Machine Learning

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Machine Learning by Mind Map: Machine Learning

1. Role Vs Task Vs Skill

1.1. Role

1.1.1. Data Analyst Analyzing data in order to tell a story and produce actionable insights

1.1.2. Machine Learning Enginnering Working software and consists of other software components that run autonomosly with minimal human supervision

1.2. Task

1.2.1. Regression Exp: Regression trees, Linear regression

1.2.2. Classification Exp: Decision trees, Support vector machine

1.2.3. Clustering Exp: Mean-shift(High accuracy), Hierarchical clustering

1.2.4. Multivariate Querying Exp: Nearest neighbors, Range Search

1.2.5. Density Estimation Exp: Kernel density estimation(High accuracy), Mixture of Gaussians

1.2.6. Dimension Reduction Exp: Manifold learning/KPCA(High accuracy), Principal component analysis

1.2.7. Testing and Matching Exp: Minimum spanning tree

1.3. Skill

1.3.1. Computer Science Fundamental and Programming Exp: Data Structures(stacks, queues),Python/C++/R/Java, algorithms(Searching, Sorting)

1.3.2. Probability and Statistics Exp: conditional probability, Bayer rule, distributions(Normal, Binomial, Poisson), analysis methods(ANOVA, hypothesis testing), measures(Mean, Median, Variance)

1.3.3. Data Modeling and Evaluation Exp: correlations, clusters, eigenvectors, classification, regression, anomaly detection

1.3.4. Applying Machine Learning Algorithms and Libraries Exp: libraries/packages/APIs(e.g. scikit-learn, Theano, Spark MLib, H2O, TensorFlow)

1.3.5. Software Engineering and System Design Exp: analysis, system design, modularity, version control, testig, documentaion

2. Technical skills

2.1. Computer Science's skills

2.1.1. SQL Database – able to write and execute complex queries in SQL

2.2. Anaylytics skill

2.2.1. R – analytics tool

2.3. Programming experience

2.3.1. Python, Java, R

2.4. Statistics and Probability

2.4.1. Prior knowledge in this would help in problem solving

2.5. Basic Linear Algebra

2.5.1. Regression analysis

3. Self-learning resources

3.1. Online courses

3.1.1. Coursera,Udacity, Khan Academy

3.2. Kaggle

3.2.1. Competitions - World most elite machine learning leaderboard Datasets - Analyze public datasets Kernels - Run code in cloud and receive community feedback

3.3. MOOC

3.3.1. Codecademy

3.4. Video Tutorial

3.4.1. Youtube

4. Soft skills

4.1. Intellectual curiosity

4.1.1. A data scientist should invest time and energy in getting into machine learning knowledge

4.2. Communication skills

4.2.1. Needed to translate technical finding to non-technical team such as Marketing departments

4.3. Business acumen

4.3.1. Understanding in industry and company on how to make profit and effective to strategy execution