机器学习工程师纳米学位

Machine Learning Engineer Nanodegree

该学位旨在让学员成长为一名机器学习工程师，在各领域如金融，健康，教育等行业中，应用机器学习的各种算法来建模。学位完成所需的时长约为400小时。

预测世界料理

所需技能：python, jupyter notebook, numpy, sklearn, 自然语言处理，特征提取，网格搜索，逻辑回归
预测波士顿房价

所需技能：python, jupyter notebook, sklearn，统计，特征选择，网格搜索，交叉验证，训练模型，分析模型
监督学习：为慈善机构寻找捐助者

所需技能：python, jupyter notebook, sklearn，特征工程，数据预处理，探索性数据分析及可视化，比较各监督学习模型，训练模型并优化模型，参数调优，网格搜索
非监督学习：创建客户细分

所需技能：python, jupyter notebook, sklearn，特征工程，特征缩放，异常值检测, PCA，比较各非监督学习模型，创建聚类及可视化，模型回归与分类
深度学习： CNN算法进行狗的品种分类

所需技能：卷积神经网络，keras，迁移学习，CNN模型训练及参数优化，人脸检测，深度学习，python
强化学习：训练机器人走迷宫

所需技能：强化学习，Q-learning算法，python, 线性代数
毕业项目：预测欧洲连锁药妆店Rossmann的营业额

所需技能：数据挖掘，数据预处理，特征工程与选择，算法比较、选择与参数优化，模型回归，数据可视化，商业分析

Predict Your Cuisine

Load the data set (39774 rows for train set and 9944 rows for test set) from Kaggle competition; perform statistical analysis
Apply NLP techniques to pre-process the cuisine and ingredients
Perform Logistic Regression and grid search to train and optimize the model and then make prediction for the test set

Predict Boston’s Housing Price

Perform statistical analysis and feature engineering; split the data set and train the model with decision tree algorithm
Perform grid search and cross validation to optimize the model and make price prediction
Evaluate the coefficient of determination, the robustness and the applicability of the model

Supervised Learning – Finding Donors for CharityML

Perform EDA and data wrangling on the data set
Select 3 algorithms (Decision Tree, SVM and Adaboost) from 7 supervised learning techniques to train the model, compare and evaluate the models using different metrics and select the optimal model
Optimize the model and make the prediction

Unsupervised Learning – Customer Segments

Deep Learning – Dog Breed Classifier

Use OpenCV's classifier to detect human faces
Pre-process the data for CNN architecture
Build a CNN from scratch to predict the dogs' breeds
Apply a CNN model for transfer learning
Write and test my algorithm based on the optimum CNN model selected to classify dog breeds

Reinforcement Learning – Robot Maze

Create the maze; apply Q-learning algorithm to guide the robot’s movement in the maze
Use the most updated reinforcement technique to train a robot in a maze and avoid traps

Capstone Project – Predict Rossmann Store Sales

Download data sets from Kaggle and perform data wrangling preprocessing, EDA and data visualization
Perform feature engineering; conduct comparison, selection and optimize of different algorithms
Train the model and fine-tune the parameters to make prediction and visualization