A few years ago, I was studying Machine Learning in school. In that time, I feel that playing Machine Learning is the best thing in the world, but what's the most unacceptable is that when it's applied to reality, it's much more complicated than I could think.
There are some contents for beginners:
Supervised Learning 监督学习
Unsupervised Learning 无监督学习
Reinforcement Learning 强化学习
Top 10 machine learning algorithms 10大机器学习算法
Decision tree 决策树／判定树
K-Means Clustering K –均值聚类
K-Nearest Neighbor Algorithm/KNN K近邻算法
Support Vector Machine/SVM 支持向量机
Naive Bayes Classifier 朴素贝叶斯分类器
Gradient Boost 和 Adaboost 算法
Random Forest Algorithm 随机森林算法
Neural Network 神经网络
Markov Chains马尔可夫链
Logistic Regression逻辑回归
数据集Data Set
训练集 train set
验证集validation set
测试集 test set
Training Models 训练模型
Loss Function损失函数
Optimization Algorithms 优化算法
Gradient Descent Method 梯度下降法
Newtonian method 牛顿法
Momentum动量
Nesterov Momentum
Adagrad Adaptive Gradient
Adam Adaptive Moment Estimation
Estimate model 评估模型
Accuracy 准确率
Precision 精确率
Recall 召回率
True Positive Rate 真阳性率
Mean Square Error (MSE, RMSE) 平均方差
Absolute Error (MAE, RAE) 绝对误差
The above is just the basic content about machine learning.
Stay hungry, Stay foolish !
感谢关注，谢谢
