ChatGPT For Learning

Machine Learning Roadmap

AI Prediction in 2025

1. Major Acquisitions: Anthropic (Amazon), Mistral (Meta), and Cohere (Google).

2. Mistral will be absorbed into Meta

3. Cohere will be bought by Google

4. Rest like SSI (from Ilya) etc will simply fold like Inflection AI etc.

5. Only OpenAI and XAI may remain as independent companies.

6. Mainstream Adoption of AI Agents.

7. Proliferation of Specialized Large Language Models.

Chrome vs Firefox

Artificial Intelligence, Game Theory and Mechanism Design in Politics
Tshilidzi Marwala, 2023

Fundamentals of Deep Learning
Nithin Buduma, 2022

Modern Deep Learning for Tabular Data
Andre Ye, 2023

Machine Learning Projects

Data Scientist vs Ai Engineer

Useful AI Tools

12 Fundamental Math Theories Needed to Understand AI

1. Curse of Dimensionality
This phenomenon occurs when analyzing data in high-dimensional spaces. As dimensions increase, the volume of the space grows exponentially, making it challenging for algorithms to identify meaningful patterns due to the sparse nature of the data.
2. Law of Large Numbers
A cornerstone of statistics, this theorem states that as a sample size grows, its mean will converge to the expected value. This principle assures that larger datasets yield more reliable estimates, making it vital for statistical learning methods.
3. Central Limit Theorem
This theorem posits that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the original distribution. Understanding this concept is crucial for making inferences in machine learning.
4. Bayes’ Theorem
A fundamental concept in probability theory, Bayes’ Theorem explains how to update the probability of your belief based on new evidence. It is the backbone of Bayesian inference methods used in AI.
5. Overfitting and Underfitting
Overfitting occurs when a model learns the noise in training data, while underfitting happens when a model is too simplistic to capture the underlying patterns. Striking the right balance is essential for effective modeling and performance.
6. Gradient Descent
This optimization algorithm is used to minimize the loss function in machine learning models. A solid understanding of gradient descent is key to fine-tuning neural networks and AI models.
7. Information Theory
Concepts like entropy and mutual information are vital for understanding data compression and feature selection in machine learning, helping to improve model efficiency.
8. Markov Decision Processes (MDP)
MDPs are used in reinforcement learning to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. This framework is crucial for developing effective AI agents.
9. Game Theory
Old school AI is based off game theory. This theory provides insights into multi-agent systems and strategic interactions among agents, particularly relevant in reinforcement learning and competitive environments.
10. Statistical Learning Theory
This theory is the foundation of regression, regularization and classification. It addresses the relationship between data and learning algorithms, focusing on the theoretical aspects that govern how models learn from data and make predictions.
11. Hebbian Theory
This theory is the basis of neural networks, “Neurons that fire together, wire together”. Its a biology theory on how learning is done on a cellular level, and as you would have it — Neural Networks are based off this theory.
12. Convolution (Kernel)
Not really a theory and you don’t need to fully understand it, but this is the mathematical process on how masks work in image processing. Convolution matrix is used to combine two matrixes and describes the overlap.

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Famous programming languages and their frameworks


1. Python:

Frameworks:
Django
Flask
Pyramid
Tornado

2. JavaScript:

Frameworks (Front-End):
React
Angular
Vue.js
Ember.js
Frameworks (Back-End):
Node.js (Runtime)
Express.js
Nest.js
Meteor

3. Java:

Frameworks:
Spring Framework
Hibernate
Apache Struts
Play Framework

4. Ruby:

Frameworks:
Ruby on Rails (Rails)
Sinatra
Hanami

5. PHP:

Frameworks:
Laravel
Symfony
CodeIgniter
Yii
Zend Framework

6. C#:

Frameworks:
.NET Framework
ASP.NET
ASP.NET Core

7. Go (Golang):

Frameworks:
Gin
Echo
Revel

8. Rust:

Frameworks:
Rocket
Actix
Warp

9. Swift:

Frameworks (iOS/macOS):
SwiftUI
UIKit
Cocoa Touch

10. Kotlin:
- Frameworks (Android):
- Android Jetpack
- Ktor

11. TypeScript:
- Frameworks (Front-End):
- Angular
- Vue.js (with TypeScript)
- React (with TypeScript)

12. Scala:
- Frameworks:
- Play Framework
- Akka

13. Perl:
- Frameworks:
- Dancer
- Catalyst

14. Lua:
- Frameworks:
- OpenResty (for web development)

15. Dart:
- Frameworks:
- Flutter (for mobile app development)

16. R:
- Frameworks (for data science and statistics):
- Shiny
- ggplot2

17. Julia:
- Frameworks (for scientific computing):
- Pluto.jl
- Genie.jl

18. MATLAB:
- Frameworks (for scientific and engineering applications):
- Simulink

19. COBOL:
- Frameworks:
- COBOL-IT

20. Erlang:
- Frameworks:
- Phoenix (for web applications)

21. Groovy:
- Frameworks:
- Grails (for web applications)

Coding Interview Preparation

Here are some highly recommended tools and materials to help you succeed in your tech interviews.

AlgoMonster:
Learn coding interview patterns which can be used to solve variety of coding problems on LeetCode and NeetCode

Educative-99:
a curated set of 99 coding interview questions designed to help candidates master 26 essential problem-solving patterns. It provides a hands-on, setup-free coding environment where users can efficiently practice and internalize coding patterns crucial for tech interviews, making it easier to tackle various coding challenges in a structured manner​

LeetCode:
Practice coding problems of varying difficulty levels.

NeetCode:
Get access to a structured plan for mastering coding problems.
Cracking the Coding Interview: A comprehensive guidebook with 189 programming questions and solutions.

Here are some essential Python Concepts for Data Analyst

Here is an A-Z list of essential programming terms:

1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.

2. Boolean: A data type that represents true or false values.

3. Conditional Statement: A statement that executes different code based on a condition.

4. Debugging: The process of identifying and fixing errors or bugs in a program.

5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.

6. Function: A block of code that performs a specific task and can be called multiple times in a program.

7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.

8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.

9. Integer: A data type that represents whole numbers without any fractional part.

10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.

11. Loop: A programming construct that allows repeating a block of code multiple times.

12. Method: A function that is associated with an object in object-oriented programming.

13. Null: A special value that represents the absence of a value.

14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.

15. Pointer: A variable that stores the memory address of another variable.

16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.

17. Recursion: A programming technique where a function calls itself to solve a problem.

18. String: A data type that represents a sequence of characters.

19. Tuple: An ordered collection of elements, similar to an array but immutable.

20. Variable: A named storage location in memory that holds a value.

21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.

Best Programming Resources: https://topmate.io/coding/898340

Join for more: https://t.iss.one/programming_guide

ENJOY LEARNING 👍👍

Python in a nutshell

Important Programming Languages