FreeCourseSite com Udemy - Machine Learning Data Science and Deep Learning with Python

1. 1. Getting Started/1. Introduction.mp459.6MB
2. 1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].mp421.12MB
3. 1. Getting Started/11. Introducing the Pandas Library [Optional].mp4123.1MB
4. 1. Getting Started/2. Udemy 101 Getting the Most From This Course.mp419.77MB
5. 1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4101.15MB
6. 1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp493.97MB
7. 1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp482.69MB
8. 1. Getting Started/7. Python Basics, Part 1 [Optional].mp432.97MB
9. 1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].mp420.64MB
10. 1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].mp410.09MB
11. 10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp474.18MB
12. 10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.mp488.19MB
13. 10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).mp493.09MB
14. 10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.mp469.56MB
15. 10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).mp469.17MB
16. 10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.mp481.36MB
17. 10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.mp4115.26MB
18. 10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp418.43MB
19. 10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.mp433.63MB
20. 10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.mp4128.24MB
21. 10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.mp438.64MB
22. 10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.mp479.98MB
23. 10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.mp4141.58MB
24. 10. Deep Learning and Neural Networks/4. Deep Learning Details.mp464.21MB
25. 10. Deep Learning and Neural Networks/5. Introducing Tensorflow.mp464.15MB
26. 10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.mp4118.22MB
27. 10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.mp4104.55MB
28. 10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.mp492.05MB
29. 11. Final Project/1. Your final project assignment Mammogram Classification.mp451.64MB
30. 11. Final Project/2. Final project review.mp498.51MB
31. 12. You made it!/1. More to Explore.mp464.06MB
32. 2. Statistics and Probability Refresher, and Python Practice/1. Types of Data (Numerical, Categorical, Ordinal).mp477.24MB
33. 2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.mp4116.74MB
34. 2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.mp4125.14MB
35. 2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.mp422MB
36. 2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.mp458.91MB
37. 2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.mp456.15MB
38. 2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.mp461.94MB
39. 2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.mp4110.86MB
40. 2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.mp430.07MB
41. 2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions (Normal, Binomial, Poisson, etc).mp475.36MB
42. 2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.mp4114.04MB
43. 2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.mp4129.35MB
44. 2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.mp4147.81MB
45. 3. Predictive Models/1. [Activity] Linear Regression.mp4100.46MB
46. 3. Predictive Models/2. [Activity] Polynomial Regression.mp466.77MB
47. 3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.mp473.85MB
48. 3. Predictive Models/4. Multi-Level Models.mp447.46MB
49. 4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp498.61MB
50. 4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.mp47.05MB
51. 4. Machine Learning with Python/11. Decision Trees Concepts.mp486.53MB
52. 4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.mp495.95MB
53. 4. Machine Learning with Python/13. Ensemble Learning.mp465.21MB
54. 4. Machine Learning with Python/14. [Activity] XGBoost.mp4102.08MB
55. 4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.mp444.75MB
56. 4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.mp446.7MB
57. 4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp458.14MB
58. 4. Machine Learning with Python/3. Bayesian Methods Concepts.mp440.72MB
59. 4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp489.08MB
60. 4. Machine Learning with Python/5. K-Means Clustering.mp471.95MB
61. 4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp457.29MB
62. 4. Machine Learning with Python/7. Measuring Entropy.mp434.97MB
63. 4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.mp42.06MB
64. 4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.mp414.83MB
65. 5. Recommender Systems/1. User-Based Collaborative Filtering.mp486.37MB
66. 5. Recommender Systems/2. Item-Based Collaborative Filtering.mp475MB
67. 5. Recommender Systems/3. [Activity] Finding Movie Similarities using Cosine Similarity.mp4107.83MB
68. 5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp494.86MB
69. 5. Recommender Systems/5. [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering.mp4132.55MB
70. 5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp484.24MB
71. 6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.mp440.28MB
72. 6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.mp4142.06MB
73. 6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis (PCA).mp467.74MB
74. 6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.mp4109.74MB
75. 6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.mp4103.33MB
76. 6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4132.26MB
77. 6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp477.96MB
78. 6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.mp414.83MB
79. 6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp425.11MB
80. 7. Dealing with Real-World Data/1. BiasVariance Tradeoff.mp466.31MB
81. 7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp447.92MB
82. 7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp476.1MB
83. 7. Dealing with Real-World Data/3. Data Cleaning and Normalization.mp478.76MB
84. 7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.mp4129.38MB
85. 7. Dealing with Real-World Data/5. Normalizing numerical data.mp438.2MB
86. 7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp436.32MB
87. 7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.mp441.71MB
88. 7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.mp449.02MB
89. 7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp436.34MB
90. 8. Apache Spark Machine Learning on Big Data/10. TF IDF.mp468.86MB
91. 8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.mp4102.99MB
92. 8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4105.68MB
93. 8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.mp483.63MB
94. 8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.mp4111.98MB
95. 8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.mp489.86MB
96. 8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp498.52MB
97. 8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp454.74MB
98. 8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.mp4134.01MB
99. 8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4117.86MB
100. 9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.mp433.04MB
101. 9. Experimental Design ML in the Real World/2. AB Testing Concepts.mp497.48MB
102. 9. Experimental Design ML in the Real World/3. T-Tests and P-Values.mp464.91MB
103. 9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.mp481.62MB
104. 9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.mp434.84MB
105. 9. Experimental Design ML in the Real World/6. AB Test Gotchas.mp496.1MB