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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
california_housing_prices's Introduction
Data science and Engineering Student at UC3M
📩 Connect with me: ![Gmail](https://camo.githubusercontent.com/71a0f4bfcf1f2220e2b1c246ac2ee681c47ee914d1c1f0e27a0e6c9ac2e9f134/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f476d61696c2d4431343833363f7374796c653d666f722d7468652d6261646765266c6f676f3d676d61696c266c6f676f436f6c6f723d7768697465)
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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.
- 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.
- 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.
- 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.
- 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.
- Statistics
- Probability
- Calculus
- Linear Algebra
- Numerical Methods
- Statistical Processing for Signals
- Signals and Systems
- Cryptography
- Discrete Mathematics
- 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.
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