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Udemy - Machine Learning, Data Science and Deep Learning with Python

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Udemy - Machine Learning, Data Science and Deep Learning with Python (Size: 7.7 GB)
  1. BiasVariance Tradeoff.mp4 66.3 MB
  1. BiasVariance Tradeoff.srt 14.4 KB
  1. Deep Learning Pre-Requisites.mp4 74.2 MB
  1. Deep Learning Pre-Requisites.srt 21.5 KB
  1. Deploying Models to Real-Time Systems.mp4 33 MB
  1. Deploying Models to Real-Time Systems.srt 15.4 KB
  1. Introduction.mp4 59.6 MB
  1. Introduction.srt 4.7 KB
  1. K-Nearest-Neighbors Concepts.mp4 40.3 MB
  1. K-Nearest-Neighbors Concepts.srt 8.9 KB
  1. More to Explore.mp4 64.1 MB
  1. More to Explore.srt 7.2 KB
  1. Supervised vs. Unsupervised Learning, and TrainTest.mp4 98.6 MB
  1. Supervised vs. Unsupervised Learning, and TrainTest.srt 20.9 KB
  1. Types of Data.mp4 77.2 MB
  1. Types of Data.srt 16.2 KB
  1. User-Based Collaborative Filtering.mp4 86.4 MB
  1. User-Based Collaborative Filtering.srt 19.4 KB
  1. Warning about Java 11 and Spark 3!.html 614 B
  1. Your final project assignment.mp4 51.6 MB
  1. Your final project assignment.srt 11.6 KB
  1. [Activity] Linear Regression.mp4 100.5 MB
  1. [Activity] Linear Regression.srt 25.7 KB
  10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4 47.9 MB
  10. Binning, Transforming, Encoding, Scaling, and Shuffling.srt 14.2 KB
  10. TF IDF.mp4 68.8 MB
  10. TF IDF.srt 14 KB
  10. [Activity] Covariance and Correlation.mp4 116.7 MB
  10. [Activity] Covariance and Correlation.srt 25.9 KB
  10. [Activity] LINUX Installing Graphviz.mp4 7 MB
  10. [Activity] LINUX Installing Graphviz.srt 1.1 KB
  10. [Activity] Python Basics, Part 4 [Optional].mp4 21.1 MB
  10. [Activity] Python Basics, Part 4 [Optional].srt 21.1 MB
  10. [Activity] Using Keras to Predict Political Affiliations.mp4 88.2 MB
  10. [Activity] Using Keras to Predict Political Affiliations.srt 21.1 KB
  11. Convolutional Neural Networks (CNN's).mp4 93.1 MB
  11. Convolutional Neural Networks (CNN's).srt 19.9 KB
  11. Decision Trees Concepts.mp4 86.5 MB
  11. Decision Trees Concepts.srt 21.1 KB
  11. Introducing the Pandas Library [Optional].mp4 123.1 MB
  11. Introducing the Pandas Library [Optional].srt 18 KB
  11. [Activity] Searching Wikipedia with Spark.mp4 103 MB
  11. [Activity] Searching Wikipedia with Spark.srt 12.9 KB
  11. [Exercise] Conditional Probability.mp4 125.1 MB
  11. [Exercise] Conditional Probability.srt 28.4 KB
  12. Exercise Solution Conditional Probability of Purchase by Age.mp4 22 MB
  12. Exercise Solution Conditional Probability of Purchase by Age.srt 4 KB
  12. [Activity] Decision Trees Predicting Hiring Decisions.mp4 95.9 MB
  12. [Activity] Decision Trees Predicting Hiring Decisions.srt 22.4 KB
  12. [Activity] Using CNN's for handwriting recognition.mp4 69.6 MB
  12. [Activity] Using CNN's for handwriting recognition.srt 13.8 KB
  12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4 105.7 MB
  12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.srt 13.9 KB
  13. Bayes' Theorem.mp4 58.9 MB
  13. Bayes' Theorem.srt 11.5 KB
  13. Ensemble Learning.mp4 65.2 MB
  13. Ensemble Learning.srt 14.5 KB
  13. Recurrent Neural Networks (RNN's).mp4 69.2 MB
  13. Recurrent Neural Networks (RNN's).srt 18.5 KB
  14. Support Vector Machines (SVM) Overview.mp4 44.7 MB
  14. Support Vector Machines (SVM) Overview.srt 9.9 KB
  14. [Activity] Using a RNN for sentiment analysis.mp4 81.4 MB
  14. [Activity] Using a RNN for sentiment analysis.srt 16.8 KB
  15. [Activity] Transfer Learning.mp4 115.3 MB
  15. [Activity] Transfer Learning.srt 21.5 KB
  15. [Activity] Using SVM to cluster people using scikit-learn.mp4 46.7 MB
  15. [Activity] Using SVM to cluster people using scikit-learn.srt 16.7 KB
  16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4 18.4 MB
  16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.srt 8.3 KB
  17. Deep Learning Regularization with Dropout and Early Stopping.mp4 33.6 MB
  17. Deep Learning Regularization with Dropout and Early Stopping.srt 12 KB
  18. The Ethics of Deep Learning.mp4 128.2 MB
  18. The Ethics of Deep Learning.srt 19.8 KB
  19. Learning More about Deep Learning.mp4 38.6 MB
  19. Learning More about Deep Learning.srt 3.1 KB
  2. AB Testing Concepts.mp4 97.5 MB
  2. AB Testing Concepts.srt 20.2 KB
  2. Don't Forget to Leave a Rating!.html 614 B
  2. Final project review.mp4 98.5 MB
  2. Final project review.srt 24.5 KB
  2. Item-Based Collaborative Filtering.mp4 75 MB
  2. Item-Based Collaborative Filtering.srt 20 KB
  2. Mean, Median, Mode.mp4 56.1 MB
  2. Mean, Median, Mode.srt 13 KB
  2. Spark installation notes for MacOS and Linux users.html 3.6 KB
  2. The History of Artificial Neural Networks.mp4 80 MB
  2. The History of Artificial Neural Networks.srt 19.1 KB
  2. Udemy 101 Getting the Most From This Course.mp4 19.8 MB
  2. Udemy 101 Getting the Most From This Course.srt 4 KB
  2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4 102.3 MB
  2. [Activity] K-Fold Cross-Validation to avoid overfitting.srt 24.5 KB
  2. [Activity] Polynomial Regression.mp4 66.8 MB
  2. [Activity] Polynomial Regression.srt 17.6 KB
  2. [Activity] Using KNN to predict a rating for a movie.mp4 142.1 MB
  2. [Activity] Using KNN to predict a rating for a movie.srt 28.5 KB
  2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4 58.1 MB
  2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.srt 13.1 KB
  3. Bayesian Methods Concepts.mp4 40.7 MB
  3. Bayesian Methods Concepts.srt 8.8 KB
  3. Bonus Lecture More courses to explore!.html 7.4 KB
  3. Data Cleaning and Normalization.mp4 78.7 MB
  3. Data Cleaning and Normalization.srt 17.1 KB
  3. Dimensionality Reduction; Principal Component Analysis.mp4 67.7 MB
  3. Dimensionality Reduction; Principal Component Analysis.srt 12.3 KB
  3. Installation Getting Started.html 307 B
  3. T-Tests and P-Values.mp4 64.9 MB
  3. T-Tests and P-Values.srt 13.2 KB
  3. [Activity] Deep Learning in the Tensorflow Playground.mp4 141.6 MB
  3. [Activity] Deep Learning in the Tensorflow Playground.srt 19.7 KB
  3. [Activity] Finding Movie Similarities.mp4 107.8 MB
  3. [Activity] Finding Movie Similarities.srt 20.1 KB
  3. [Activity] Installing Spark - Part 1.mp4 83.6 MB
  3. [Activity] Installing Spark - Part 1.srt 12 KB
  3. [Activity] Multiple Regression, and Predicting Car Prices.mp4 73.9 MB
  3. [Activity] Multiple Regression, and Predicting Car Prices.srt 21.1 KB
  3. [Activity] Using mean, median, and mode in Python.mp4 61.9 MB
  3. [Activity] Using mean, median, and mode in Python.srt 15 KB
  3.1 winutils.exe.html 102 B
  4. Deep Learning Details.mp4 64.2 MB
  4. Deep Learning Details.srt 16.8 KB
  4. Multi-Level Models.mp4 47.5 MB
  4. Multi-Level Models.srt 10.7 KB
  4. [Activity] Cleaning web log data.mp4 129.4 MB
  4. [Activity] Cleaning web log data.srt 23.8 KB
  4. [Activity] Hands-on With T-Tests.mp4 81.6 MB
  4. [Activity] Hands-on With T-Tests.srt 13.7 KB
  4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4 89.1 MB
  4. [Activity] Implementing a Spam Classifier with Naive Bayes.srt 17.4 KB
  4. [Activity] Improving the Results of Movie Similarities.mp4 94.9 MB
  4. [Activity] Improving the Results of Movie Similarities.srt 16.8 KB
  4. [Activity] Installing Spark - Part 2.mp4 112 MB
  4. [Activity] Installing Spark - Part 2.srt 10.6 KB
  4. [Activity] PCA Example with the Iris data set.mp4 109.7 MB
  4. [Activity] PCA Example with the Iris data set.srt 21.2 KB
  4. [Activity] Variation and Standard Deviation.mp4 110.8 MB
  4. [Activity] Variation and Standard Deviation.srt 25.8 KB
  4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4 102.8 MB
  4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.srt 18.9 KB
  4.1 winutils.exe.html 102 B
  5. Data Warehousing Overview ETL and ELT.mp4 103.3 MB
  5. Data Warehousing Overview ETL and ELT.srt 19.7 KB
  5. Determining How Long to Run an Experiment.mp4 34.8 MB
  5. Determining How Long to Run an Experiment.srt 8.3 KB
  5. Introducing Tensorflow.mp4 64.2 MB
  5. Introducing Tensorflow.srt 20.2 KB
  5. K-Means Clustering.mp4 71.9 MB
  5. K-Means Clustering.srt 17.2 KB
  5. Normalizing numerical data.mp4 38.2 MB
  5. Normalizing numerical data.srt 7.7 KB
  5. Probability Density Function; Probability Mass Function.mp4 30.1 MB
  5. Probability Density Function; Probability Mass Function.srt 7.6 KB
  5. Spark Introduction.mp4 89.9 MB
  5. Spark Introduction.srt 21.2 KB
  5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp4 96.5 MB
  5. [Activity] MAC Installing and Using Anaconda & Course Materials.srt 14.5 KB
  5. [Activity] Making Movie Recommendations to People.mp4 132.6 MB
  5. [Activity] Making Movie Recommendations to People.srt 22.6 KB
  6. AB Test Gotchas.mp4 96.1 MB
  6. AB Test Gotchas.srt 21.9 KB
  6. Common Data Distributions.mp4 75.4 MB
  6. Common Data Distributions.srt 16.1 KB
  6. Important note about Tensorflow 2.html 614 B
  6. Reinforcement Learning.mp4 132.3 MB
  6. Reinforcement Learning.srt 28.5 KB
  6. Spark and the Resilient Distributed Dataset (RDD).mp4 98.5 MB
  6. Spark and the Resilient Distributed Dataset (RDD).srt 24.4 KB
  6. [Activity] Clustering people based on income and age.mp4 57.3 MB
  6. [Activity] Clustering people based on income and age.srt 11.5 KB
  6. [Activity] Detecting outliers.mp4 36.3 MB
  6. [Activity] Detecting outliers.srt 11.4 KB
  6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4 80.2 MB
  6. [Activity] LINUX Installing and Using Anaconda & Course Materials.srt 14.7 KB
  6. [Exercise] Improve the recommender's results.mp4 84.2 MB
  6. [Exercise] Improve the recommender's results.srt 13.2 KB
  6.1 Python Markov Decision Process Toolbox.html 102 B
  6.2 Pac-Man Example.html 102 B
  6.3 Cat and Mouse Example.html 102 B
  7. Feature Engineering and the Curse of Dimensionality.mp4 41.7 MB
  7. Feature Engineering and the Curse of Dimensionality.srt 38.1 MB
  7. Introducing MLLib.mp4 54.8 MB
  7. Introducing MLLib.srt 54.8 MB
  7. Measuring Entropy.mp4 35 MB
  7. Measuring Entropy.srt 6.9 KB
  7. Python Basics, Part 1 [Optional].mp4 33 MB
  7. Python Basics, Part 1 [Optional].srt 7.8 KB
  7. [Activity] Percentiles and Moments.mp4 114 MB
  7. [Activity] Percentiles and Moments.srt 28.3 KB
  7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp4 78 MB
  7. [Activity] Reinforcement Learning & Q-Learning with Gym.srt 22.5 KB
  7. [Activity] Using Tensorflow, Part 1.mp4 118.2 MB
  7. [Activity] Using Tensorflow, Part 1.srt 23.5 KB
  8. Imputation Techniques for Missing Data.mp4 49 MB
  8. Imputation Techniques for Missing Data.srt 14.3 KB
  8. Introduction to Decision Trees in Spark.mp4 134 MB
  8. Introduction to Decision Trees in Spark.srt 28.1 KB
  8. Understanding a Confusion Matrix.mp4 14.8 MB
  8. Understanding a Confusion Matrix.srt 9.7 KB
  8. [Activity] A Crash Course in matplotlib.mp4 129.3 MB
  8. [Activity] A Crash Course in matplotlib.srt 28.6 KB
  8. [Activity] Python Basics, Part 2 [Optional].mp4 20.6 MB
  8. [Activity] Python Basics, Part 2 [Optional].srt 7.6 KB
  8. [Activity] Using Tensorflow, Part 2.mp4 104.5 MB
  8. [Activity] Using Tensorflow, Part 2.srt 21.6 KB
  8. [Activity] WINDOWS Installing Graphviz.mp4 2.1 MB
  8. [Activity] WINDOWS Installing Graphviz.srt 716 B
  9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4 36.3 MB
  9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.srt 9.9 KB
  9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4 25.8 MB
  9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).srt 10.8 KB
  9. [Activity] Advanced Visualization with Seaborn.mp4 147.8 MB
  9. [Activity] Advanced Visualization with Seaborn.srt 30 KB
  9. [Activity] Introducing Keras.mp4 92.1 MB
  9. [Activity] Introducing Keras.srt 23.7 KB
  9. [Activity] K-Means Clustering in Spark.mp4 117.9 MB
  9. [Activity] K-Means Clustering in Spark.srt 17.7 KB
  9. [Activity] MAC Installing Graphviz.mp4 14.8 MB
  9. [Activity] MAC Installing Graphviz.srt 1.3 KB
  9. [Activity] Python Basics, Part 3 [Optional].mp4 10.1 MB
  9. [Activity] Python Basics, Part 3 [Optional].srt 4.2 KB
  [FreeAllCourse.Com].URL 204 B
  ▲ 220 total files

Description


Machine Learning, Data Science and Deep Learning with Python



Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

What you'll learn:

Build artificial neural networks with Tensorflow and Keras
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Implement machine learning at massive scale with
Apache Spark's MLLib
Understand reinforcement learning - and how to build a
Pac-Man bot

Created by Sundog Education by Frank Kane, Frank Kane
Last updated 12/2019
English

For More Updated Course Visit: freeallcourse.com

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