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- 11 - Effective Learning Strategies for Machine Learning FAQ by Student Request/50 - Machine Learning and AI Prerequisite Roadmap pt 2.mp4 142.4 MB
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- 11. Appendix FAQ Finale/1. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
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- 2. Numpy (New)/7. Generating Data.mp4 66.2 MB
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- 7. Machine Learning Basics/9. Comparing Different Machine Learning Models.mp4 47.7 MB
- 8. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
- 7. Machine Learning Basics/4. What is Regression.mp4 45.3 MB
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- 7. Machine Learning Basics/1. Machine Learning Section Introduction.mp4 40.8 MB
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- 3. Matplotlib (New)/5. Plotting Images.mp4 40.5 MB
- 11. Appendix FAQ Finale/1. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
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- 8. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 195.3 MB
- 7. Machine Learning Basics/3. Classification in Code.mp4 131.5 MB
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- 3. Matplotlib (New)/5. Plotting Images.mp4 40.5 MB
- 11. Appendix FAQ Finale/1. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
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- 11 Appendix _ FAQ Finale/050 BONUS_ Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
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- [TutsNode.com] - Deep Learning Prerequisites/02 Numpy (New)/009 Generating Data.mp4 66.2 MB
- [TutsNode.com] - Deep Learning Prerequisites/07 Machine Learning Basics/034 What is Classification_.mp4 65.2 MB
- [TutsNode.com] - Deep Learning Prerequisites/07 Machine Learning Basics/037 Regression in Code.mp4 64.9 MB
- [TutsNode.com] - Deep Learning Prerequisites/01 Welcome and Logistics/001 Introduction and Outline.mp4 63.4 MB
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- [TutsNode.com] - Deep Learning Prerequisites/02 Numpy (New)/004 Arrays vs Lists.mp4 53.2 MB
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- [TutsNode.com] - Deep Learning Prerequisites/07 Machine Learning Basics/041 Comparing Different Machine Learning Models.mp4 47.7 MB
- [TutsNode.com] - Deep Learning Prerequisites/08 Setting Up Your Environment (FAQ by Student Request)/045 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
- [TutsNode.com] - Deep Learning Prerequisites/07 Machine Learning Basics/036 What is Regression_.mp4 45.3 MB
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- 11 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/002 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 85.1 MB
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- 10 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/002 Proof that using Jupyter Notebook is the same as not using it.mp4 67.5 MB
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- 7. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
- 2. Basics What is linear classification What's the relation to neural networks/3. How do we calculate the output of a neuron logistic classifier - Theory.srt 84.1 MB
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- 7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
- 4. Practical concerns/11. Practical Section Summary.srt 82.1 MB
- 1. Start Here/1. Introduction and Outline.mp4 49.2 MB
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- 7. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
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- 7. Appendix FAQ/13. What order should I take your courses in (part 2).mp4 39.4 MB
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- 3. Solving for the optimal weights/7. Maximizing the likelihood.mp4 26.4 MB
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- 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
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- 7. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
- 7. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
- 7. Appendix FAQ/14. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
- 7. Appendix FAQ/13. What order should I take your courses in (part 2).mp4 39.4 MB
- 7. Appendix FAQ/12. What order should I take your courses in (part 1).mp4 30.7 MB
- 2/5. Interpretation of Logistic Regression Output.mp4 29.2 MB
- 3/7. Maximizing the likelihood.mp4 26.4 MB
- 4. Practical concerns/8. The donut problem.mp4 25.9 MB
- 7. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
- 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
- 4. Practical concerns/10. Why Divide by Square Root of D.mp4 24.6 MB
- 7. Appendix FAQ/2. Gradient Descent Tutorial.mp4 23.9 MB
- 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 22.5 MB
- 3/10. E-Commerce Course Project Training the Logistic Model.mp4 17.9 MB
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- 6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
- 6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
- 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
- 6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 62.3 MB
- 1. Welcome/1. Welcome.mp4 52.1 MB
- 6. Appendix FAQ/11. What order should I take your courses in (part 2).srt 49.7 MB
- 6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
- 6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
- 4. Practical machine learning issues/3. Generalization error, train and test sets.srt 40.9 MB
- 6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
- 6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 39.4 MB
- 6. Appendix FAQ/10. What order should I take your courses in (part 1).srt 30.8 MB
- 6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 30.7 MB
- 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
- 6. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
- 4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
- 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
- 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 20.7 MB
- 6. Appendix FAQ/6. How to Code by Yourself (part 2).srt 20.5 MB
- 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
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- 6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
- 6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
- 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
- 6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 62.3 MB
- 1. Welcome/1. Welcome.mp4 52.1 MB
- 6. Appendix FAQ/11. What order should I take your courses in (part 2).srt 49.7 MB
- 6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
- 6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
- 4. Practical machine learning issues/3. Generalization error, train and test sets.srt 40.9 MB
- 6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
- 6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 39.4 MB
- 6. Appendix FAQ/10. What order should I take your courses in (part 1).srt 30.8 MB
- 6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 30.7 MB
- 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
- 6. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
- 4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
- 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
- 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 20.7 MB
- 6. Appendix FAQ/6. How to Code by Yourself (part 2).srt 20.5 MB
- 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
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- 6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
- 6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
- 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
- 6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 62.3 MB
- 1. Welcome/1. Welcome.mp4 52.1 MB
- 6. Appendix FAQ/11. What order should I take your courses in (part 2).srt 49.7 MB
- 6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
- 4. Practical machine learning issues/3. Generalization error, train and test sets.srt 40.9 MB
- 6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
- 6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
- 6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 39.4 MB
- 6. Appendix FAQ/10. What order should I take your courses in (part 1).srt 30.8 MB
- 6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 30.7 MB
- 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
- 6. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
- 4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
- 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
- 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 20.7 MB
- 6. Appendix FAQ/6. How to Code by Yourself (part 2).srt 20.5 MB
- 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
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- 8/1. Anaconda Environment Setup.mp4 195.3 MB
- 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
- 1. Start Here/3. Statistics vs. Machine Learning.mp4 58.4 MB
- 8/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
- 1. Start Here/2. How to Succeed in this Course.mp4 45.9 MB
- 1. Start Here/1. Introduction and Outline.mp4 41.3 MB
- 10/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
- 11. Appendix FAQ Finale/2. BONUS.srt 39.7 MB
- 11. Appendix FAQ Finale/2. BONUS.mp4 39.7 MB
- 10/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
- 10/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
- 2/5. Interpretation of Logistic Regression Output.mp4 29.3 MB
- 3. Solving for the optimal weights/7. Maximizing the likelihood.mp4 26.4 MB
- 4. Practical concerns/8. The donut problem.mp4 25.9 MB
- 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).mp4 25.7 MB
- 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
- 4. Practical concerns/10. Why Divide by Square Root of D.mp4 24.6 MB
- 7. Background Review/1. Gradient Descent Tutorial.mp4 23.9 MB
- 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 22.5 MB
- 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp4 17.9 MB
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- 8. Setting Up Your Environment/1. Windows-Focused Environment Setup 2018.mp4 195.3 MB
- 9. Extra Help With Python Coding for Beginners/4. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
- 3/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
- 1. Start Here/3. Statistics vs. Machine Learning.mp4 58.4 MB
- 8. Setting Up Your Environment/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
- 10. Effective Learning Strategies for Machine Learning/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
- 11. Appendix FAQ/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
- 10. Effective Learning Strategies for Machine Learning/4. What order should I take your courses in (part 2).mp4 39.5 MB
- 10. Effective Learning Strategies for Machine Learning/3. What order should I take your courses in (part 1).mp4 30.8 MB
- 1. Start Here/2. How to Succeed in this Course.mp4 29.3 MB
- 4. Practical concerns/8. The donut problem.mp4 25.9 MB
- 2/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
- 9. Extra Help With Python Coding for Beginners/2. How to Code by Yourself (part 1).mp4 25.7 MB
- 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
- 4. Practical concerns/10. Why Divide by Square Root of D.mp4 24.6 MB
- 7. Background Review/1. Gradient Descent Tutorial.mp4 23.9 MB
- 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 22.5 MB
- 2/9. Moore's Law Derivation.mp4 21.2 MB
- 2/5. Determine how good the model is - r-squared.mp4 20.7 MB
- 2/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
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- 6. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 195.3 MB
- 1. Welcome/4. Anyone Can Succeed in this Course.mp4 88.1 MB
- 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
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- 1. Welcome/1. Welcome.mp4 52.1 MB
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- 8/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
- 9. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
- 8/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
- 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 37.8 MB
- 8/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
- 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
- 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25.7 MB
- 4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
- 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
- 2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.mp4 21.2 MB
- 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
- 8/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
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- 2. Numpy (New)/7. Generating Data.mp4 66.3 MB
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- 4. Pandas (New)/3. Selecting Rows and Columns.mp4 51.5 MB
- 6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
- 3. Matplotlib (New)/5. Plotting Images.mp4 40.5 MB
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- 4. Pandas (New)/2. Loading in Data.mp4 24.8 MB
- 5. Scipy (New)/3. Convolution.mp4 22.5 MB
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