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种子名称： [DesireCourse.Net] Udemy - Machine Learning, Data Science and Deep Learning with Python
文件类型： 视频
文件数目： 105个文件
文件大小： 9.39 GB
收录时间： 2021-9-22 15:12
已经下载： 3次
资源热度： 190
最近下载： 2024-12-13 09:59

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[DesireCourse.Net] Udemy - Machine Learning, Data Science and Deep Learning with Python.torrent

1. Getting Started/1. Introduction.mp459.61MB

1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].mp421.13MB

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

1. Getting Started/2. Udemy 101 Getting the Most From This Course.mp419.78MB

1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4102.77MB

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.mp480.22MB

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].mp410.08MB

10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp474.18MB

10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.mp488.19MB

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.mp469.57MB

10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).mp469.18MB

10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.mp481.37MB

10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.mp4115.27MB

10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp418.44MB

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.mp4128.25MB

10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.mp438.65MB

10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.mp479.99MB

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.mp464.16MB

10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.mp4118.23MB

10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.mp4104.55MB

10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.mp492.06MB

11. Final Project/1. Your final project assignment.mp451.64MB

11. Final Project/2. Final project review.mp498.51MB

12. You made it!/1. More to Explore.mp4104.69MB

2. Statistics and Probability Refresher, and Python Practice/1. Types of Data.mp4112.17MB

2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.mp4167.41MB

2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.mp4125.15MB

2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.mp422.01MB

2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.mp486.08MB

2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.mp482.7MB

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.mp4156.35MB

2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.mp442.49MB

2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions.mp4105.82MB

2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.mp4159.62MB

2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.mp4182.27MB

2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.mp4147.82MB

3. Predictive Models/1. [Activity] Linear Regression.mp4144.36MB

3. Predictive Models/2. [Activity] Polynomial Regression.mp496.61MB

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

3. Predictive Models/4. Multi-Level Models.mp469.52MB

4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp4144.92MB

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

4. Machine Learning with Python/11. Decision Trees Concepts.mp4125.19MB

4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.mp4134.38MB

4. Machine Learning with Python/13. Ensemble Learning.mp495.42MB

4. Machine Learning with Python/14. [Activity] XGBoost.mp4102.09MB

4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.mp465.67MB

4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.mp446.71MB

4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp482.5MB

4. Machine Learning with Python/3. Bayesian Methods Concepts.mp458.05MB

4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4125.19MB

4. Machine Learning with Python/5. K-Means Clustering.mp4104MB

4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp482.36MB

4. Machine Learning with Python/7. Measuring Entropy.mp452.08MB

4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.mp42.07MB

4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.mp414.84MB

5. Recommender Systems/1. User-Based Collaborative Filtering.mp4124.65MB

5. Recommender Systems/2. Item-Based Collaborative Filtering.mp4108.57MB

5. Recommender Systems/3. [Activity] Finding Movie Similarities.mp4152.01MB

5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp4136.49MB

5. Recommender Systems/5. [Activity] Making Movie Recommendations to People.mp4188.51MB

5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp4128.73MB

6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.mp459.24MB

6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.mp4200.58MB

6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis.mp498.55MB

6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.mp4155.8MB

6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.mp4150.65MB

6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4189.7MB

6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp477.97MB

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).mp425.12MB

7. Dealing with Real-World Data/1. BiasVariance Tradeoff.mp495.81MB

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.mp476.1MB

7. Dealing with Real-World Data/3. Data Cleaning and Normalization.mp4112.36MB

7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.mp4190.99MB

7. Dealing with Real-World Data/5. Normalizing numerical data.mp456.9MB

7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp436.33MB

7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.mp441.72MB

7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.mp449.03MB

7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp436.35MB

8. Apache Spark Machine Learning on Big Data/10. TF IDF.mp4100.04MB

8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.mp4103MB

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.mp4127.54MB

8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp4140.62MB

8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp481.23MB

8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.mp4134.01MB

8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4117.87MB

9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.mp433.05MB

9. Experimental Design ML in the Real World/2. AB Testing Concepts.mp4141.84MB

9. Experimental Design ML in the Real World/3. T-Tests and P-Values.mp495.66MB

9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.mp4113.84MB

9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.mp451.33MB

9. Experimental Design ML in the Real World/6. AB Test Gotchas.mp4139.78MB