Complete Ai/Ml road map🔥

It's the trend of Ai👇

1.Intro to AI:
• Definition of AI
• Types of AI
• AI in real life

2.Introduction to ML:
• Definition of ML
• Types of ML
- Supervised
- Unsupervised
- Reinforcement
• ML applications

3.Mathematics for ML:
• Linear Algebra
• Calculus
• Probability and Statistics

4.Programming Basics:
• Choose a prog language
- e.g., Python
• Basic syntax
• data structures
• Intro to libraries like
- NumPy
- Pandas

5.Supervised Learning:
• Regression
• Classification
• Model evaluation
• metrics

6.Unsupervised Learning:
• Clustering
• Dimensionality reduction
• Association rule learning

7.Deep Learning Basics:
• Neural networks
• Activation functions
• Backpropagation

8.Intro to TensorFlow and PyTorch:
• Basic usage
• syntax
• Building simple neural networks

9.Advanced Deep Learning:
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transfer Learning

10.Natural Language Processing (NLP):
• Text processing
• Tokenization and stemming
• Sentiment analysis

11. Computer Vision:
• Image processing
• Object detection
• Image segmentation

12.Reinforcement Learning:
• Basics of RL
• Markov Decision Processes
• Q-Learning
• Policy Gradient methods

13.Generative Adversarial Networks (GANs):
• Introduction to GANs
• Image generation with GANs

14.Time Series Analysis:
• Time series forecasting
• ARIMA models
• LSTM for time series

15.Anomaly Detection:
• Types of anomalies
• Approaches to anomaly detection

16. AI Ethics and Bias:
• Ethical considerations in AI/ML
• Addressing bias in models

17.Model Deployment:
• Containerization
- e.g., Docker
• Deployment platforms
- e.g., Flask, FastAPI

18.Monitoring and Maintenance:
• Model monitoring
• Continuous integration (CI)
• continuous deployment (CD)

19.Scalability:
• Handling large datasets
• Distributed computing frameworks
- e.g., Apache Spark

20. AI/ML in the Cloud:
• Using cloud services
- e.g., AWS, Azure, GCP
• Serverless computing

21.Explainable AI (XAI):
• Techniques for interpretable models
• Importance of model explainability

22.AutoML:
• Automated machine learning tools
• Hyperparameter tuning

23.Quantum Machine Learning:
• Basics of quantum computing
• Quantum machine learning algorithms

24. AI for Edge Computing:
• Deploying models on edge devices
• Edge AI applications

25.Stay Updated:
• Follow research papers
• Followconferences
- e.g., NeurIPS, ICML
• Join AI/ML communities

26.Advanced Research Topics:
• Dive into cutting-edge research areas
• Contribute to open-source AI projects

------------------- END --------------------

That's a wrap👏

"Take a simple idea, and take it seriously."
— Charlie Munger

try this. 😊 👇

Seriosly,

pick an object and ask a series of what, how, why, where, and when questions.

Do not limit yourself to the physical world here, or even direct connections.

focus on it for atleast 10 minutes contemplating and questioning it.

what do you get ?.. what did you realize ?...

#my_sunday_madness 😊😊😂

i love minimalist things.

no more stuff 😊

"Earn with your mind, not with your time." @naval

Open your IDE and try this codes.

#for_your_dopamine 😊

try it 😂😂😂

Not using Ai ?..You are falling behind!

start with Bard Ai.

https://www.youtube.com/watch?v=YMybfJuaWP4


in this video, i will show you how to use bard for you use case!

go to bard's site 👇
https://bard.google.com/

Don't follow the mass.

rebel against them.

make mistakes and learn through the process

question everything.

አምፅ ብሮ!... አምፅ ግዴለህም!.... BE FREE

with ai, you can do more

Pick a side-hustle that will become your main hustle

START TODAY!

GOOD MORNING!

Pick a side-hustle that will become your main hustle

START TODAY!

HAVE A GREAT WEEK

Have a digital workspace| a digital Notebook.

Use Notion.

https://www.youtube.com/watch?v=8FZAcl39UrQ

Complete DSA road map🔥

In file tree structure👇
|
|-- 📁01_Basics
| |-- 📁01_Introduction_to_DSA
| | |-- Introduction
| | |-- Importance
| | |-- Applications
| |
| |-- 📁02_Big_O_Notation
| |-- Big_O_Notation
|
|-- 📁02_Arrays_and_Strings
| |-- 📁01_Arrays
| | |-- Introduction_to_Arrays
| | |-- Operations_on_Arrays
| | |-- Searching_and_Sorting
| |
| |-- 📁02_Strings
| |-- Introduction_to_Strings
| |-- String_Manipulation
| |-- Introduction_to_Arrays
|
|-- 📁03_Linked_Lists
| |-- 📁01_Singly_Linked_List
| | |-- Intro_to_Singly_Linked_List
| | |-- Operations_on_Singly_Linked_List
| | |-- Detect_and_Remove_Cycle
| |
| |-- 📁02_Doubly_Linked_List
| |-- Intro_to_Doubly_Linked_List
| |-- Operations_on_Doubly_Linked_List
|
|-- 📁04_Stacks_and_Queues
| |-- 📁01_Stacks
| | |-- Introduction_to_Stacks
| | |-- Stack_Operations
| | |-- Implementing_Stacks
| |
| |-- 📁02_Queues
| |-- Introduction_to_Queues
| |-- Queue_Operations
| |-- Implementing_Queues
|
|-- 📁05_Trees_and_Graphs
| |-- 📁01_Trees
| | |-- Intro_to_Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| |
| |-- 📁02_Graphs
| |-- Introduction_to_Graphs
| |-- Depth_First_Search
| |-- Breadth_First_Search
|
|-- 📁06_Sorting_and_Searching
| |-- 📁01_Sorting_Algorithms
| | |-- Bubble_Sort
| | |-- Insertion_Sort
| | |-- Merge_Sort
| | |-- Quick_Sort
| |
| |-- 📁02_Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
|
|-- 📁07_Hash_Tables
| |-- Introduction_to_Hash_Tables
| |-- Hash_Functions
| |-- Collision_Resolution
| |-- Applications_of_Hash_Tables
|
|-- 📁08_Dynamic_Programming
| |-- Intro_to_Dynamic_Programming
| |-- Overlapping_Subproblems
| |-- Optimal_Substructure
| |-- Top-Down_vs_Bottom-Up
| |-- Common_DP_Problems
|
|-- 📁09_Greedy_Algorithms
| |-- Intro_to_Greedy_Algorithms
| |-- Standard_Greedy_Algorithms
| |-- Applications_of_Greedy_Algorithms
|
|-- 📁10_Advanced_Data_Structures
| |-- Trie
| |-- Segment_Tree
| |-- Disjoint_Set_Union
| |-- Fenwick_Tree
|
|-- 📁11_Algorithmic_Paradigms
| |-- Divide_and_Conquer
| |-- Backtracking
| |-- Sliding_Window
|
|-- 📁12_Interview_Preparation
|-- Coding_Practice
|-- Problem_Solving_Strategies
|-- Mock_Interviews

------------------- END -------------------

Good resources to learn and practice DSA👇

1. Courses
Advanced Data Structures (MIT)
rb.gy/qtyuc
Algorithms Specialization (Stanford University)
rb.gy/0pcln
FreeCodeCampOrg
rb.gy/mpyce
The Odin Project DSA
rb.gy/6402y

2. Book
Introduction to Algorithms [Book]
rb.gy/ui3xc

3. YouTube
Abdul Bari
youtube.com/@abdul_bari?si…
Code N Code
youtube.com/@codencode?si=…
Striver
youtube.com/@takeUforward?…

4.Coding Platforms
• LeetCode
• HackerRank
• CodeChef
• GeeksforGeeks
• TopCoder

Feel free to add anything I missed ☺️🌱🌱

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

That's a wrap👏

🔗 Full Stack Project Ideas:

• 🤝 Networking Site
• ✍️ Blog Platform
• 🛒 Online Marketplace
• 🎓 Course App
• 📋 Project Management Tool
• 🏥 Health Tracker App
• 🍽 Recipe Sharing Platform
• 📈 Financial Dashboard
• 🌍 Travel Journal App
• 🎨 Digital Art Gallery

The Feynman Technique

To learn anything:

Step 1: Identify a topic
Step 2: Try to explain it to a 5-year-old
Step 3: Study to fill in knowledge gaps
Step 4: Organize, convey, and review

True genius is the ability to simplify, not complicate.

Simple is beautiful.

Luck Surface Area

👉The amount of luck that will occur in your life, your Luck Surface Area, is directly proportional to what you do something and how much you tell to people .

👉Doing: This refers to the actions you take to create value in the world, such as building a product, writing a book, or starting a business.

👉Telling: This refers to the communication you have with others about what you are doing, such as sharing your ideas, pitching your product, or networking with potential customers.

👉Luck Surface Area: This is the product of doing and telling. The more you do and the more you tell, the larger your luck surface area becomes.

👉Aperture: This is the openness or receptiveness you have to the potential lucky events that come your way.

👉 if you want to create more luck, you should increase your luck surface area by doing more and telling more, and open up your aperture