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To work in AI and machine learning, you need a mix of technical, mathematical, and soft skills. Here are the key ones:
1. Mathematics & Statistics - Linear Algebra – or understanding vectors, matrices, and tensors in ML models.Probability & Statistics – for data distribution, Bayesian inference, and hypothesis testing.Calculus – optimization techniques like gradient descent.
2. Programming & Software Development - Python (main language) – libraries like TensorFlow, PyTorch, Scikit-learn.R – useful for statistical computing.SQL – for handling structured data.C++/Java – for performance-intensive AI applications.
3. Data Handling & Processing - Data Wrangling – cleaning and transforming raw data.Big Data Technologies – Hadoop, Spark, or cloud platforms (AWS, Google Cloud).Feature Engineering – selecting and transforming variables for models.
4. Machine Learning & Deep Learning - Supervised & Unsupervised Learning – regression, classification, clustering.Neural Networks – deep learning with CNNs, RNNs, GANs.NLP (Natural Language Processing) – transformers (BERT, GPT).
5. Model Evaluation & Optimization - Hyperparameter Tuning – Grid Search, Random Search.Bias-Variance Tradeoff – understanding overfitting and underfitting. Cross-Validation – improving model generalization.
Phone - +65 66018888
Address - Block AS8, 10 Kent Ridge Crescent, #03-01 Singapore 119260
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