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If you just getting started in data science and machine learning, read these articles chronologically.
- Python for data science tutorial (Complete guide with examples and notebook)
- NumPy tutorial(Everything you need to know about NumPy with examples and video tutorials)
- Pandas tutorial (A complete guide with examples and notebook)
- Data visualization with Matplotlib
- Seaborn tutorial
- Linear regression in Python with Scikit-learn (With examples, code, and notebook)
- Logistic regression in Python with Scikit-learn
- Decision Trees and Random Forests(Building and optimizing decision tree and random forest models)
- Entropy, information gain, and Gini impurity(Decision tree splitting criteria)
- Streamlit tutorial(How to build machine learning applications)
- Gradio tutorial (Build machine learning applications)
- How to build artificial neural networks with Keras and TensorFlow
- How to build CNN in TensorFlow(examples, code, and notebooks)
- TensorFlow Recurrent Neural Networks (Complete guide with examples and code)
- How to build TensorFlow models with the Keras Functional API (Examples, code, and notebook)
- How to create custom training loops in Keras
- Object detection with TensorFlow 2 Object detection API
- TensorBoard tutorial (Deep dive with examples and notebook)
- Distributed training with TensorFlow: How to train Keras models on multiple GPUs
- Transfer learning guide(With examples for text and images in Keras and PyTorch)
- Create U-Net from scratch (Image segmentation with U-Net with Keras and TensorFlow)
- How to Generate Images with Variational Autoencoders(VAE) (Create VAE from scratch using Keras and TensorFlow)
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