###### Brief Information

- Name : Machine Learning Foundations: A Case Study Approach
- Lecturer : Carlos Guestrin and Emily Fox
- Duration: 2015-10-22 ~ 11-02 (6 weeks) (~11-09)
- Course : The 1st (1/6) course of Machine Learning Specialization in Coursera
- Syllabus
- Record
- Certificate
- Learning outcome
- Identify potential applications of machine learning in practice.
- Describe the core differences in analyses enabled by regression, classification, and clustering.
- Select the appropriate machine learning task for a potential application.
- Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
- Represent your data as features to serve as input to machine learning models.
- Assess the model quality in terms of relevant error metrics for each task.
- Utilize a dataset to fit a model to analyze new data.
- Build an end-to-end application that uses machine learning at its core.
- Implement these techniques in Python.

###### Scores of Assignments

- Total score = 971 / 1000 = 0.98 (98%)
- I submitted two assignments late.So the real score is a little smaller than 0.98

#### Syllabus ↑

##### Week 1 | Welcome

###### Lecture

- Why you should learn machine learning with us
- Who this specialization is for and what you will be able to do
- Getting started with the tools for the course
- Getting started with Python and the IPython Notebook
- Getting started with SFrames for data engineering and analysis

##### Week 2 | Regression: Predicting House Prices

###### Lecture

- Linear regression modeling
- Evaluating regression models
- Summary of regression
- Predicting house prices: IPython Notebook
- Programming assignment

###### Quiz

- Quiz: Regression
- Quiz: Predicting house prices

##### Week 3 | Classification: Analyzing Sentiment

###### Lecture

- Classification modeling
- Evaluating classification models
- Summary of classification
- Analyzing sentiment: IPython Notebook
- Programming assignment

###### Quiz

- Quiz: Classification
- Quiz: Analyzing product sentiment

##### Week 4 | Clustering and Similarity: Retrieving Documents

###### Lecture

- Algorithms for retrieval and measuring similarity of documents
- Clustering models and algorithms
- Summary of clustering and similarity
- Document retrieval: IPython Notebook
- Programming assignment

###### Quiz

- Quiz: Clustering and Similarity
- Quiz: Retrieving Wikipedia articles

##### Week 5 | Recommending Products

###### Lecture

- Recommender systems
- Co-occurrence matrices for collaborative filtering
- Matrix factorization
- Performance metrics for recommender systems
- Summary of recommender systems
- Song recommender: IPython Notebook
- Programming assignment

###### Quiz

- Quiz: Recommender Systems
- Quiz: Recommending songs

##### Week 6 | Deep Learning: Searching for Images

Closing Remarks

###### Lecture

- Neural networks: Learning very non-linear features
- Deep learning & deep features
- Summary of deep learning
- Deep features for image classification: IPython Notebook
- Deep features for image retrieval: IPython Notebook
- Programming assignment
- Deploying machine learning as a service
- Machine learning challenges and future directions

###### Quiz

- Quiz: Deep Learning
- Quiz: Deep features for image retrieval