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ChapterChapterChapter
1. Exploratory Data Analysis and Statistics 2. Frequency Distributions 3. Numerical Measures
4. Measures of Spread 5. Standard Deviation 6. Examining Relationships
7. Linear Relationships 8. Regression 9. Causation
10. Producing Data 11. Designing Studies 12. Sample Surveys
13. Blind and Double-Blind Experiments 14. Introduction to Probability 15. Relative frequency
16. Probability Rules 17. General Addition Rule 18. Conditional Probability
19. Independence Check 20. Probability Trees 21. Random Variables
22. Mean and Variance 23. Binomial Random Variables 24. Normal Random Variables
25. Normal Approximation to the Binomial 26. Inference 27. Point Estimation
28. Confidence Intervals 29. Hypothesis Testing 30. Population Proportion p: z-test
31. Population Proportion p: Effect of Sample Size 32. Population Mean: Confidence Intervals 33. Inference for Relationships
34. Two Independent Samples 35. Matched Pairs 36. ANOVA
37. Chi-Square Test




ExercisesExercisesExercises
1. Exploring Variables in a Dataset 2. Building A Two-Way Table 3. Exploring Simple Random Samples
4. Creating Similar Treatment Groups 5. Calculating Probabilities for Binomial Random Variables 6. Using the Normal Distribution
7. Calculating Confidence Intervals 8. Carrying Out the z-test 9. Examining the Conditions for the z-test
10. Examining the Conditions for the z-test 11. Checking Conditions for the Two-Sample t-test 12. Testing for a Linear Relationship




ChapterChapterChapter
1. Introduction to Machine Learning 2. Linear Algebra 3. Linear Regression with One Variable
4. Gradient Descent 5. Linear regression with multiple variable 6. Gradient Descent For Multiple Variables
7. Logistic regression 8. Cost function 9. Problem of Overfitting
10. Neural Networks 11. Cost Function 12. Advice for Applying Machine Learning
13. Diagnosing Bias vs. Variance 14. Prioritizing What to Work On 15. Support vector machines
16. SVM Large margin intuition 17. Large margin classification mathematics 18. Adapting SVM to non-linear classifiers
19. Unsupervised learning 20. K Means Algorithm 21. Dimension reduction
22. Anomaly Detection 23. Recommender systems 24. Learning With Large Datasets




ExercisesExercisesExercises
1. Octave Tutorial : Basic Operations 2. Linear Regression 3. Linear Regression with multiple variables
4. Logistic Regression 5. Regularized Logistic Regression 6. Multi-class Classification and Neural Networks
7. Neural Networks Learning 8. Regularized Linear Regression 9. Support Vector Machines
10. K-means Clustering 11. Principal Component Analysis 12. Anomaly Detection Systems
13. Recommender Systems new jobs  Machine Learning - Lab Exercises Solutions




Chapter Chapter Chapter
1. Deep Learning 2. Supervised Learning with Neural Networks 3. Why Deep Learning is taking off ?
4. Neural Network Basics 5. Logistic regression 6. Deep Learning course notations
7. Logistic Regression: Cost Function 8. Logistic Regression: Gradient Descent 9. Basics of Neural Network Programming - Derivatives
10. Computation graph 11. Logistic Regression Gradient Descent 12. Introduction to Vectorization
13. Vectorizing Logistic Regression 14. Broadcasting in Python 15. Neural Networks Overview
16. Computing a Neural Network's Output - Second Layer 17. Activation functions 18. Gradient descent for Neural Networks
19. Deep L-layer neural network 20. Getting your matrix dimensions right 21. Building blocks of deep neural networks
22. Forward and Backward Propagation




Quiz
Quiz 1: Introduction to deep learning Quiz 2: Neural Network Basics Quiz 3: Shallow Neural Networks
Quiz 4: Key concepts on Deep Neural Networks




Programming Exercise
new jobs  Introductory Video 1. Python Basics With Numpy 2. Logistic Regression with a Neural Network mindset
3. Planar data classification with one hidden layer 4 Building your Deep Neural Network Step by Step 5. Deep Neural Network Application




Chapter Chapter Chapter
1. Setting up your Machine Learning Application 2. Bias / Variance 3. Basic Recipe for Machine Learning
4. Regularizing your neural network 5. Why regularization reduces overfitting? 6. Dropout Regularization
7. Understanding Dropout 8. Other regularization methods 9. Normalizing inputs
10. Vanishing / Exploding gradients 11. Weight Initialization for Deep Networks 12. Numerical approximation of gradients
13. Need for Optimization Techniques 14. Understanding mini-batch gradient descent 15. Exponentially weighted averages
16. Understanding exponentially weighted averages 17. Bias correction in exponentially weighted averages 18. Gradient descent with momentum
19. RMSProp - Root Mean Square Prop 20. Adam optimization algorithm 21. Learning rate decay
22. Tuning process 23. Using an appropriate scale to pick hyperparameters 24. Hyperparameters tuning in practice: Pandas vs. Caviar
25. Normalizing activations in a network 26. Softmax Regression




Quiz
Quiz 1: Practical aspects of deep learning Quiz 2: Optimization algorithms Quiz 3: Hyperparameter tuning, Batch Normalization, Programming Frameworks




Programming Exercise
new jobs  Introductory Video 1. Improving Deep Neural Networks 2. Gradient Checking
3. Regularization 4. Tensorflow 5. Optimization




Chapter Chapter Chapter
1. Introduction to ML Strategy - Why ML Strategy 2. Single number evaluation metric 3. Satisficing and Optimizing metric
4. Train/dev/test distributions 5. Size of the dev and test sets 6. When to change dev/test sets and metrics
7. Comparing to human-level performance 8. Avoidable bias 9. Understanding human-level performance
10. Surpassing human-level performance 11. Carrying out error analysis 12. Cleaning up incorrectly labeled data
13. Build your first system quickly, then iterate 14. Training and testing on different distributions 15. Transfer learning
16. Multi-task learning 17. End-to-end deep learning




Quiz
Quiz 1: Bird Recognition system (Case study) Quiz 2: Autonomous driving (Case study)