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种子名称： [FreeCourseLab.com] Udemy - Machine Learning, Data Science and Deep Learning with Python
文件类型： 视频
文件数目： 87个文件
文件大小： 7.38 GB
收录时间： 2019-6-9 18:39
已经下载： 3次
资源热度： 155
最近下载： 2024-12-25 13:33

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

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

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

1. Getting Started/3. [Activity] Getting What You Need.mp428.06MB

1. Getting Started/4. [Activity] Installing Enthought Canopy.mp4109.01MB

1. Getting Started/5. Python Basics, Part 1 [Optional].mp4133.83MB

1. Getting Started/6. [Activity] Python Basics, Part 2 [Optional].mp477.2MB

1. Getting Started/7. Running Python Scripts [Optional].mp444.7MB

1. Getting Started/8. Introducing the Pandas Library [Optional].mp4127.88MB

10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp467.87MB

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

10. Deep Learning and Neural Networks/11. [Activity] Using CNN's for handwriting recognition.mp480.8MB

10. Deep Learning and Neural Networks/12. Recurrent Neural Networks (RNN's).mp469.17MB

10. Deep Learning and Neural Networks/13. [Activity] Using a RNN for sentiment analysis.mp494.77MB

10. Deep Learning and Neural Networks/14. The Ethics of Deep Learning.mp4128.25MB

10. Deep Learning and Neural Networks/15. 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.mp496.39MB

10. Deep Learning and Neural Networks/6. [Activity] Using Tensorflow, Part 1.mp4102.32MB

10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 2.mp4133.62MB

10. Deep Learning and Neural Networks/8. [Activity] Introducing Keras.mp4107.45MB

10. Deep Learning and Neural Networks/9. [Activity] Using Keras to Predict Political Affiliations.mp4104.32MB

11. Final Project/1. Your final project assignment.mp458.9MB

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

12. You made it!/1. More to Explore.mp464.07MB

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

2. Statistics and Probability Refresher, and Python Practise/10. [Exercise] Conditional Probability.mp4130.37MB

2. Statistics and Probability Refresher, and Python Practise/11. Exercise Solution Conditional Probability of Purchase by Age.mp428.74MB

2. Statistics and Probability Refresher, and Python Practise/12. Bayes' Theorem.mp458.9MB

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

2. Statistics and Probability Refresher, and Python Practise/3. [Activity] Using mean, median, and mode in Python.mp492.74MB

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

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

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

2. Statistics and Probability Refresher, and Python Practise/7. [Activity] Percentiles and Moments.mp4114.05MB

2. Statistics and Probability Refresher, and Python Practise/8. [Activity] A Crash Course in matplotlib.mp4129.36MB

2. Statistics and Probability Refresher, and Python Practise/9. [Activity] Covariance and Correlation.mp4116.75MB

3. Predictive Models/1. [Activity] Linear Regression.mp4100.47MB

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

3. Predictive Models/3. [Activity] Multivariate Regression, and Predicting Car Prices.mp4123.78MB

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

4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp498.62MB

4. Machine Learning with Python/10. [Activity] Decision Trees Predicting Hiring Decisions.mp495.95MB

4. Machine Learning with Python/11. Ensemble Learning.mp465.22MB

4. Machine Learning with Python/12. Support Vector Machines (SVM) Overview.mp444.74MB

4. Machine Learning with Python/13. [Activity] Using SVM to cluster people using scikit-learn.mp454.98MB

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.mp440.72MB

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.mp471.95MB

4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp457.3MB

4. Machine Learning with Python/7. Measuring Entropy.mp434.98MB

4. Machine Learning with Python/9. Decision Trees Concepts.mp486.53MB

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

5. Recommender Systems/2. Item-Based Collaborative Filtering.mp475.01MB

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

5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp494.87MB

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

5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp484.24MB

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.mp4142.07MB

6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis.mp467.75MB

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.mp4103.34MB

6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4132.27MB

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

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.mp4129.39MB

7. Dealing with Real-World Data/5. Normalizing numerical data.mp438.21MB

7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp483.6MB

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.mp4111.51MB

8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4113.82MB

8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.mp487.37MB

8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.mp4172.29MB

8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.mp489.87MB

8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp498.52MB

8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp454.75MB

8. Apache Spark Machine Learning on Big Data/8. [Activity] Decision Trees in Spark.mp4193.25MB

8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4133.83MB

9. Experimental Design/1. AB Testing Concepts.mp497.48MB

9. Experimental Design/2. T-Tests and P-Values.mp464.91MB

9. Experimental Design/3. [Activity] Hands-on With T-Tests.mp481.62MB

9. Experimental Design/4. Determining How Long to Run an Experiment.mp434.85MB

9. Experimental Design/5. AB Test Gotchas.mp496.1MB