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种子名称： [UdemyCourseDownloader] Data Science, Deep Learning, & Machine Learning with Python
文件类型： 视频
文件数目： 87个文件
文件大小： 3.07 GB
收录时间： 2019-6-8 06:12
已经下载： 3次
资源热度： 127
最近下载： 2024-12-15 07:42

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[UdemyCourseDownloader] Data Science, Deep Learning, & Machine Learning with Python.torrent

08 Apache Spark_ Machine Learning on Big Data/061 [Activity] Decision Trees in Spark.mp488.24MB

01 Getting Started/001 Introduction.mp427.73MB

01 Getting Started/002 [Activity] Getting What You Need.mp49.52MB

01 Getting Started/003 [Activity] Installing Enthought Canopy.mp440.51MB

01 Getting Started/004 Python Basics_ Part 1.mp443.97MB

01 Getting Started/005 [Activity] Python Basics_ Part 2.mp428.46MB

01 Getting Started/006 Running Python Scripts.mp421.57MB

01 Getting Started/007 Introducing the Pandas Library.mp443.34MB

02 Statistics and Probability Refresher_ and Python Practise/008 Types of Data.mp434.11MB

02 Statistics and Probability Refresher_ and Python Practise/009 Mean_ Median_ Mode.mp428.17MB

02 Statistics and Probability Refresher_ and Python Practise/010 [Activity] Using mean_ median_ and mode in Python.mp438.99MB

02 Statistics and Probability Refresher_ and Python Practise/011 [Activity] Variation and Standard Deviation.mp445.62MB

02 Statistics and Probability Refresher_ and Python Practise/012 Probability Density Function; Probability Mass Function.mp412.35MB

02 Statistics and Probability Refresher_ and Python Practise/013 Common Data Distributions.mp427.67MB

02 Statistics and Probability Refresher_ and Python Practise/014 [Activity] Percentiles and Moments.mp442.98MB

02 Statistics and Probability Refresher_ and Python Practise/015 [Activity] A Crash Course in matplotlib.mp449.28MB

02 Statistics and Probability Refresher_ and Python Practise/016 [Activity] Covariance and Correlation.mp449.78MB

02 Statistics and Probability Refresher_ and Python Practise/017 [Exercise] Conditional Probability.mp449.55MB

02 Statistics and Probability Refresher_ and Python Practise/018 Exercise Solution_ Conditional Probability of Purchase by Age.mp414.09MB

02 Statistics and Probability Refresher_ and Python Practise/019 Bayes' Theorem.mp430.13MB

03 Predictive Models/020 [Activity] Linear Regression.mp432.97MB

03 Predictive Models/021 [Activity] Polynomial Regression.mp429.78MB

03 Predictive Models/022 [Activity] Multivariate Regression_ and Predicting Car Prices.mp449.97MB

03 Predictive Models/023 Multi-Level Models.mp423.66MB

04 Machine Learning with Python/024 Supervised vs_ Unsupervised Learning_ and Train_Test.mp447.8MB

04 Machine Learning with Python/025 [Activity] Using Train_Test to Prevent Overfitting a Polynomial Regression.mp422.02MB

04 Machine Learning with Python/026 Bayesian Methods_ Concepts.mp419.41MB

04 Machine Learning with Python/027 [Activity] Implementing a Spam Classifier with Naive Bayes.mp436.48MB

04 Machine Learning with Python/028 K-Means Clustering.mp430.3MB

04 Machine Learning with Python/029 [Activity] Clustering people based on income and age.mp423.98MB

04 Machine Learning with Python/030 Measuring Entropy.mp417.48MB

04 Machine Learning with Python/032 Decision Trees_ Concepts.mp439.72MB

04 Machine Learning with Python/033 [Activity] Decision Trees_ Predicting Hiring Decisions.mp438.23MB

04 Machine Learning with Python/034 Ensemble Learning.mp433.7MB

04 Machine Learning with Python/035 Support Vector Machines (SVM) Overview.mp421.77MB

04 Machine Learning with Python/036 [Activity] Using SVM to cluster people using scikit-learn.mp420.84MB

05 Recommender Systems/037 User-Based Collaborative Filtering.mp436.81MB

05 Recommender Systems/038 Item-Based Collaborative Filtering.mp433.56MB

05 Recommender Systems/039 [Activity] Finding Movie Similarities.mp448.48MB

05 Recommender Systems/040 [Activity] Improving the Results of Movie Similarities.mp443.98MB

05 Recommender Systems/041 [Activity] Making Movie Recommendations to People.mp463.25MB

05 Recommender Systems/042 [Exercise] Improve the recommender's results.mp445.85MB

06 More Data Mining and Machine Learning Techniques/043 K-Nearest-Neighbors_ Concepts.mp419.9MB

06 More Data Mining and Machine Learning Techniques/044 [Activity] Using KNN to predict a rating for a movie.mp459.21MB

06 More Data Mining and Machine Learning Techniques/045 Dimensionality Reduction; Principal Component Analysis.mp435.67MB

06 More Data Mining and Machine Learning Techniques/046 [Activity] PCA Example with the Iris data set.mp453.31MB

06 More Data Mining and Machine Learning Techniques/047 Data Warehousing Overview_ ETL and ELT.mp454.11MB

06 More Data Mining and Machine Learning Techniques/048 Reinforcement Learning.mp463.56MB

07 Dealing with Real-World Data/049 Bias_Variance Tradeoff.mp431.23MB

07 Dealing with Real-World Data/050 [Activity] K-Fold Cross-Validation to avoid overfitting.mp445.87MB

07 Dealing with Real-World Data/051 Data Cleaning and Normalization.mp431.9MB

07 Dealing with Real-World Data/052 [Activity] Cleaning web log data.mp460.16MB

07 Dealing with Real-World Data/053 Normalizing numerical data.mp419.02MB

07 Dealing with Real-World Data/054 [Activity] Detecting outliers.mp439.04MB

08 Apache Spark_ Machine Learning on Big Data/056 [Activity] Installing Spark - Part 1.mp444.24MB

08 Apache Spark_ Machine Learning on Big Data/057 [Activity] Installing Spark - Part 2.mp485.81MB

08 Apache Spark_ Machine Learning on Big Data/058 Spark Introduction.mp432.46MB

08 Apache Spark_ Machine Learning on Big Data/059 Spark and the Resilient Distributed Dataset (RDD).mp436.41MB

08 Apache Spark_ Machine Learning on Big Data/060 Introducing MLLib.mp426.27MB

08 Apache Spark_ Machine Learning on Big Data/062 [Activity] K-Means Clustering in Spark.mp462.85MB

08 Apache Spark_ Machine Learning on Big Data/063 TF _ IDF.mp432.2MB

08 Apache Spark_ Machine Learning on Big Data/064 [Activity] Searching Wikipedia with Spark.mp459.76MB

08 Apache Spark_ Machine Learning on Big Data/065 [Activity] Using the Spark 2_0 DataFrame API for MLLib.mp430.53MB

09 Experimental Design/066 A_B Testing Concepts.mp442.69MB

09 Experimental Design/067 T-Tests and P-Values.mp430.11MB

09 Experimental Design/068 [Activity] Hands-on With T-Tests.mp440.06MB

09 Experimental Design/069 Determining How Long to Run an Experiment.mp416.68MB

09 Experimental Design/070 A_B Test Gotchas.mp443.78MB

10 Deep Learning and Neural Networks/071 Deep Learning Pre-Requisites.mp430.43MB

10 Deep Learning and Neural Networks/072 The History of Artificial Neural Networks.mp422.45MB

10 Deep Learning and Neural Networks/073 [Activity] Deep Learning in the Tensorflow Playground.mp449.01MB

10 Deep Learning and Neural Networks/074 Deep Learning Details.mp418.17MB

10 Deep Learning and Neural Networks/075 Introducing Tensorflow.mp425.44MB

10 Deep Learning and Neural Networks/076 [Activity] Using Tensorflow_ Part 1.mp428.35MB

10 Deep Learning and Neural Networks/077 [Activity] Using Tensorflow_ Part 2.mp434.22MB

10 Deep Learning and Neural Networks/078 [Activity] Introducing Keras.mp433.83MB

10 Deep Learning and Neural Networks/079 [Activity] Using Keras to Predict Political Affiliations.mp427.3MB

10 Deep Learning and Neural Networks/080 Convolutional Neural Networks (CNN's).mp423.99MB

10 Deep Learning and Neural Networks/081 [Activity] Using CNN's for handwriting recognition.mp422.43MB

10 Deep Learning and Neural Networks/082 Recurrent Neural Networks (RNN's).mp421.56MB

10 Deep Learning and Neural Networks/083 [Activity] Using a RNN for sentiment analysis.mp427.71MB

10 Deep Learning and Neural Networks/084 The Ethics of Deep Learning.mp453.66MB

10 Deep Learning and Neural Networks/085 Learning More about Deep Learning.mp426.18MB

11 Final Project/086 Your final project assignment.mp419.61MB

11 Final Project/087 Final project review.mp428.03MB

12 You made it!/088 More to Explore.mp445.14MB

12 You made it!/090 Bonus Lecture_ Discounts on my Spark and MapReduce courses!.mp424.32MB