ML Engineer Python

Skills

Python Programming: Proficiency in Python is essential, as it's the primary language used in machine learning and data science. You should be comfortable with data structures, object-oriented programming, and libraries like NumPy, Pandas, and Matplotlib.

Mathematics and Statistics: A solid understanding of linear algebra, calculus, probability, and statistics forms the foundation of machine learning.

Machine Learning Algorithms: You should have a deep understanding of various machine learning algorithms, including supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and reinforcement learning

Machine Learning Libraries: Experience with popular machine learning libraries like scikit-learn, TensorFlow, and PyTorch is necessary.

Data Preprocessing and Feature Engineering: Knowledge of data preprocessing techniques such as cleaning, normalization, encoding, and feature scaling is required.

Deep Learning: Familiarity with deep learning concepts and architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models is beneficial.

Software Development Skills: Proficiency in software development practices

Problem Solving Skills and Continuous Learning