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Kazim Ali
£9/hr
Contact
teacher
£9/hr
Contact
Kazim Ali
Town/city/borough London
English as a foreign language Lessons
verified Verified data time 1 year teaching experience
Price
Price
£9/hr
Level of the lessons
Level of the lessons
Secondary school
GCSE
University students
Adults
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I have completed a Bachelor of Science from the University of Punjab Lahore, M.Sc in Computer Science from Government College University Lahore (Pakistan), MS/MPhil in Computer Science from Lahore Leads University, and PhD in Computer Science from the Uni

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Deep Learning Skills and Knowledge • Artificial Neural Networks • Convolutional Neural Networks (CNNs) • Deep Learning for Text – Embeddings • Recurrent Neural Networks • LSTMs • Transformer Models • Machine Learning (Supervised and Un-Supervised) Skills and Knowledge • Exploratory Data Analysis and Visualization • Linear Regression • Auto-regression • Logistic Regression • Classification Using K-Nearest Neighbors • Classification Using Decision Trees • Ensemble Modeling • One-Hot Encoding • Bagging • Boosting • Stacking • Model Evaluation • k-means Clustering • Hierarchical Clustering • Principal Component Analysis • Topic Modeling • Market Basket Analysis • Computer Visio Skills and knowledge • Deep learning for Computer Vision • Performing Image Classification • Video Classification • Convolutional Neural Network • The AlexNet model • The VGG-16 model • The Google Inception-V3 model • The Microsoft ResNet-50 model • The SqueezeNet model • The DenseNet model • Pre-Trained Networks with Transfer Learning • Styling Images with DeepDream • Neural Style Transfer • Image Super-Resolution • Reducing Noise with Autoencoders • Generative Adversarial Network • Pix2Pix Model • CycleGAN • Adversarial Attacks • Captioning Images with CNNs and RNNs • Images Segmentation with U-Net • Localizing and Detecting in Images • Deep Learning to Videos • Natural Language Processing Skills and Knowledge WORK EXPERIENCE • Tokenization • PoS Tagging • Stop Word Removal • Text Normalization • Spelling Correction • Stemming • Lemmatization • Named Entity Recognition (NER) • Word Sense Disambiguation • Bag of Words (BoW) • Term Frequency–Inverse Document Frequency (TFIDF) • Developing a Text Classifier • Collecting Text Data with Web Scraping and APIs • Topic Modeling • Vector Representation • Text Generation • Text Summarization • Sentiment Analysis
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