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Machine Learning, Data Science and Deep Learning with Python

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Machine Learning, Data Science and Deep Learning with Python (Size: 7.95 GB)
  1. Getting Started
  1. Introduction.mp4 59.6 MB
  1. Introduction.srt 4.75 KB
  10. [Activity] Python Basics, Part 4 [Optional].mp4 21.12 MB
  10. [Activity] Python Basics, Part 4 [Optional].srt 6 KB
  11. Introducing the Pandas Library [Optional].mp4 123.1 MB
  11. Introducing the Pandas Library [Optional].srt 18.05 KB
  2. Udemy 101 Getting the Most From This Course.mp4 19.77 MB
  2. Udemy 101 Getting the Most From This Course.srt 4.04 KB
  3. Installation Getting Started.html 265 B
  4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4 102.76 MB
  4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.srt 18.88 KB
  5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp4 96.53 MB
  5. [Activity] MAC Installing and Using Anaconda & Course Materials.srt 14.48 KB
  6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4 80.21 MB
  6. [Activity] LINUX Installing and Using Anaconda & Course Materials.srt 14.66 KB
  7. Python Basics, Part 1 [Optional].mp4 32.98 MB
  7. Python Basics, Part 1 [Optional].srt 7.76 KB
  8. [Activity] Python Basics, Part 2 [Optional].mp4 20.63 MB
  8. [Activity] Python Basics, Part 2 [Optional].srt 7.63 KB
  9. [Activity] Python Basics, Part 3 [Optional].mp4 10.09 MB
  9. [Activity] Python Basics, Part 3 [Optional].srt 4.24 KB
  10. Deep Learning and Neural Networks
  1. Deep Learning Pre-Requisites.mp4 74.17 MB
  1. Deep Learning Pre-Requisites.srt 21.52 KB
  10. [Activity] Using Keras to Predict Political Affiliations.mp4 88.2 MB
  10. [Activity] Using Keras to Predict Political Affiliations.srt 21.14 KB
  11. Convolutional Neural Networks (CNN's).mp4 93.09 MB
  11. Convolutional Neural Networks (CNN's).srt 19.86 KB
  12. [Activity] Using CNN's for handwriting recognition.mp4 69.56 MB
  12. [Activity] Using CNN's for handwriting recognition.srt 13.76 KB
  13. Recurrent Neural Networks (RNN's).mp4 69.17 MB
  13. Recurrent Neural Networks (RNN's).srt 18.48 KB
  14. [Activity] Using a RNN for sentiment analysis.mp4 81.36 MB
  14. [Activity] Using a RNN for sentiment analysis.srt 16.82 KB
  15. [Activity] Transfer Learning.mp4 115.26 MB
  15. [Activity] Transfer Learning.srt 21.53 KB
  16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4 18.43 MB
  16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.srt 8.29 KB
  17. Deep Learning Regularization with Dropout and Early Stopping.mp4 33.64 MB
  17. Deep Learning Regularization with Dropout and Early Stopping.srt 11.97 KB
  18. The Ethics of Deep Learning.mp4 128.24 MB
  18. The Ethics of Deep Learning.srt 19.84 KB
  19. Learning More about Deep Learning.mp4 38.64 MB
  19. Learning More about Deep Learning.srt 3.14 KB
  2. The History of Artificial Neural Networks.mp4 79.98 MB
  2. The History of Artificial Neural Networks.srt 19.07 KB
  3. [Activity] Deep Learning in the Tensorflow Playground.mp4 141.58 MB
  3. [Activity] Deep Learning in the Tensorflow Playground.srt 141.62 MB
  4. Deep Learning Details.mp4 64.22 MB
  4. Deep Learning Details.srt 64.25 MB
  5. Introducing Tensorflow.mp4 86.27 MB
  5. Introducing Tensorflow.srt 22.51 KB
  6. Important note about Tensorflow 2.html 1000 B
  7. [Activity] Using Tensorflow, Part 1.mp4 72.69 MB
  7. [Activity] Using Tensorflow, Part 1.srt 13.84 KB
  8. [Activity] Using Tensorflow, Part 2.mp4 108.64 MB
  8. [Activity] Using Tensorflow, Part 2.srt 23.35 KB
  9. [Activity] Introducing Keras.mp4 92.05 MB
  9. [Activity] Introducing Keras.srt 23.75 KB
  11. Final Project
  1. Your final project assignment.mp4 51.63 MB
  1. Your final project assignment.srt 11.56 KB
  2. Final project review.mp4 98.5 MB
  2. Final project review.srt 24.51 KB
  12. You made it!
  1. More to Explore.mp4 64.06 MB
  1. More to Explore.srt 7.24 KB
  2. Don't Forget to Leave a Rating!.html 564 B
  3. Bonus Lecture More courses to explore!.html 7.32 KB
  2. Statistics and Probability Refresher, and Python Practice
  1. Types of Data.mp4 77.25 MB
  1. Types of Data.srt 16.24 KB
  10. [Activity] Covariance and Correlation.mp4 116.74 MB
  10. [Activity] Covariance and Correlation.srt 25.91 KB
  11. [Exercise] Conditional Probability.mp4 125.14 MB
  11. [Exercise] Conditional Probability.srt 28.41 KB
  12. Exercise Solution Conditional Probability of Purchase by Age.mp4 22 MB
  12. Exercise Solution Conditional Probability of Purchase by Age.srt 3.99 KB
  13. Bayes' Theorem.mp4 58.9 MB
  13. Bayes' Theorem.srt 11.49 KB
  2. Mean, Median, Mode.mp4 56.15 MB
  2. Mean, Median, Mode.srt 12.95 KB
  3. [Activity] Using mean, median, and mode in Python.mp4 61.93 MB
  3. [Activity] Using mean, median, and mode in Python.srt 15.01 KB
  4. [Activity] Variation and Standard Deviation.mp4 110.86 MB
  4. [Activity] Variation and Standard Deviation.srt 25.83 KB
  5. Probability Density Function; Probability Mass Function.mp4 30.07 MB
  5. Probability Density Function; Probability Mass Function.srt 7.59 KB
  6. Common Data Distributions.mp4 75.37 MB
  6. Common Data Distributions.srt 16.08 KB
  7. [Activity] Percentiles and Moments.mp4 114.04 MB
  7. [Activity] Percentiles and Moments.srt 28.33 KB
  8. [Activity] A Crash Course in matplotlib.mp4 129.35 MB
  8. [Activity] A Crash Course in matplotlib.srt 28.57 KB
  9. [Activity] Advanced Visualization with Seaborn.mp4 147.81 MB
  9. [Activity] Advanced Visualization with Seaborn.srt 29.96 KB
  3. Predictive Models
  1. [Activity] Linear Regression.mp4 100.46 MB
  1. [Activity] Linear Regression.srt 25.7 KB
  2. [Activity] Polynomial Regression.mp4 66.77 MB
  2. [Activity] Polynomial Regression.srt 17.59 KB
  3. [Activity] Multiple Regression, and Predicting Car Prices.mp4 73.85 MB
  3. [Activity] Multiple Regression, and Predicting Car Prices.srt 21.13 KB
  4. Multi-Level Models.mp4 47.47 MB
  4. Multi-Level Models.srt 10.66 KB
  4. Machine Learning with Python
  1. Supervised vs. Unsupervised Learning, and TrainTest.mp4 98.61 MB
  1. Supervised vs. Unsupervised Learning, and TrainTest.srt 20.9 KB
  10. [Activity] LINUX Installing Graphviz.mp4 7.05 MB
  10. [Activity] LINUX Installing Graphviz.srt 1.11 KB
  11. Decision Trees Concepts.mp4 86.53 MB
  11. Decision Trees Concepts.srt 21.1 KB
  12. [Activity] Decision Trees Predicting Hiring Decisions.mp4 95.95 MB
  12. [Activity] Decision Trees Predicting Hiring Decisions.srt 22.45 KB
  13. Ensemble Learning.mp4 65.21 MB
  13. Ensemble Learning.srt 14.55 KB
  14. Support Vector Machines (SVM) Overview.mp4 44.74 MB
  14. Support Vector Machines (SVM) Overview.srt 9.88 KB
  15. [Activity] Using SVM to cluster people using scikit-learn.mp4 43.94 MB
  15. [Activity] Using SVM to cluster people using scikit-learn.srt 14.85 KB
  2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4 58.14 MB
  2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.srt 13.11 KB
  3. Bayesian Methods Concepts.mp4 40.73 MB
  3. Bayesian Methods Concepts.srt 8.83 KB
  4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4 89.09 MB
  4. [Activity] Implementing a Spam Classifier with Naive Bayes.srt 17.42 KB
  5. K-Means Clustering.mp4 71.94 MB
  5. K-Means Clustering.srt 17.2 KB
  6. [Activity] Clustering people based on income and age.mp4 57.29 MB
  6. [Activity] Clustering people based on income and age.srt 11.55 KB
  7. Measuring Entropy.mp4 34.97 MB
  7. Measuring Entropy.srt 6.9 KB
  8. [Activity] WINDOWS Installing Graphviz.mp4 2.06 MB
  8. [Activity] WINDOWS Installing Graphviz.srt 689 B
  9. [Activity] MAC Installing Graphviz.mp4 14.83 MB
  9. [Activity] MAC Installing Graphviz.srt 1.26 KB
  5. Recommender Systems
  1. User-Based Collaborative Filtering.mp4 86.37 MB
  1. User-Based Collaborative Filtering.srt 19.38 KB
  2. Item-Based Collaborative Filtering.mp4 75 MB
  2. Item-Based Collaborative Filtering.srt 19.99 KB
  3. [Activity] Finding Movie Similarities.mp4 107.83 MB
  3. [Activity] Finding Movie Similarities.srt 20.08 KB
  4. [Activity] Improving the Results of Movie Similarities.mp4 94.86 MB
  4. [Activity] Improving the Results of Movie Similarities.srt 16.78 KB
  5. [Activity] Making Movie Recommendations to People.mp4 132.55 MB
  5. [Activity] Making Movie Recommendations to People.srt 22.61 KB
  6. [Exercise] Improve the recommender's results.mp4 84.23 MB
  6. [Exercise] Improve the recommender's results.srt 13.2 KB
  6. More Data Mining and Machine Learning Techniques
  1. K-Nearest-Neighbors Concepts.mp4 40.28 MB
  1. K-Nearest-Neighbors Concepts.srt 8.95 KB
  2. [Activity] Using KNN to predict a rating for a movie.mp4 142.06 MB
  2. [Activity] Using KNN to predict a rating for a movie.srt 28.48 KB
  3. Dimensionality Reduction; Principal Component Analysis.mp4 67.74 MB
  3. Dimensionality Reduction; Principal Component Analysis.srt 12.32 KB
  4. [Activity] PCA Example with the Iris data set.mp4 109.73 MB
  4. [Activity] PCA Example with the Iris data set.srt 21.2 KB
  5. Data Warehousing Overview ETL and ELT.mp4 103.33 MB
  5. Data Warehousing Overview ETL and ELT.srt 19.74 KB
  6. Reinforcement Learning.mp4 132.26 MB
  6. Reinforcement Learning.srt 28.5 KB
  6.1 Cat and Mouse Example.html 140 B
  6.2 Pac-Man Example.html 145 B
  6.3 Python Markov Decision Process Toolbox.html 119 B
  7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp4 77.96 MB
  7. [Activity] Reinforcement Learning & Q-Learning with Gym.srt 22.49 KB
  8. Understanding a Confusion Matrix.mp4 14.84 MB
  8. Understanding a Confusion Matrix.srt 9.71 KB
  9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4 25.79 MB
  9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).srt 10.82 KB
  7. Dealing with Real-World Data
  1. BiasVariance Tradeoff.mp4 66.31 MB
  1. BiasVariance Tradeoff.srt 14.4 KB
  10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4 47.91 MB
  10. Binning, Transforming, Encoding, Scaling, and Shuffling.srt 14.21 KB
  2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4 102.34 MB
  2. [Activity] K-Fold Cross-Validation to avoid overfitting.srt 24.54 KB
  3. Data Cleaning and Normalization.mp4 78.75 MB
  3. Data Cleaning and Normalization.srt 17.08 KB
  4. [Activity] Cleaning web log data.mp4 129.38 MB
  4. [Activity] Cleaning web log data.srt 23.78 KB
  5. Normalizing numerical data.mp4 38.2 MB
  5. Normalizing numerical data.srt 7.65 KB
  6. [Activity] Detecting outliers.mp4 36.32 MB
  6. [Activity] Detecting outliers.srt 11.44 KB
  7. Feature Engineering and the Curse of Dimensionality.mp4 41.71 MB
  7. Feature Engineering and the Curse of Dimensionality.srt 11.83 KB
  8. Imputation Techniques for Missing Data.mp4 49.02 MB
  8. Imputation Techniques for Missing Data.srt 14.31 KB
  9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4 36.34 MB
  9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.srt 9.88 KB
  8. Apache Spark Machine Learning on Big Data
  1. Warning about Java 11 and Spark 2.4!.html 650 B
  10. TF IDF.mp4 68.85 MB
  10. TF IDF.srt 14.03 KB
  11. [Activity] Searching Wikipedia with Spark.mp4 102.99 MB
  11. [Activity] Searching Wikipedia with Spark.srt 12.85 KB
  12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4 105.68 MB
  12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.srt 13.91 KB
  2. Spark installation notes for MacOS and Linux users.html 3.48 KB
  3. [Activity] Installing Spark - Part 1.mp4 83.63 MB
  3. [Activity] Installing Spark - Part 1.srt 12.04 KB
  3.1 winutils.exe.html 108 B
  4. [Activity] Installing Spark - Part 2.mp4 111.98 MB
  4. [Activity] Installing Spark - Part 2.srt 10.59 KB
  4.1 winutils.exe.html 108 B
  5. Spark Introduction.mp4 89.86 MB
  5. Spark Introduction.srt 21.21 KB
  6. Spark and the Resilient Distributed Dataset (RDD).mp4 98.51 MB
  6. Spark and the Resilient Distributed Dataset (RDD).srt 24.41 KB
  7. Introducing MLLib.mp4 54.74 MB
  7. Introducing MLLib.srt 11.46 KB
  8. Introduction to Decision Trees in Spark.mp4 134.02 MB
  8. Introduction to Decision Trees in Spark.srt 28.1 KB
  9. [Activity] K-Means Clustering in Spark.mp4 117.86 MB
  9. [Activity] K-Means Clustering in Spark.srt 17.73 KB
  9. Experimental Design ML in the Real World
  1. Deploying Models to Real-Time Systems.mp4 33.04 MB
  1. Deploying Models to Real-Time Systems.srt 15.42 KB
  2. AB Testing Concepts.mp4 97.49 MB
  2. AB Testing Concepts.srt 97.49 MB
  3. T-Tests and P-Values.mp4 64.92 MB
  3. T-Tests and P-Values.srt 13.16 KB
  4. [Activity] Hands-on With T-Tests.mp4 81.62 MB
  4. [Activity] Hands-on With T-Tests.srt 81.63 MB
  5. Determining How Long to Run an Experiment.mp4 34.84 MB
  5. Determining How Long to Run an Experiment.srt 8.34 KB
  6. AB Test Gotchas.mp4 96.1 MB
  6. AB Test Gotchas.srt 21.88 KB
  GetFreeCourses.Co.url 116 B
  How you can help GetFreeCourses.Co.txt 182 B

Description


Download Paid Udemy Courses For Free: GetFreeCourses.Co

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

Description

New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks - as well as Tensorflow 2.0!

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

Read More At: https://www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/

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