--- title: CoderSchool FTMLE tags: CoderSchool, Mariana --- # Overview **Course Name:** Full-time Bootcamp Machine Learning Engineer **Duration:** 12 weeks **Hours:** 10am-6pm Monday to Friday **Delivered** Face-to-Face with Online Blended Activities **Instructed by** Hai Minh Do (Google Partner) [Linkedin](https://www.linkedin.com/in/haiminhdo/) | [Github](https://github.com/dhminh1024) # Coursework ## Week 1: Environment Setup + Basic Python * Git & Github * Bash Command Line * VSCode * Jupyter Notebook & Google Colab * Basic **Python** 🐍: Finished 100 Repl.it Python exercises * Web Scraping with [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) ### Weekly Project: Deploy a web-crawler that takesa [Tiki](https://tiki.vn) URL as an input and return a dataframe. --- ## Week 2: Intermediate Python + Database Fundamental * Data Structure and Algorithms * REGEX and String manipulation with Python * Relational Database (PostgreSQL) * SQL Queries ### Weekly Project - Build a web-crawler that crawls 10 pages from each main category in [Tiki](https://tiki.vn) - Store the data in PostgresSQL database. - Analyse production information on Tiki. --- ## Week 3: Data Analysis & Visualization with Pandas, Matplotlib & Seaborn + Google Data Studio * Fundamental Statistics * Data Manipulation with `Pandas` * Data visualization `Matplotlib` and `Seaborn` * Making Dashboard with `Google Data Studio` ### Weekly Project - Collect, clean, analyze datasets from [Kaggle](kaggle.com), produce insights and visualize findingsa with [Google Data Studio](https://datastudio.google.com). --- ## Week 4: Machine Learning Basics * Defining & Solving Regression Problems * Simple Linear Regression * Multiple Linear Regression * Polynomial Linear Regression * Overfitting vs Underfitting * Evaluation Metrics * Basic Natural Language Processing or [NLP](https://en.wikipedia.org/wiki/Natural_language_processing) * [Bag of Words](https://machinelearningmastery.com/gentle-introduction-bag-words-model/)/ [TF-IDF](https://towardsdatascience.com/natural-language-processing-feature-engineering-using-tf-idf-e8b9d00e7e76)/ [Word2Vec](https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa)/ [Random Forest](https://towardsdatascience.com/understanding-random-forest-58381e0602d2) ### Weekly Project [Sentiment Analysis on Movie Reviews](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews) - a Kaggle challenge. <!-- Achieved accuracy of [*your accuracy here*]. --> --- ## Week 5: Intermediate Machine Learning * How to Structure a Machine Learning Project * [Support Vector Machine](https://medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72) & [K Nearest Neighbors](https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761) * [Decision Tree](https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052) - [Random Forest](https://towardsdatascience.com/understanding-random-forest-58381e0602d2) * [Unsupervised Learning](https://en.wikipedia.org/wiki/Unsupervised_learning) (K-Means/Clustering Method/Hierarchical Clustering/PCA) ### Weekly Project [Fashion MNIST](https://www.kaggle.com/zalando-research/fashionmnist) - a Kaggle challenge. <!-- Achieved accuracy of [*your accuracy here*]. --> --- ## Week 6: Neural Network and Deep Neural Network * Intro to Deep Learning * Neural Network from scratch * Deep Neural Network or DNN from scratch * Improving DNNs ### Weekly Project - Cat / Dog Classifier: Develop a `Flask` app that classifies whether the input image shows a "Cat" or a "Dog" - Allow the user to confirm or disprove the prediction. - Store users' feedback in a PostgreSQL database --- ## Week 7: Deep Learning in Project * The mechanics of `TensorFlow` * Inspecting and monitoring models using `Keras Callbacks` and `Tensorboard` * Convolutional Neural Network or [CNN](https://en.wikipedia.org/wiki/Unsupervised_learning) * Transfer Learning and Fine-tuning pretrained models ### Weekly Project Define a Problem ➡️ Collect Data ➡️ Modeling ➡️ Hyperparameter Turning ➡️ Build a `Flask` application. <!-- [*Details of your project here.*] --> --- ## Week 8: OpenCV, 'Magic Calculator', RNN, LSTM, CRNN * Intro to `OpenCV` * `OpenCV` intermediate * [Magic Calculator](https://github.com/hoahh2201/ilovemath_project) * RNN, LSTM + Sentiment Analysis Model for IMBD movie reviews * CRNN + CAPTCHA/font handwritten recognition ### Weekly Project Employing CRNN (Convolutional Recurrent Neural Network) and CTC (Connectionist Temporal Classification) to recognize Vietnamese handwritten addresses, a [CinnamonAI Challenge](https://drive.google.com/drive/folders/1Qa2YA6w6V5MaNV-qxqhsHHoYFRK5JB39?fbclid=IwAR01dHUA2QttdDgNXBNWloqOtJ0XZj_9_Zbq-6hqyhVAeZQlVfwnnJFZO2Y). --- ## Week 9: Recommendation System and Representation Learning * Recommendation system, Embedding Space, Collaborative Filtering, Matrix Factorization * Representation Learning, Autoencoder * Encoder, Decoder and RNN in NLP --- ## Week 10: ---