    Machine Learning – Data science培训

Machine Learning introduction
Types of Machine learning – supervised vs unsupervised learning
From Statistical learning to Machine learning
The Data Mining workflow:
Business understanding
Data Understanding
Data preparation
Modelling
Evaluation
Deployment
Machine learning algorithms
Choosing appropriate algorithm to the problem
Overfitting and bias-variance tradeoff in ML
ML libraries and programming languages
Why use a programming language
Choosing between R and Python
Python crash course
Python resources
Python Libraries for Machine learning
Jupyter notebooks and interactive coding
Testing ML algorithms
Generalization and overfitting
Avoiding overfitting
Holdout method
Cross-Validation
Bootstrapping
Evaluating numerical predictions
Measures of accuracy: ME, MSE, RMSE, MAPE
Parameter and prediction stability
Evaluating classification algorithms
Accuracy and its problems
The confusion matrix
Unbalanced classes problem
Visualizing model performance
Profit curve
ROC curve
Lift curve
Model selection
Model tuning – grid search strategies
Examples in Python
Data preparation
Data import and storage
Understand the data – basic explorations
Data manipulations with pandas library
Data transformations – Data wrangling
Exploratory analysis
Missing observations – detection and solutions
Outliers – detection and strategies
Standarization, normalization, binarization
Qualitative data recoding
Examples in Python
Classification
Binary vs multiclass classification
Classification via mathematical functions
Linear discriminant functions
Quadratic discriminant functions
Logistic regression and probability approach
k-nearest neighbors
Naïve Bayes
Decision trees
CART
Bagging
Random Forests
Boosting
Xgboost
Support Vector Machines and kernels
Maximal Margin Classifier
Support Vector Machine
Ensemble learning
Examples in Python
Regression and numerical prediction
Least squares estimation
Variables selection techniques
Regularization and stability- L1, L2
Nonlinearities and generalized least squares
Polynomial regression
Regression splines
Regression trees
Examples in Python
Unsupervised learning
Clustering
Centroid-based clustering – k-means, k-medoids, PAM, CLARA
Hierarchical clustering – Diana, Agnes
Model-based clustering - EM
Self organising maps
Clusters evaluation and assessment
Dimensionality reduction
Principal component analysis and factor analysis
Singular value decomposition
Multidimensional Scaling
Examples in Python
Text mining
Preprocessing data
The bag-of-words model
Stemming and lemmization
Analyzing word frequencies
Sentiment analysis
Creating word clouds
Examples in Python
Recommendations engines and collaborative filtering
Recommendation data
User-based collaborative filtering
Item-based collaborative filtering
Examples in Python
Association pattern mining
Frequent itemsets algorithm
Market basket analysis
Examples in Python
Outlier Analysis
Extreme value analysis
Distance-based outlier detection
Density-based methods
High-dimensional outlier detection
Examples in Python
Machine Learning case study
Business problem understanding
Data preprocessing
Algorithm selection and tuning
Evaluation of findings
Deployment