Archive For The “Data Scientist” Category

Natural Language Processing

Natural Language Processing

Introduction to Statistical NLP Techniques, Word Embeddings, Introduction to Sequential models, NLP Applications   Introduction to Statistical NLP Techniques 1.       Introduction to NLP 2.       Pre-processing in NLP-Tokenization, Stop words, Normalisation,stemg and lemmatization 3.       Pre-processing in NLP-Bag of words, TF-IDF as features 4.       Language Models Probabilistic models, N-gram model and channel model 5.       Hands On Lab…

Computer Vision

Computer Vision

Introduction, Working with Images, CNN Building Blocks, CNN Architectures, Transfer Learning, Visualizations, Semantic Segmentation and Object Detection, Bounding box regressor, CNNs at work: Siamese Network for Metric Learning   Introduction – ·         Courseoutline -Computer Vision.pdf   Working with Images 1.       Working with Images_Introduction 2.       Working with Images – Digitization, Sampling, and Quantization 3.       Working with…

Neural Network & Deep Learning

Neural Network & Deep Learning

Introduction to Neural Network and Deep Learning, Mathematics for Deep Learning : Linear Algebra part 1, Math for Deep Learning : Functions and Convex optimization, Introduction to Loss Functions, Neural Networks deconstructed for Supervised Learning (Classification), Building Blocks of Neural Networks, TensorFlow, Keras, and Tensorboard, Babysitting the Neural Network Introduction to Neural Network and Deep…

Python For Deep Learning

Python For Deep Learning

Python for Deep Learning, Introduction to NumPy, Introduction to Pandas, Object Oriented Programming Concepts, Other Python Libraries, Mathematics – Probability, Vectors and Matrices, Functions Python for Deep Learning 1.       Introduction to Python 2.       Basic Installation of Python 3.       Keyboard shortcuts and installing packages 4.       How To Install and work with Jupyter notebook    Introduction to…

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