#Machine Learning Algorithmic Improvements

Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai

Kadane Algorithm. #instagram #reels #tech #coding #softwareengineer #programming #dsa

I’ve been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start. Comment down below “TRAIN” and I’ll send you a more in-depth checklist along with the best GitHub links to help you start learning each concept. If you don’t receive the link you either need to follow first then comment, or your instagram is outdated. Either way, no worries. send me a dm and I’ll get it to you ASAP. #cs #ai #dev #university #softwareengineer #viral #advice #machinelearning

Have you heard about Machine Learning? Well applied it could guide your strategy to profit: Want to start in ML for trading here are some ideas: 1. Forecast the stocks 2. Make a sentiment analysis tool to predict if a market could be bullish or bearish 3. Predict the outcome of your next trade Do you want to learn?

How machine learning trading algorithms actually work, explained in 60 seconds. The models, the data, the process. #MachineLearning #AlgorithmicTrading #QuantitativeFinance #FinancialAlgorithms #Trading

Comment “statistics” and I’ll send you the link. Learn machine learning statistics the easy way with interactive visual experiments on topics like regression, probability distributions, and more. See real-time simulations to understand how concepts work.

Here’s the framework we’ve developed to explain how algorithms work based on our experience and testing for clients. While the exact mechanisms are closely guarded secrets at companies like Meta, LinkedIn, Twitter, and others, we’ve distilled our insights to provide you with a clear understanding. Curious about leveraging algorithm to enhance your business? Connect with us to take your media game to the next level.

Get started with algorithmic trading? It’s the process of using code and data to automate trading decisions—based on logic, historical patterns, or machine learning. If you're learning or building in this space, here are some of the **most common projects** you'll come across: Popular Algorithmic Trading Projects: - Time Series Forecasting - Anomaly Detection - Pairs Trading - Volatility Modeling - Sentiment-Based Trading - Machine Learning Stock Screener - Reinforcement Learning Agents - Backtesting Engines Popular ML Models Used to build these projects! - LSTM – for time series forecasting of price and volatility - Random Forest & XGBoost – classification/regression using engineered financial features - Autoencoders – detect anomalies in returns or market behavior - Isolation Forest – flag outliers in price or volume - ARIMA/GARCH – model linear trends and volatility clustering in financial data - Transformers – for multi-stock forecasting or analyzing textual data like earnings reports Want help building one? Get access to my Machine Learning Engineer roadmap and let’s break the stock market! Comment “algorithm” and I’ll send you the access!

not all of these methods are ML some are classic statistics and plain math lol, quant finance is no joke when it comes to math so if you’re interested in this — learn math #ai #machinelearning #math #quantfinance #algotrading #datascientist #coding #careertips

Many non-CS students struggle with machine learning not because they lack ability, but because they follow learning paths designed for CS graduates. That was my mistake early on. I tried to replicate standard CS-oriented roadmaps: – jumping straight into libraries and frameworks – completing ML courses back-to-back – building projects that ran correctly but were difficult to explain – focusing on outputs instead of understanding On the surface, it felt like progress. In reality, my understanding was shallow. The problem was not the effort, but the order of learning and i wasted months this way. Most CS roadmaps assume: – prior exposure to algorithms and data structures – comfort with mathematical abstractions – experience translating theory into code Non-CS students often lack this context, so copying the same sequence leads to confusion rather than clarity. The turning point for me was restructuring how I approached ML: – prioritizing mathematical foundations before models – building intuition before using high-level libraries – implementing algorithms from scratch to understand their mechanics – focusing on depth instead of breadth Once I changed the sequence, machine learning became more coherent. Concepts connected logically, and projects became explainable rather than mechanical. This distinction matters if you are a non-CS student aiming to break into ML. In the next part, I will outline the exact learning structure I would follow if I were starting again today. I’ve also compiled this approach into a complete roadmap for non-CS learners. You can find it linked in my bio if you want the full framework.

🚀 Dijkstra’s Algorithm – The Shortest Path Unveiled! 🌍🔗 Ever wondered how Google Maps finds the fastest route? Or how networks optimize data transfer? 📍 The answer lies in Dijkstra’s Algorithm – a powerful greedy algorithm that finds the shortest path in a graph with non-negative weights! ⚡ How it Works: ✅ Assign each node a distance value (∞ for all except the source = 0) ✅ Use a priority queue (Min Heap) to explore the nearest node first ✅ Relax edges by updating distances if a shorter path is found ✅ Repeat until all shortest paths are discovered! 🔹 Time Complexity: O((V + E) log V) using a Min Heap 🔹 Use Cases: GPS navigation, network routing, AI pathfinding, and more! 🎥 Watch this step-by-step visualization to master Dijkstra’s Algorithm! 💡 Save this for later & share with a fellow coder! #computerscience #programming #coding #technology #programmer #python #computer #developer #tech #coder #javascript #java #codinglife #html #code #softwaredeveloper #webdeveloper #software #cybersecurity #linux #webdevelopment #computerengineering #softwareengineer #hacking #engineering #machinelearning #datascience #css #programmers #pythonprogramming

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