种子简介

种子名称： [FreeCourseSite.com] Udemy - Machine Learning, Data Science and Deep Learning with Python
文件类型： 视频
文件数目： 104个文件
文件大小： 7.58 GB
收录时间： 2019-11-29 22:57
已经下载： 3次
资源热度： 214
最近下载： 2024-11-14 11:08

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:5071a8b07c27b203f564c086f04ddf35f4d59a5b&dn=[FreeCourseSite.com] Udemy - Machine Learning, Data Science and Deep Learning with Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[FreeCourseSite.com] Udemy - Machine Learning, Data Science and Deep Learning with Python.torrent

1. Getting Started/1. Introduction.mp459.6MB

1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].mp421.12MB

1. Getting Started/11. Introducing the Pandas Library [Optional].mp4123.1MB

1. Getting Started/2. Udemy 101 Getting the Most From This Course.mp419.77MB

1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4102.76MB

1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp496.53MB

1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp480.21MB

1. Getting Started/7. Python Basics, Part 1 [Optional].mp432.98MB

1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].mp420.63MB

1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].mp410.09MB

10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp474.17MB

10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.mp488.2MB

10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).mp493.09MB

10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.mp469.56MB

10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).mp469.17MB

10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.mp481.36MB

10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.mp4115.26MB

10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp418.43MB

10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.mp433.64MB

10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.mp4128.24MB

10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.mp438.64MB

10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.mp479.98MB

10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.mp4141.58MB

10. Deep Learning and Neural Networks/4. Deep Learning Details.mp464.22MB

10. Deep Learning and Neural Networks/5. Introducing Tensorflow.mp486.27MB

10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.mp472.69MB

10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.mp4108.64MB

10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.mp492.05MB

11. Final Project/1. Your final project assignment.mp451.63MB

11. Final Project/2. Final project review.mp498.5MB

12. You made it!/1. More to Explore.mp464.06MB

2. Statistics and Probability Refresher, and Python Practice/1. Types of Data.mp477.25MB

2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.mp4116.74MB

2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.mp4125.14MB

2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.mp422MB

2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.mp458.9MB

2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.mp456.15MB

2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.mp461.93MB

2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.mp4110.86MB

2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.mp430.07MB

2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions.mp475.37MB

2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.mp4114.04MB

2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.mp4129.35MB

2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.mp4147.81MB

3. Predictive Models/1. [Activity] Linear Regression.mp4100.46MB

3. Predictive Models/2. [Activity] Polynomial Regression.mp466.77MB

3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.mp473.85MB

3. Predictive Models/4. Multi-Level Models.mp447.47MB

4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp498.61MB

4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.mp47.05MB

4. Machine Learning with Python/11. Decision Trees Concepts.mp486.53MB

4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.mp495.95MB

4. Machine Learning with Python/13. Ensemble Learning.mp465.21MB

4. Machine Learning with Python/14. Support Vector Machines (SVM) Overview.mp444.74MB

4. Machine Learning with Python/15. [Activity] Using SVM to cluster people using scikit-learn.mp443.94MB

4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp458.14MB

4. Machine Learning with Python/3. Bayesian Methods Concepts.mp440.73MB

4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp489.09MB

4. Machine Learning with Python/5. K-Means Clustering.mp471.94MB

4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp457.29MB

4. Machine Learning with Python/7. Measuring Entropy.mp434.97MB

4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.mp42.06MB

4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.mp414.83MB

5. Recommender Systems/1. User-Based Collaborative Filtering.mp486.37MB

5. Recommender Systems/2. Item-Based Collaborative Filtering.mp475MB

5. Recommender Systems/3. [Activity] Finding Movie Similarities.mp4107.83MB

5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp494.86MB

5. Recommender Systems/5. [Activity] Making Movie Recommendations to People.mp4132.55MB

5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp484.23MB

6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.mp440.28MB

6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.mp4142.06MB

6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis.mp467.74MB

6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.mp4109.73MB

6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.mp4103.33MB

6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4132.26MB

6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp477.96MB

6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.mp414.84MB

6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp425.79MB

7. Dealing with Real-World Data/1. BiasVariance Tradeoff.mp466.31MB

7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp447.91MB

7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4102.34MB

7. Dealing with Real-World Data/3. Data Cleaning and Normalization.mp478.75MB

7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.mp4129.38MB

7. Dealing with Real-World Data/5. Normalizing numerical data.mp438.2MB

7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp436.32MB

7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.mp441.71MB

7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.mp449.02MB

7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp436.34MB

8. Apache Spark Machine Learning on Big Data/10. TF IDF.mp468.85MB

8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.mp4102.99MB

8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4105.68MB

8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.mp483.63MB

8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.mp4111.98MB

8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.mp489.86MB

8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp498.51MB

8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp454.74MB

8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.mp4134.02MB

8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4117.86MB

9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.mp433.04MB

9. Experimental Design ML in the Real World/2. AB Testing Concepts.mp497.49MB

9. Experimental Design ML in the Real World/3. T-Tests and P-Values.mp464.92MB

9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.mp481.62MB

9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.mp434.84MB

9. Experimental Design ML in the Real World/6. AB Test Gotchas.mp496.1MB