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Example of nd-to-end Machine Learning Problem: Frame problem, select features , select metrics, analyze data, feature engineering ,clean data, prepare data, pipelines transformations, build mode, hyperparamter tuning , test_model, save model, confidence interval for error estimation, deployment

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california_housing_prices's Introduction

Hi, I'm Daniel Totolici❗

Data science and Engineering Student at UC3M

✅ All of my projects are available at My Portfolio

📩 Connect with me: Gmail

I’m currently learning 📚

tf

django

react



Tech Stack 💻

Vim

JavaScript

R

Python

MySQL

Git

Docker

Spark

Arch Linux




Abilities 🧠

Data Manipulation and Analysis 🔄

  • Pandas: Expert in data cleaning, manipulation, and preprocessing.
  • NumPy: Proficient in numerical computations and array operations.
  • Dplyr: Experienced in data manipulation and transformation in R.

Data Visualization 📊

  • Matplotlib: Skilled in creating static, animated, and interactive visualizations in Python.
  • Seaborn: Proficient in statistical data visualization based on Matplotlib.
  • Plotly: Experienced in interactive visualizations and dashboards.
  • ggplot2: Expert in data visualization in R.

Machine Learning 🤖

  • Scikit-learn: Proficient in implementing and tuning machine learning algorithms.
  • TensorFlow: Experienced in building and training deep learning models.
  • Keras: Skilled in high-level neural network API, running on top of TensorFlow.
  • PyTorch: Knowledgeable in building deep learning models and custom neural networks.

Deep Learning 🧬

  • Convolutional Neural Networks (CNNs): Expertise in image classification and object detection.
  • Recurrent Neural Networks (RNNs): Experienced in sequence modeling and time series prediction.
  • Natural Language Processing (NLP): Skilled in text processing, sentiment analysis, and language models.
  • Recommendation Systems: Skilled in recommendation systems, collaborative filtering, and content-based filtering.

Statistical Analysis 📈

  • Descriptive Statistics: Proficient in summarizing and describing data.
  • Inferential Statistics: Skilled in hypothesis testing, confidence intervals, and regression analysis.
  • Probability: Skilled in probability, stochastic processes, Markov chains.

Mathematics

  • Statistics
  • Probability
  • Calculus
  • Linear Algebra
  • Numerical Methods
  • Statistical Processing for Signals
  • Signals and Systems
  • Cryptography
  • Discrete Mathematics

Additional Skills 🛠️

  • A/B Testing: Proficient in designing and analyzing A/B tests for experimental evaluation.
  • Time Series Analysis: Skilled in analyzing and forecasting temporal data.
  • Predictive Modelling: Skilled in creating linear models, multiple variable linear models, logistic regression.
  • Web Development: Skilled in creating websites with HTML, CSS, and JS/React.
  • Backend Development: Skilled in creating RESTful APIs with Django/Flask.
  • Data Structures and Algorithms: Skilled in using data structures and creating efficient algorithms.
  • Optimization: Experienced in mathematical optimization techniques for model tuning.

Socials 🌐

LinkedIn Kaggle LeetCode

california_housing_prices's People

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