In this course, you will get hands-on experience with machine learning from a series of practical case-studies. Through hands-on practice with these cases, you will be able to apply machine learning methods in a wide range of domains. This course can be found at Coursera.
- Represent the data set as features to serve as input in a machine learning model.
- Implement the techniques in Jupyter Notebook.
- Apply regression, classification, clustering, retrieval, recommender systems and deep learning.
- The core differences in analysis by regression, classification and clustering.
- Identify potential application of machine learning in practice.
- Improving analyzing skills.
- Getting started with Python, Jupyter Notebook & Turi Create
- Getting started with SFrames for data engineering and analysis
- Doing week 1 exercies: Getting started with SFrames assignment
- Download WSL on Windows10 for running Jupyter Notebook and Turi Create
- Linear regression modeling
- Evaluating regression models
- Summary of regression
- pip install matplotlib
- Doing week 2 exercise: Predicting House Prices
- Classification modeling
- Evaluating calssification models
- Summary of classification
- Doing week 3 exercise: Analyzing Sentiment
- Algorithms for retrieval and measuring similarity of documents
- Clustering models and algorithms
- Summary of clustering and similarity
- Doing week 4 exercise: Retrieving Wikipedia Articles
- About recommender systems
- Co-occurrence matrices for collaborative filtering
- Matrix factorization
- Performance metrics for recommender systems
- Summary for recommender systems
- Doing week 5 exercise: Recommending Songs System
- Neural networks: Learning very non-linear features
- Deep learning & deep features
- Summary of deep learning
- Deep features for image classification
- Doing week 6 exercise: Deep Features for Image Retrieval