New Video : The New ChatGPT

ChatGPT ሙሉ ለሙሉ ኢንተርፌሱን ቀይሯል። ChatGPT Desktop update ስታደርጉ አዳድስ Ai agents and features ታገኛላችሁ።

- Codex
- ChatGPT Work
- Plugins
- Skills

እና የበፊቱን ራሱን ChatGPT ጨምሮ አንድ አፕ ላይ ታገኙታላችሁ።

ChatGPT አሁን ላይ ከ Ai chatbotነት ወደ Ai Operating System ወደ መሆን ሄዷል።

በዚህ ቪድዮ ሙሉ ስለዚህ እና እንዴት መጠቀም እንዳለብቻችሁ በዝርዝር ይዤላችሁ መጥቻለሁ!

ሁላችሁም ብታዩት ታተርፋላችሁ!

https://youtu.be/9lJfniEELqw

@ImmersiveAi

ክረምቱን Ai'ን መማር ለምትፈልጉ ይችን ቻናል ከፍቻለሁ!

እናም ብዙ ይጠቅማሉ የምላቸውን Ai ላይ ብቻ ትኩረታቸውን ያደረጉ ትምርቶችን ፕሪሚየም ኮርሶችን እለቃለሁ!

ተማሪዎችም ፤ ሰራተኞችም ፤ ፕሮፌሽናሎችም ሁላችሁም ክረምቱን ራሳችሁን በዚህ ስኪል አበልፅጋችሁ ብትቆዩ ታተርፋላችሁ!

ብዙ ቪድዮ ስለምሰራ ደግሞ ከስር ከስር ብታዩ የበለጠ ተጠቃሚ ትሆናላችሁ!

እስካሁን ያለፋችሁንም ወደኋላ ሄዳችሁ ራሳችሁን ብቁ አድርጉ!


Ai ከምታስቡት በላይ ነገሮችን እየቀየረ ነው። ነገሮች ሁሉ ተቀይረዋል። አሁን ላይ Ai'ን ማወቅ ከቅንጦትነት አልፎ ግዴታ ሆኗል። ደሞም የመጣው ለሁሉም ኢንዱስትሪ ነው።

በተጨማሪ የ Ai ስኪሎችን መያዝ ከሁሉም ቀዳሚ እና ተጠቃሚ ያደርጋችኋል።

ቻናሉን ሰብስክራይብ በማድረግ ቪድዮው እንዳያመልጣችሁ አድርጉ!

በተረፈ እንዲሰራላችሁ ምትፈልጉትን ቭዲዮ ኮሜንት ላይ አስቀምጡልኝ። በውስጥም ልትልኩልኝ ትችላላችሁ!

ሊንክ ፡ https://www.youtube.com/@ImmersiveAi1

@ImmersiveAi

NB : Mathematics is the backbone of Ai.

Every advanced model you know is made from Linear Algebra, Calculus and Probability and Statistics.

I think, i should do a math course. Do you guys need ?

@ImmersiveAi

Share this channel for anyone who begins any ai engineering, machine learning or related journey.

Let's grow together.

Let's build a community of Ai.

@ImmersiveAi

የ Mathematics ሙሉ ኮርስ በ 2 ሰዓት ብቻ ለመስራት ደፍሪያለሁ 😊

Yeah... I did it.

AI የሚጀምሩ ልጆች የሚጠይቁኝ ተደጋጋሚ ጥያቄ ምን ያክል Math ያስፈልጋል የሚል ነው።

Yeah... AI በ Math ነው የተሰራው። ስለዚህ እንዲትጀምሩት ሙሉ mathematics ዘርፎችን ቲዮረቲካሊ ሄጀበታለሁ። ከዚህ ተነስታችሁ በ ዋናነት 4ቱን ዋና ዋናዎቹ ደሞ በራሳችሁ ብትሂዱበት ታተርፋላችሁ!

- Linear Algebra
- Calculus
- Probability and Statistics
- Optimization theories.

እነዚህን ካደማችሁ በቂ ነው።

ቀጣይ ብዙ ኢትዮጵያውያን Ai Engineers እናያለን።

ይሄኛውን የማትስ ኮርስ ግን ማንኛውም ሰው ቢያየው ይጠቀማል። Maths ሲባል የምትጠሉ ሰዎች እራሱ ይሄን ቪድዮ ካያካችሁ በኋላ በ ፍቅሩ ትወድቃላችሁ!

Btw...የ ስንት አመት ኮርስን በ 2 ሰዓት ለማሳጠር በጣም ከባድ ነው። እኔ ይሄን ቪድዮ እንደ ታች ክላስ የተማራችሁትን እንደ ክለሳ ክላስ አስቡት!

እስኪ እዩት!.... 👇👇👇

https://youtu.be/wa65bGkElzg

How to learn Python For Ai.

https://youtu.be/pHuSSzATC7Q

የ ፓይተን ሙሉ የ 8 - ሰዓት ኮርስ ከዚህ ቀደም ሰርቼ በ @EmmersiveLearning ላይ ለቅቄ ብዙዎች ወደውት፤ ተምረውበት ነበር።

አሁን እዚያ ስለሌል በ ድጋሜ እዚህ ቻናል ላይ ለቅቄዋለሁ! የምትፈልጉት እዚኛው ቻናል እየገባችሁ ተማሩ።

በተጨማሪ ቪድዮው ላይ ያለውን paid guide book የምትፈልጉ አናግሩኝ ለ 1 ሳምንት Free gift ይኖራል።

https://youtu.be/HnBPwSOIHCA

Python is The king of programming languages in this day.

Learn it here :https://youtu.be/HnBPwSOIHCA

Python Full Course in 8-hours

https://youtu.be/HnBPwSOIHCA

Phases of Artificial Intelligence Adoption as outlined by Boris Cherny, creator of Claude Code:

1. AI-Gated: Utilization of zero agents.
2. AI-Assisted: Utilization of one agent.
3. AI-Parallel: Utilization of approximately ten agents.
4. AI-Supervised: Utilization of over one hundred agents.
5. AI-Native: Utilization of over one thousand agents.

@ImmersiveAI

You want to pursue Ai Engineering ?

Study those math concept first :

- Basic Math
- Linear Algebra
- Calculus
- Probability and Statistics


Watch the map of math in Onr video below 👇

@ImmersiveAi

.
Here is the map of Math 👇


https://youtu.be/wa65bGkElzg

Python is ubiquitous, forming the foundational architecture of the modern world.

Mastery of this language is essential.

I have developed an eight-hour comprehensive course available at no cost.

Additionally, a complimentary guidebook is provided to facilitate your learning.

Successful completion requires only one week of undivided concentration.

https://youtu.be/HnBPwSOIHCA

.
Python Full Course ...

https://youtu.be/HnBPwSOIHCA

AI Engineer Learning Roadmap (2026)

🎯 Step 1: Learn Python
✅ Variables & Data Types
✅ Loops & Functions
✅ Object-Oriented Programming
✅ Exception Handling
✅ File Handling
✅ Modules & Packages

🎯 Step 2: Master Python Libraries
✅ NumPy
✅ Pandas
✅ Matplotlib
✅ Scikit-learn

🎯 Step 3: Learn AI Fundamentals
✅ Artificial Intelligence
✅ Machine Learning
✅ Deep Learning
✅ Neural Networks
✅ Generative AI
✅ Large Language Models (LLMs)

🎯 Step 4: Learn Prompt Engineering
✅ Zero-shot Prompting
✅ One-shot Prompting
✅ Few-shot Prompting
✅ Role Prompting
✅ Chain of Thought Prompting

🎯 Step 5: Learn LLMs
✅ Tokenization
✅ Embeddings
✅ Context Window
✅ Temperature
✅ Model Parameters

🎯 Step 6: Learn RAG
✅ Embeddings
✅ Chunking
✅ Vector Databases
✅ Similarity Search
✅ Retrieval Pipeline

🎯 Step 7: Learn AI Agents
✅ Agent Architecture
✅ Memory
✅ Planning
✅ Tool Calling
✅ Multi-Agent Systems

🎯 Step 8: Learn AI Frameworks
✅ LangChain
✅ LangGraph
✅ LlamaIndex
✅ CrewAI

🎯 Step 9: Learn Backend Development
✅ FastAPI
✅ REST APIs
✅ Authentication
✅ Database Integration

🎯 Step 10: Deploy AI Applications
✅ Docker
✅ Git & GitHub
✅ Cloud Platforms (AWS, Azure, or GCP)
✅ Monitoring & Logging

🎯 Step 11: Build Projects
✅ AI Chatbot
✅ PDF Chat Assistant
✅ AI Research Assistant
✅ AI Resume Analyzer
✅ Customer Support Agent
✅ AI Coding Assistant

Watch this Ai Roadmap Video 👇 :

https://youtu.be/tKO4lOq32p0

AI is rapidly evolving into a massive, multi-layered industry, forming the largest technology ecosystem in history.

It is becoming foundational infrastructure like electricity, the internet, and computers.

We are entering the intelligence age and heading toward the Singularity.

View the complete AI ecosystem map here in this video:

https://youtu.be/TbPd2gTBGRs

@ImmersiveAi

New Video: Data Science Roadmap

For individuals pursuing careers in artificial intelligence, working with data constitutes the primary responsibility.

Data Science provides the essential tools, techniques, and methodologies required for effective data management.

It serves as the foundational discipline for Data Analytics, AI Engineering, and related fields.

The following is a brief overview and roadmap for Data Science.

https://youtu.be/XxwsYE-hMk4

@ImmersiveAi

Prompt engineering →
Context engineering →
Harness engineering →
Loop engineering →
Graph engineering... →

.
.
.
What is Next ?

You Just Need Better Harness and you will fix it when needed.

@ImmersiveAI

New Video : NumPy Course.

NumPy is one of the most important Python Library for those pursuing into Ai Engineering and Data Science related Fields.

Here is a full course. 👇


https://youtu.be/mIgz-n5vMQY

Most people learning AI make this mistake...
They jump straight into Machine Learning, Deep Learning, or LLMs...

...without learning NumPy.


That's like trying to build a skyscraper without understanding concrete.


Why is NumPy so important?

✅ It's the foundation of Python for Data Science.
✅ It powers fast numerical computation.
✅ It makes working with large datasets efficient.
✅ Libraries like Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch, and many others are built on top of it or heavily rely on its array concepts.


If you want to become an:
• AI Engineer
• Data Scientist
• Machine Learning Engineer
• Data Analyst

NumPy isn't optional—it's one of the first tools you should master.

Spend a few hours learning it now, and you'll save hundreds of hours later when building AI applications.


That's why I created a complete 80-minute NumPy course that takes you from beginner to confident with the fundamentals.


Link : https://youtu.be/mIgz-n5vMQY

@ImmersiveAI

Data Science Roadmap
|
|-- Core Foundations
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus Basics
| | |-- Probability
| | |-- Statistics
| |
| |-- Programming
| | |-- Python
| | | |-- NumPy
| | | |-- Pandas
| | | |-- Matplotlib
| | | |-- Seaborn
| | |-- R
| | |-- SQL
|
|-- Data Handling
| |-- Data Collection
| | |-- APIs
| | |-- Web Scraping
| | |-- Database Queries
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Outliers
| | |-- Feature Scaling
| | |-- Encoding
|
|-- Exploratory Data Analysis
| |-- Summary Statistics
| |-- Univariate Analysis
| |-- Bivariate Analysis
| |-- Visualizations
| |-- Correlation Checks
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- PCA
| |
| |-- Model Selection
| | |-- Train Test Split
| | |-- Cross Validation
| | |-- Hyperparameter Tuning
|
|-- Advanced Machine Learning
| |-- Ensemble Methods
| | |-- Random Forest
| | |-- XGBoost
| | |-- LightGBM
| |
| |-- Time Series
| | |-- ARIMA
| | |-- LSTM
| |
| |-- NLP
| | |-- Text Preprocessing
| | |-- TF IDF
| | |-- Word Embeddings
| |
| |-- Deep Learning
| | |-- Neural Networks
| | |-- CNN
| | |-- RNN
| | |-- Transformers
|
|-- Big Data
| |-- PySpark
| |-- Hadoop
| |-- Distributed Processing
|
|-- Model Deployment
| |-- Flask
| |-- FastAPI
| |-- Streamlit
| |-- Docker
| |-- Cloud Deployment
|
|-- MLOps
| |-- Experiment Tracking
| |-- Model Monitoring
| |-- CI CD
|
|-- Domain Knowledge
| |-- Finance
| |-- Healthcare
| |-- Retail
| |-- Marketing
|
|-- Ethics
| |-- Bias
| |-- Interpretability
| |-- Fairness


@ImmersiveAi