24、python-机器学习-进阶实战

编辑点评

实战导向，涵盖数据特征工程、GBDT、降维、贝叶斯优化、隐马尔可夫模型等高级内容，适合有一定基础的学习者。

⭐ 编辑推荐

本课程深入讲解Python机器学习进阶实战，通过实际项目案例，帮助学习者掌握高级机器学习技巧。

✨ 课程亮点

课程目录

📁 唐宇迪-机器学习-进阶实战-资料
📁 1.数据特征
📁 数值特征
📁 .ipynb_checkpoints
Feature Engineering on Numeric Data-checkpoint.ipynb [238.7KB]
Feature Engineering on Temporal Data-checkpoint.ipynb [23.1KB]
Feature Engineering on Text Data-checkpoint.ipynb [47.6KB]
Feature Selection-checkpoint.ipynb [29.2KB]
数值特征-checkpoint.ipynb [216.9KB]
图像特征-checkpoint.ipynb [791.8KB]
特征预处理-checkpoint.ipynb [11.7KB]
文本特征-checkpoint.ipynb [42.5KB]
📁 datasets
cat.png [119.5KB]
desktop.ini [138B]
dog.png [54.6KB]
fcc_2016_coder_survey_subset.csv [2.6M]
item_popularity.csv [147B]
Pokemon.csv [46.9KB]
song_views.csv [27.8KB]
vgsales.csv [1.3M]
数值特征.ipynb [217.2KB]
图像特征.ipynb [259.5KB]
特征预处理.ipynb [11.7KB]
文本特征.ipynb [44.5KB]
📁 2.GBDT提升算法
GBDT.pdf [763.8KB]
📁 3.xgboost-gbdt-lightgbm
GBDT.zip [51.6M]
📁 4.使用lightgbm进行饭店流量预测
GBDT【整理不易‖记得关注：CunWorKNotes】.zip [51.6M]
📁 5.人口普查数据集项目实战-收入预测
1.png [186.7KB]
2.png [62.6KB]
3.png [698.3KB]
机器学习数据分析模板.ipynb [4.2M]
📁 6.降维算法-线性判别分析
9-LDA与PCA算法.pdf [1M]
降维算法.zip [443.7KB]
📁 7.贝叶斯优化及其工具包使用
贝叶斯优化：Hyperopt.zip [17.4M]
📁 8.贝叶斯优化实战
贝叶斯优化：Hyperopt.zip [17.4M]
📁 9.EM算法
10-EM算法.pdf [811.4KB]
📁 10.HMM隐马尔科夫模型
HMM.pdf [1M]
📁 11.HMM案例实战
📁 HMM
📁 __pycache__
data.cpython-36.pyc [216B]
get_hmm_param.cpython-36.pyc [2.4KB]
data.py [318B]
get_hmm_param.py [2.6KB]
hmm_start.py [823B]
data2.csv [130.7KB]
hmm实践.ipynb [5.8KB]
时间序列.ipynb [189KB]
📁 12.推荐系统
推荐系统.pdf [2.1M]
📁 13.音乐推荐系统实战
📁 Python实现音乐推荐系统
📁 .ipynb_checkpoints
推荐系统-checkpoint.ipynb [344.8KB]
📁 __pycache__
Recommenders.cpython-36.pyc [5KB]
1.png [45.3KB]
2.png [30.4KB]
3.png [43KB]
4.png [12KB]
5.png [3.6KB]
6.png [60.3KB]
7.png [77.3KB]
8.png [68.8KB]
老版.ipynb [344.8KB]
recommendation_engines.py [13.7KB]
Recommenders.py [9.2KB]
song_playcount_df.csv [8.5M]
推荐系统.ipynb [363.5KB]
track_metadata.db [711.6M]
track_metadata_df_sub.csv [5.9M]
train_triplets.txt [2.8G]
triplet_dataset_sub_song.csv [648.3M]
user_playcount_df.csv [44.1M]
📁 14.基于统计分析的电影推荐
电影推荐.zip [10M]
📁 15.学习曲线
📁 学习曲线
📁 .ipynb_checkpoints
学习曲线（Bias和Variance）-checkpoint.ipynb [137.1KB]
1.png [52.1KB]
2.png [36KB]
3.png [56.1KB]
4.png [24.9KB]
5.png [45.1KB]
6.png [93.1KB]
7.png [75.3KB]
8.png [58.4KB]
9.png [5.2KB]
10.png [39.9KB]
11.png [67.5KB]
Folds5x2_pp.xlsx [1.9M]
学习曲线（Bias和Variance）.ipynb [137.1KB]
📁 16.NLP-文本特征方法对比
clean_data.csv [1.1M]
GoogleNews-vectors-negative300.bin [3.4G]
socialmedia_relevant_cols.csv [1.2M]
socialmedia_relevant_cols_clean.csv [1.2M]
socialmedia_relevant_cols_clean2.csv [1.2M]
自然语言处理方法对比.ipynb [1M]
📁 17.使用word2vec分类任务
word2vec.zip [84.6M]
📁 18.Tensorflow自己打造word2vec
📁 word2vec
word2vec.zip [32.6M]
📁 19.制作自己常用工具包
📁 特征筛选
📁 .ipynb_checkpoints
Feature Selector Usage-checkpoint.ipynb [433.5KB]
工具-checkpoint.ipynb [353.3KB]
📁 __pycache__
feature_selector.cpython-36.pyc [19.5KB]
📁 data
AirQualityUCI.csv [615.1KB]
caravan-insurance-challenge.csv [1.7M]
credit_example.csv [5.2M]
📁 feature_selector
__init__.py [46B]
feature_selector.py [27.9KB]
📁 images
工具.ipynb [396KB]
📁 20.数据处理与特征工程
📁 机器学习项目实战流程
📁 .ipynb_checkpoints
Exploratory_Work-checkpoint.ipynb [1.4M]
机器学习项目实战-1-数据预处理-checkpoint.ipynb [1.2M]
机器学习项目实战-2-建模-checkpoint.ipynb [362KB]
机器学习项目实战-3-分析-checkpoint.ipynb [3.6M]
📁 auto_ml
tpot_exported_pipeline.py [1.3KB]
📁 data
cleaned_data.csv [6.4M]
Energy_and_Water_Data_Disclosure_for_Local_Law_84_2017__Data_for_Calendar_Year_2016_.csv [7.9M]
no_score.csv [373.3KB]
testing_features.csv [571.8KB]
testing_labels.csv [16.6KB]
training_features.csv [1.3M]
training_labels.csv [38.8KB]
X_test.csv [802.9KB]
X_train.csv [1.8M]
Y_test.csv [13.9KB]
Y_train.csv [32.3KB]
📁 images
annotated_individual_node.PNG [84.6KB]
correlation_examples.png [15.4KB]
cover_auto_ml.jpg [221.3KB]
cover_one.jpg [259.6KB]
cover_three.jpg [703.4KB]
cover_two.jpg [255KB]
data_formatted_with_score.PNG [22.5KB]
density_boroughs.png [108KB]
density_type.png [94.8KB]
df_info.PNG [41.2KB]
feature_importances.PNG [16.9KB]
feature_importances_graph.png [84KB]
feature_pairs.png [145.4KB]
formatted_train_data.PNG [41.4KB]
individual_node.png [7.2KB]
intrepretability_vs_accuracy.png [50.3KB]
kfold_cv.png [80.5KB]
lime_wrong_explanation.PNG [30.7KB]
local_explanation_one.png [92.5KB]
missing_values.PNG [23.5KB]
2016_nyc_benchmarking_data_disclosure_definitions.pdf [99KB]
Building Data Report.pdf [769.6KB]
hw_assignment.docx [13.9KB]
hw_assignment.pdf [12.4KB]
机器学习项目实战-1-数据预处理.ipynb [1.2M]
机器学习项目实战-2-建模.ipynb [318.2KB]
机器学习项目实战-3-分析.ipynb [3.6M]
1 数据特征.mp4 [236.1M]
2 GBDT提升算法【优质资源‖关注：cunWorkNotes 解锁】.mp4 [62.1M]
4 使用lightgbm进行饭店流【资源精选‖更多关注：CunworkNotes】.mp4 [105M]
5 人口普查数据集项目实战.mp4 [221.1M]
6 降维算法-线性判别分析.mp4 [82.5M]
7 贝叶斯优化及其工具包使用.mp4 [125.9M]
8 贝叶斯优化实战.mp4 [93.8M]
10 HMM隐马尔科夫模型.mp4 [117.2M]
11 HMM案例实战.mp4 [66.6M]
12 推荐系统.mp4 [71.6M]
13 音乐推荐系统实战【资源精选‖更多关注：CunworkNotes】.mp4 [208.4M]
14 基于统计分析的电影推荐.mp4 [233.2M]
16 NLP-文本特征方法对比.mp4 [140.9M]
17 使用word2vec分类任务.mp4 [156.2M]
20 机器学习项目实战-数据处.mp4 [195.6M]
21 机器学习项目实战-建模与.mp4 [154.6M]

适合人群

• 机器学习爱好者
• 有一定基础的学习者
• 希望进阶的从业者

🎯 学习收获

• 掌握高级机器学习算法
• 提升数据特征工程能力
• 解决实际机器学习问题

🎉 祝您学习愉快！