
08:55 – Month 5-6: Specialization, Advanced Topics & Capstone
10:12 – Final Advice & Resources

Free Resources Mentioned:

Prerequisites-
Google's Python Class: https://developers.google.com/edu/python
Python for Everybody (Charles Severance): https://www.py4e.com/

Month 1: Foundations

Hugging Face Agents Course: https://huggingface.co/learn/agents-course

Month 2: Frameworks & Memory

LangGraph Documentation: https://www.langchain.com/langgraph
CrewAI Documentation: https://docs.crewai.com/

Month 3-4: Tools, Multi-Agent & Production

OpenAI Function Calling Guide: https://platform.openai.com/do....cs/guides/function-c
LangSmith (Evaluation): https://www.langchain.com/langsmith

Month 5-6: Advanced & Capstone

Berkeley LLM Agents Course: https://llmagents-learning.org/f24

Drop a comment: Where are you in your AI agents journey? Just starting, already building, or stuck somewhere in the middle?

Build an AI agent that learns alongside you

In this free, end‑to‑end course, you’ll build a powerful language‑learning AI agent from scratch using Python, LangGraph, OpenAI, Ollama, and MCP. Over this hands‑on tutorial, we’ll create a ReAct‑style agent that sources vocabulary, performs accurate translations, and automatically generates Anki flashcards.

You’ll learn how to:
• Build a ReAct agent using LangGraph
• Connect agents to external tools with MCP
• Combine proprietary and local LLMs (OpenAI & Ollama)
• Design real‑world AI agent workflows

No NLP background required — we explain both the how and the why. By the end of this course, you’ll go from an empty Python project to a fully functional AI assistant you can adapt for your own AI projects.

Download PyCharm for free, the only Python IDE you need to build data models and AI agents: https://jb.gg/ai-agents-course

The full code for this tutorial, as well as the resources used, can be found in this GitHub repo: https://github.com/t-redactyl/....language-learning-ag
You can connect with Jodie, as well as see more of her work, at http://t-redactyl.io

Resources:
https://github.com/eymenefealt....un/all-words-in-all-
https://www.kaggle.com/datasets
https://www.kaggle.com/datasets?tags=13204-NLP
https://archive.ics.uci.edu/datasets
https://huggingface.co/learn/agents-course/

Timestamps
00:00 - Course Intro: What We’ll Build, AI agent tech stack
00:43 - What You’ll Learn: Building an AI Language Learning Agent
01:41 – Who This AI Agent Tutorial Is For (Python Prerequisites)
02:42 – Instructor Introduction: NLP & Data Science Background
03:01 – Why Use PyCharm for AI Agents & Data Science

Environment & Project Setup
03:36 – Installing PyCharm and AI Assistant
05:45 – Creating a Python Project with Virtual Environments

Dataset Selection & Preparation
07:26 – Choosing a Multilingual Vocabulary Dataset
08:46 – Best NLP Datasets: Kaggle & UCI Repositories
10:02 – Cloning and Organizing NLP Datasets in PyCharm

Installing Python & AI Dependencies
13:06 – Installing NLP Libraries: pandas, SpaCy, wordfreq
15:09 – Installing LangChain, LangGraph & MCP Libraries

Data Exploration & Analysis
16:56 – Exploring NLP Data with Jupyter Notebooks
18:01 – Analyzing Vocabulary Size Across Languages
20:45 – Visualizing Word Counts with Pandas Charts
22:08 – Identifying Data Problems in Multilingual Word Lists
24:36 – Introduction to SpaCy for Natural Language Processing

Cleaning the Word Lists
29:02 – Inspecting and Debugging Raw Vocabulary Data
31:48 – Removing Noise: Basic Text Cleaning Techniques
33:06 – Lemmatizing Words with SpaCy Models
35:10 – Using Zipf’s Law Overview to Filter Rare Words
37:26 – Word Frequency Analysis with wordfreq and SpaCy
42:35 - Understand Word Frequencies with wordfreq

Final Dataset Creation
45:22 – Building a Complete NLP Cleaning Pipeline
49:01 – Validating Results with a Spanish Dataset
50:01 – Comparing Raw vs Cleaned NLP Data

ReAct Agent Basics
54:40 – From Clean Data to an AI Agent
55:45 – What Is an AI Agent? Core Concepts
56:19 – Thought-Action-Observation Loop Explained
58:11 – Types of AI Agents

Large Language Models (LLMs) Explained
1:00:56 – How Large Language Models Understand Language
1:01:01 – Word Embeddings & Word2Vec Explained
1:05:24 – Why Word Embeddings Fail Without Context
1:06:05 – Transformers & Self-Attention Explained
1:10:23 – GPT Models and Decoder-Only Architectures

Reasoning Models for AI Agents
1:11:52 – Why Reasoning Models Power AI Agents
1:12:08 – How Reasoning Models Are Trained (Chain-of-Thought)
1:17:04 – When Not to Use Reasoning Models

1:20:24 How to Build a ReAct Agent with LangGraph
1:21:31 Agent State, Memory & Tools Explained
1:23:34 Choosing Between GPT-4 and Open-Source Models
1:26:36 How to Manage OpenAI API Keys Securely

1:30:09 How to Build Custom Tools for LangGraph Agents
1:33:14 Auto-Generating Tool Docstrings with AI
1:35:01 Improving Agent Reliability with System Prompts

1:38:13 Building and Connecting a LangGraph Agent Graph
1:41:21 Running an AI Agent End-to-End
1:43:02 How to Debug AI Agents in PyCharm
1:44:37 Visualizing Agent Execution Graphs

1:47:36 How to Run AI Agents Locally with Ollama
1:49:24 Choosing the Best Open-Source Reasoning Model
1:51:04 Installing and Managing Ollama Models
1:53:19 GPT-4 vs Ollama: Model Comparison for Agents

1:58:12 Switching LangGraph Agents from OpenAI to Ollama
2:00:22 Testing a Fully Local AI Agent

2:11:31 Adding Difficulty-Aware Vocabulary Tools
2:14:01 Handling Ambiguous User Requests in AI Agents
2:19:56 Testing AI Agents with Natural Language Prompts

2:24:48 How to Translate Words Using an LLM Tool
2:27:34 Building a Translation Tool with Ollama
2:32:28 Parsing Structured Output from LLMs

2:37:02 Multi-Step Tool Use in ReAct Agents
2:39:34 Handling Errors and Non-Determinism in AI Agents

2:41:40 What Is MCP (Model Context Protocol)?
2:42:13 Connecting AI Agents to External Tools with MCP

Come join us in this Live Q/A where our guest is Miguel Otero Pedrido, the creator of Neural Maze!
Feel free to post your questions beforehand or ask them live!

🔥Data Analyst Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/data-analyst-masters-certification-training-course?utm_campaign=ZpmTNYuRkLo&utm_medium=Lives&utm_source=Youtube
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🔥Purdue - Post Graduate Program in Data Analytics - https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=ZpmTNYuRkLo&utm_medium=Lives&utm_source=Youtube
🔥Caltech - Data Analytics Bootcamp (US Only) - https://www.simplilearn.com/data-analytics-bootcamp?utm_campaign=ZpmTNYuRkLo&utm_medium=Lives&utm_source=Youtube

This Video on Data Analytics Full Course by Simplilearn will help you learn everything you need to know about data analysis. this data analytics full course covers the basics through What is Data Analytics and the Data Analyst Roadmap to advanced topics such as Data Analytics using AI and Data Analysis with Python. You'll learn key skills like Data Manipulation in R, Data Transformation, and SQL Introduction, including Normalization in SQL and SQL CTE. Understand the differences between Data Science vs Data Analyst and gain hands-on experience creating Power BI Dashboards in minutes. Additionally, explore DBMS, Top BI Terms every data analyst should know, and prepare for interviews with common Data Analytics Interview Questions. Finally, leverage ChatGPT and Excel with Data Analytics to enhance your learning.

00:00:00 Introduction to Data Analytics Full Course
00:04:02 What is Big Data
00:05:26 What is Data Analytics
00:07:30 Data Science Vs Data Analyst
00:21:34 Steps Involved in Data Analytics
00:29:52 Data Analyst Roadmap
00:31:00 What is DBMS
00:42:32 Top BI Terms every data analyst know
01:02:24 Create PowerBI Dashboard in minutes
01:47:39 Who is a Data Analyst till R or Python
03:26:56 Data Analysis with Python
03:30:20 Descriptive Analysis Vs Stat
03:47:52 What is ETL
04:02:56 Descriptive statistics using excel
04:19:17 Data Transformation
07:44:15 Data Manipulation In R
07:59:52 Chatgpt and Excel with Data Analytics
08:03:09 SQL Basics
08:33:44 Tableau Calculations and filters
09:18:37 SQL Server tutorial
09:18:47 Data Analytics using AI
09:53:08 Top 10 Data Analytics certifications

✅ What is Data Analytics?

Data analytics is the process of analyzing data to extract insights and make conclusions. It's a multidisciplinary field that uses a variety of techniques, including math, statistics, and computer science. Data analytics can help businesses optimize performance, make more informed decisions, and maximize profit.

⏩ Check out the Data Analytics Playlist: link: https://www.youtube.com/playli....st?list=PLEiEAq2VkUU

✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

#dataanalytics #dataanalyticsforbeginners #datanalysis #sql #powerbi #tableau #excel #python #simplilearn #2025

➡️ About Post Graduate Program In Data Analytics

This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.

Key Features

✅ Post Graduate Program certificate and Alumni Association membership
✅ Exclusive hackathons and Ask me Anything sessions by IBM
✅ 8X higher live interaction in live online classes by industry experts
✅ Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
✅ Master Classes delivered by Purdue faculty and IBM experts
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies
✅ Resume preparation and LinkedIn profile building
✅ 1:1 mock interview
✅ Career accelerator webinars

Skills Covered

✅ Data Analytics
✅ Statistical Analysis using Excel
✅ Data Analysis Python and R
✅ Data Visualization Tableau and Power BI
✅ Linear and logistic regression modules
✅ Clustering using kmeans
✅ Supervised Learning

👉 Learn More at: https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=ZpmTNYuRkLo&utm_medium=Lives&utm_source=youtube

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The original Copyright(s) is (are) Solely owned by the Companies/Original-Artist(s)/Record-label(s).All the contents are intended to Showcase the creativity of the artist involved and are strictly done for promotional purpose.

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A visual description of Bayes' Theorem and the Naive Bayes algorithm, and an application to spam detection.
No previous knowledge is needed, aside from knowing how to multiply and divide, a visual mind and a desire to learn.

For a code implementation, check out this repo:
https://github.com/luisguiserr....ano/manning/tree/mas

0:00 Introduction
0:39 Spam Detector
4:59 Problem
10:34 Naive Bayes Classifier
17:00 Bayes Theorem

( ** Data Analyst Master's Program: https://www.edureka.co/masters....-program/data-analys ** )
This Edureka tutorial on "Data Analyst Roles and Responsibilities" will explain, what are the Roles and Responsibilities of a Data Analyst in the Industry. It also explains who is a Data Analyst and what does it takes to become one.

Following topics are included in the video:

1:51 Determine Organizational Goals
2:07 Mining Data
2:35 Data Cleaning
3:07 Analyzing Data
3:42 Pinpointing Trends and Patterns
4:14 Creating Reports with Clear Visualizations
4:54 Maintaining Databases and Data Systemtems

--------------------------
About the Master Program:

Data Analytics Masters Program makes you proficient in tools and systems used by Data Analytics Professionals. It includes in-depth training on Statistics, Data Analytics with R, SAS, and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.
-------------------------------------

Prerequisites:

There are no prerequisites for enrollment to the Masters Program. Whether you are an experienced professional working in the IT industry, or an aspirant planning to enter the world of Data Analyst, Masters Program is designed and developed to accommodate various professional backgrounds

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐂𝐨𝐮𝐫𝐬𝐞 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞 "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") : https://www.edureka.co/masters....-program/data-analys
In this Edureka Data Analyst Interview questions video, you will learn what kind of data analytics questions you can expect in the interview, How to answer them and much more. This Data Analyst Interview Questions video will give you a more comprehensive list of questions to crack your data analyst interview.
00:00:00 Introduction
00:01:26 Agenda
00:01:44 General Data Analyst Interview Questions
00:10:36 Data Analyst Interview Questions on Statistics
00:14:39 Data Analyst Interview Questions on Python
00:19:18 Data Analyst Interview Questions on SQL
00:22:56 Conclusion

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