Practical Machine Learning Engineer projects build into the 16 Week Machine Learning Engineer Certification - FourthBrain.
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Consumer Behavioral Intention: Analysis of consumer behavioral, applying techniques for supervised and unsupervised learning, as well as semi-supervised learning. (Midterm Project)
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Attention is all you need: Application of Attention Mechanism to Neural Networks for news classification, implementation using keras.
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NLP task: TF-IDF, Word2Vec, NaiveBayes Classifier and BiLSTM Application of different NLP techniques for Hate speech detection, application of Error Analysis for Label validation.
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Fewshot Learning for Object Detection: Fewshot Learning using PyTorch and torchvision + Finetuning .
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Clustering and Semi Supervised learning for customer segmentation: Clustering and semi-supervised learning for customer segmentation using Sklearn.
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Logistic Regression + SVM + XGBoost and reference to SHAP: Applied of common MLE techniques into a dataset of electronics purchase.
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PySpark Classification Models and Usage: Prediction of Subscriber using PySaprk in Big Data Setup, usage of different tools for data wrangling and Model training setup.
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Usage of Neural Networks for prediction + AutoML with TPOT: Application of linear regresion, Neural Networks and AutoML.
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Sentiment Analysis using Huggingface pretrained model + Reddit API Usage: Analysis of the sentiment of some reddit posts, using the framework of Hugging Face and Reddit API.
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Exploratory Data Analysis: Exploratory data analysis of the dataset from Walmart sales, using the framework of Pandas, Matplotlob and Sklearn. Example of a EDA process