Machine Learning Foundations: A Case Study Approach | Machine Learning Specialization | Coursera | Course

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

ML Foundations) Assignments) Result Score
ML Foundations) Assignments) Scores


 Syllabus

Week 1 | Welcome
Lecture
  1. Why you should learn machine learning with us
  2. Who this specialization is for and what you will be able to do
  3. Getting started with the tools for the course
  4. Getting started with Python and the IPython Notebook
  5. Getting started with SFrames for data engineering and analysis
Week 2 | Regression: Predicting House Prices
Lecture
  1. Linear regression modeling
  2. Evaluating regression models
  3. Summary of regression
  4. Predicting house prices: IPython Notebook
  5. Programming assignment
Quiz
  1. Quiz: Regression
  2. Quiz: Predicting house prices
Week 3 | Classification: Analyzing Sentiment
Lecture
  1. Classification modeling
  2. Evaluating classification models
  3. Summary of classification
  4. Analyzing sentiment: IPython Notebook
  5. Programming assignment
Quiz
  1. Quiz: Classification
  2. Quiz: Analyzing product sentiment
Week 4 | Clustering and Similarity: Retrieving Documents
Lecture
  1. Algorithms for retrieval and measuring similarity of documents
  2. Clustering models and algorithms
  3. Summary of clustering and similarity
  4. Document retrieval: IPython Notebook
  5. Programming assignment
Quiz
  1. Quiz: Clustering and Similarity
  2. Quiz: Retrieving Wikipedia articles
Week 5 | Recommending Products
Lecture
  1. Recommender systems
  2. Co-occurrence matrices for collaborative filtering
  3. Matrix factorization
  4. Performance metrics for recommender systems
  5. Summary of recommender systems
  6. Song recommender: IPython Notebook
  7. Programming assignment
Quiz
  1. Quiz: Recommender Systems
  2. Quiz: Recommending songs
Week 6 | Deep Learning: Searching for Images
Closing Remarks
Lecture
  1. Neural networks: Learning very non-linear features
  2. Deep learning & deep features
  3. Summary of deep learning
  4. Deep features for image classification: IPython Notebook
  5. Deep features for image retrieval: IPython Notebook
  6. Programming assignment
  7. Deploying machine learning as a service
  8. Machine learning challenges and future directions
Quiz
  1. Quiz: Deep Learning
  2. Quiz: Deep features for image retrieval

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