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# System prepended metadata

title: CoderSchool FTMLE (Editable)
tags: [' Mariana', CoderSchool]

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---
title: CoderSchool FTMLE (Editable)
tags: CoderSchool, Mariana
---

# Editable version
## If you need to personalize it, create your own HackMD and copy/past this following markdown code

# 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.

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## 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.

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## 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). 

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## 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*].  -->

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## 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*]. -->

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## 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

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## 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.*] -->

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## 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).

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## Week 9: Recommendation System and Representation Learning

* Recommendation system, Embedding Space, Collaborative Filtering, Matrix Factorization
* Representation Learning, Autoencoder
* Encoder, Decoder and RNN in NLP

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## Week 10: 


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