A few years ago, I was studying Machine Learning in school at this moment. 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
Learningprocedures 学习过程
Data Set 数据集
train set 训练集
validation set 验证集
test set 测试集
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.
Have you ever been crazy for Machine Learning ?
Stay hungry, Stay foolish !
感谢关注，谢谢
