1.2M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Infotech Tutorials On Machine Learning Algorithms
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CHHere’s your full roadmap on how to get into machine learning. Comment “Roadmap” to get the pdf.
Save and follow for more.
#ai #machinelearning #coding #programming #cs
@chrispathway
1.3M
SAI’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If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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
@sambhav_athreya
7.4K
M0All machine learning algorithms part-14
#coding #python #ai #artificialintelligence #programming #datascience #machinelearning #pythonprogramming #deeplearning #neuralnetworks
@m0hit_ai
3.0M
ITcomment ‘AI’ and I’ll send you the link in your DMs
this is such a great resource to guide you on your AI/ML journey!
#techcareer #ai #machinelearning #careergrowthtips #datascience #coding
@itsallykrinsky
23.7K
FR📍Day 68: 9 machine learning algorithms every data scientist should know👇
1. Linear Regression: Used for predicting a continuous value. It’s simple yet effective for various problems.
2. Logistic Regression: Despite its name, it’s used for classification tasks, particularly binary classification. And I also use class probabilities (class proba), which is the probability of the class label.
3. Decision Trees: Used for both classification and regression tasks. They split data into branches to form a tree structure.
4. Gradient Boosting Machines (GBM): An ensemble technique that builds predictive models in a stage-wise fashion, often yielding high-quality predictions. I use these frequently for high accuracy and performance.
5. Random Forests: An ensemble method that uses a collection of decision trees to improve prediction accuracy and avoid overfitting.
6. Support Vector Machines (SVM): Primarily used for classification tasks, SVMs are effective in high-dimensional spaces.
7. K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm used for classification and regression.
8. Naive Bayes: A group of simple, probabilistic classifiers based on applying Bayes’ theorem with strong independence assumptions.
9. Neural Networks: Versatile and powerful, used for a wide range of tasks including classification, regression, and unsupervised learning. Deep learning models, a subset of neural networks, are particularly notable for their performance in complex tasks like image and speech recognition.
Hashtags (ignore):
#datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code
@freakz.ai
79.6K
CAMachine Learning Crash Course by Google 😍 Free
Just comment “Google”
@careerwalaa_
310.6K
COThe best way to learn the ML math and algorithms 😍
#selftaught #machinelearning #ai #tech #data #softwareengineer #coding #programmers
@codersheary
474.0K
THWant to become a Machine Learning Engineer in 2025?
Build real projects that reflect how ML is done in the industry:
1 → End-to-End ML Pipeline
Predict something useful (like student dropout risk). Clean with Pandas, train with LightGBM, deploy with FastAPI + Docker + AWS.
2 → RAG Chatbot
Build a chatbot that answers from your course notes. Use LlamaIndex + FAISS + Llama 3.1. This is how GenAI apps work today.
3 → Fine-Tune LLMs
Take an open-source LLM and fine-tune it on your own dataset. Use QLoRA with PEFT. Example: medical Q&A bot.
4 → Model Monitoring
Build a fraud detection model and track drift post-deployment using Evidently AI + Weights & Biases. Shows you think beyond training.
5 → Multimodal AI App
Photo → nutrition info + recipe. Use CLIP or Florence-2 for vision-text, connect to LLaVA or Qwen-VL, deploy with Streamlit.
This stack hits every part of the ML lifecycle—from classic ML to GenAI to production monitoring.
[mlprojects, machinelearningengineer, genai, fine-tuning, ragchatbot, mlportfolio, endtoendpipeline, multimodalai, ai2025, llmengineer, mljobs, mlworkflow, productionai]
@the.datascience.gal
12.1K
KAWhat is xgboost in machine learning and why it beats random forest #ml #datascientist
@karinadatascientist
134.9K
AI📍Machine learning algorithms every data scientist must know👇
1. Linear Regression: Used for predicting a continuous value. It’s simple yet effective for various problems.
2. Logistic Regression: Despite its name, it’s used for classification tasks, particularly binary classification. And I also use class probabilities (class proba), which is the probability of the class label.
3. Decision Trees: Used for both classification and regression tasks. They split data into branches to form a tree structure.
4. Gradient Boosting Machines (GBM): An ensemble technique that builds predictive models in a stage-wise fashion, often yielding high-quality predictions. I use these frequently for high accuracy and performance.
5. Random Forests: An ensemble method that uses a collection of decision trees to improve prediction accuracy and avoid overfitting.
6. Support Vector Machines (SVM): Primarily used for classification tasks, SVMs are effective in high-dimensional spaces.
7. K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm used for classification and regression.
8. Naive Bayes: A group of simple, probabilistic classifiers based on applying Bayes’ theorem with strong independence assumptions.
9. Neural Networks: Versatile and powerful, used for a wide range of tasks including classification, regression, and unsupervised learning. Deep learning models, a subset of neural networks, are particularly notable for their performance in complex tasks like image and speech recognition.
Hashtags (ignore):
#datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code
@aiwithanju
511.9K
ITthis is the software side of robotics of course there’s a whole other piece to make the robots work #ai #machinelearning #datascientist #machinelearningengineer #robotics #techcareer #careergrowthtips
@itsallykrinsky
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