# 2022-Machine-Learning-Specialization
吴恩达2022新版机器学习 machine learning specialization
课程官网：https://www.coursera.org/specializations/machine-learning-introduction
bilibili:https://www.bilibili.com/video/BV19B4y1W76i
github:https://github.com/kaieye/2022-Machine-Learning-Specialization
课程代码及测验内容已更新完毕
欢迎pull request，无论是补充学习文件还是优化md笔记
交流群：484266833
## 课程大纲
Machine learning specialization课程共分为三部分
- 第一部分：Supervised Machine Learning:Regression and Classification
- 第二部分：Advanced Learning Algorithms
- 第三部分：Unsupervised Learning:Recommenders, Reinforcement Learning
目前上传的是第二部分,course1的sildes(ppt)已更新完毕
Machine Learning Specialization by Andrew Ng in 2022
Course website:https://www.coursera.org/specializations/machine-learning-introduction
bilibili:https://www.bilibili.com/video/BV19B4y1W76i
github:https://github.com/kaieye/2022-Machine-Learning-Specialization
Course code and test content have been updated
welcome to pull request, whether it is to supplement learning files or markdown notes
## Course Outline
Machine learning specialization is divided into 3 parts
- Part 1：Supervised Machine Learning:Regression and Classification
- Part 2：Advanced Learning Algorithms
- Part 3：Unsupervised Learning:Recommenders, Reinforcement Learning
The second part is currently Uploaded
the slides of course1 have been updated
## 环境配置
按照操作系统类型安装python（官方使用的环境为3.7.6），安装方式各异。安装成功后在cmd/bash中定位到该文件夹，并使用如下命令安装依赖。
```text
pip install -r requirements.txt
```
mac/linux用户需将pip切换成pip3
Machine Learing Source Code
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2022-09-24
07:49:27
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Machine Learing Source Code （743个子文件） 
content_user_train.csv 11.66MB small_movies_Y.csv 8.07MB small_movies_R.csv 4.04MB content_item_train.csv 3.03MB small_movies_X.csv 509KB content_y_train.csv 227KB small_movie_list.csv 194KB content_item_vecs.csv 99KB small_movies_W.csv 49KB content_movie_list.csv 35KB small_movies_b.csv 5KB
C2_W1_Assign1_Broadcasting.gif 10.42MB C1_W2_Lab04_dot_notrans.gif 1.61MB lunar_lander.gif 416KB C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb 855KB C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln-checkpoint.ipynb 855KB C2_W1_Lab02_CoffeeRoasting_TF.ipynb 538KB C2_W1_Lab02_CoffeeRoasting_TF.ipynb 538KB C2_W1_Lab02_CoffeeRoasting_TF.ipynb 373KB C1_W3_Lab04_LogisticLoss_Soln.ipynb 315KB C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb 294KB C1_W3_Lab06_One_Vs_All_Soln.ipynb 272KB C1_W3_Lab06_One_Vs_All_user.ipynb 272KB C1_W3_Lab06_Gradient_Descent_Soln.ipynb 166KB C1_W3_Lab06_Gradient_Descent_Soln-checkpoint.ipynb 166KB C1_W3_Lab07_Overfitting_user.ipynb 152KB C1_W3_Lab07_Overfitting_Soln.ipynb 152KB C1_W3_Lab07_Overfitting_user.ipynb 152KB C1_W3_Lab07_Overfitting_Soln.ipynb 152KB C1_W3_Lab02_Sigmoid_function_Soln.ipynb 118KB C1_W3_Lab02_Sigmoid_function_Soln-checkpoint.ipynb 118KB C1_W3_Lab09_Regularization_Soln.ipynb 114KB C1_W3_Lab09_Regularization_Soln-checkpoint.ipynb 114KB C2_W1_Lab01_Neurons_and_Layers.ipynb 111KB C2_W1_Lab01_Neurons_and_Layers.ipynb 111KB C1_W3_Logistic_Regression.ipynb 83KB C1_W3_Lab08_Overfitting_Soln.ipynb 62KB C1_W2_Lab05_Sklearn_GD_Soln.ipynb 51KB C1_W2_Linear_Regression.ipynb 51KB C1_W2_Linear_Regression-checkpoint.ipynb 51KB C1_W3_Lab01_Classification_Soln.ipynb 48KB C2_W3_Assignment.ipynb 46KB C2_W3_Assignment.ipynb 46KB C1_W3_Lab03_Decision_Boundary_Soln.ipynb 45KB C3_W3_A1_Assignment.ipynb 44KB C3_W3_A1_Assignment-checkpoint.ipynb 44KB C1_W3_Lab08_Overfitting_Soln.ipynb 43KB C2_W1_Assignment.ipynb 41KB C1_W3_Lab03_Cost_Function_Soln.ipynb 37KB C1_W3_Lab03_Cost_Function_user.ipynb 36KB C2_W4_Decision_Tree_with_Markdown.ipynb 36KB C1_W3_Lab03_Cost_Function_Soln.ipynb 35KB C1_W3_Lab03_Cost_Function_user.ipynb 35KB C1_W2_Linear_Regression - 副本-checkpoint.ipynb 34KB C2_W2_Multiclass_TF.ipynb 34KB C2_W2_Multiclass_TF.ipynb 34KB C1_W3_Lab02_Decision_Boundary_Soln.ipynb 31KB C1_W3_Lab02_Decision_Boundary_user.ipynb 31KB C2_W2_Relu.ipynb 30KB C1_W3_Lab06_One_Vs_All_Soln.ipynb 30KB C3_W2_Collaborative_RecSys_Assignment.ipynb 30KB C3_W2_Collaborative_RecSys_Assignment-checkpoint.ipynb 30KB C1_W3_Lab06_One_Vs_All_user.ipynb 30KB C2_W2_Relu.ipynb 29KB C3_W1_KMeans_Assignment-checkpoint.ipynb 29KB C3_W1_KMeans_Assignment.ipynb 29KB State-action value function example.ipynb 29KB C3_W2_RecSysNN_Assignment.ipynb 29KB C3_W2_RecSysNN_Assignment-checkpoint.ipynb 29KB C3_W1_Anomaly_Detection.ipynb 28KB C2_W2_Assignment.ipynb 27KB C2_W2_SoftMax-Copy1.ipynb 26KB C2_W2_SoftMax-Copy1.ipynb 26KB C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb 25KB C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb 25KB C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb 25KB C1_W2_Lab02_Multiple_Variable_Soln.ipynb 24KB C1_W2_Lab02_Multiple_Variable_Soln.ipynb 24KB State-action value function example-checkpoint.ipynb 23KB C2_W2_SoftMax.ipynb 21KB C2_W2_SoftMax.ipynb 21KB C1_W1_Lab05_Gradient_Descent_Soln.ipynb 21KB C1_W1_Lab05_Gradient_Descent_Soln.ipynb 20KB C1_W3_Lab01_Sigmoid_function_Soln.ipynb 20KB C1_W3_Lab01_Sigmoid_function_user.ipynb 20KB C1_W1_Lab05_Gradient_Descent_Soln.ipynb 19KB C1_W3_Lab01_Sigmoid_function_Soln.ipynb 19KB C1_W3_Lab01_Sigmoid_function_user.ipynb 19KB C1_W3_Lab09_Regularization_Soln.ipynb 18KB C1_W3_Lab04_Gradient_Descent_Soln.ipynb 16KB C1_W3_Lab06_Gradient_Descent_Soln.ipynb 16KB C1_W3_Lab04_Gradient_Descent_user.ipynb 16KB C1_W3_Lab06_Gradient_Descent_Soln.ipynb 15KB C1_W3_Lab04_Gradient_Descent_Soln.ipynb 14KB C1_W3_Lab04_Gradient_Descent_user.ipynb 14KB C1_W1_Lab03_Model_Representation_Soln.ipynb 13KB C1_W1_Lab03_Model_Representation_Soln.ipynb 13KB C1_W1_Lab03_Model_Representation_Soln.ipynb 13KB C1_W3_Lab09_Regularized_Gradient_Descent_Soln.ipynb 12KB C1_W3_Lab09_Regularized_Gradient_Descent_user.ipynb 12KB共 743 条
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