What is AI and Machine Learning

1. Is machine learning difficult?

2. What is artificial intelligence

3. Difference between AI and machine learning

4. Machine learning examples

Types of Machine Learning

1. Three different types of machine learning: supervised, unsupervised, and reinforcement learning

2. Difference between labeled and unlabeled data

3. The difference between regression and classification, and how they are used

Linear Regression

1. Fitting a line through a set of data points

2. Coding the linear regression algorithm in Python

3. Using Turi Create to build a linear regression model to predict housing prices in a real dataset

4. What is polynomial regression

5. Fitting a more complex curve to nonlinear data

6. Examples of linear regression

Optimizing the Training Process

1. What is underfitting and overfitting

2. Solutions for avoiding overfitting

3. Testing the model complexity graph, and regularization

4. Calculating the complexity of the model

5. Picking the best model in terms of performance and complexity

The Perceptron Algorithm

1. What is classification

2. Sentiment analysis

3. How to draw a line that separates points of two colors

4. What is a perceptron

5. Coding the perceptron algorithm in Python and Turi Create

Logistic Classifiers

1. Hard assignments and Soft assignments

2. The sigmoid function

3. Discrete perceptrons vs. Continuous perceptrons

4. Logistic regression algorithm for classifying data

5. Coding the logistic regression algorithm in Python

Measuring Classification Models

1. Types of errors a model can make

2. The confusion matrix

3. What are accuracy, recall, precision, F-score, sensitivity, and specificity

4. What is the ROC curve

The Naive Bayes Model

1. What is Bayes theorem

2. Dependent and independent events

3. The prior and posterior probabilities

4. Calculating conditional probabilities

5. Using the naive Bayes model

6. Coding the naive Bayes algorithm in Python

Decision Trees

1. What is a decision tree

2. Using decision trees for classification and regression

3. Building an app-recommendation system using users’ information

4. Accuracy, Gini index, and entropy

5. Using Scikit-Learn to train a decision tree

Neural Networks

1. What is a neural network

2. Architecture of a neural network: nodes, layers, depth, and activation functions

3. Training neural networks

4. Potential problems in training neural networks

5. Techniques to improve neural network training

6. Using neural networks as regression models

Bonus: Support Vector Machine and the Kernel methods

1. What a support vector machine

2. Which of the linear classifiers for a dataset has the best boundary

3. Using the kernel method to build nonlinear classifiers

4. Coding support vector machines and the kernel method in Scikit-Learn

Bonus: Ensemble Learning

1. What ensemble learning is

2. Using bagging to combine classifiers

3. Using boosting to combine classifiers

4. Ensemble methods: random forests, AdaBoost, gradient boosting, and XGBoost

Bonus: Real-World Example: Data Engineering and ML

1. Cleaning up and preprocessing data to make it readable by our model

2. Using Scikit-Learn to train and evaluate several models

3. Using grid search to select good hyperparameters for our model

4. Using k-fold cross-validation to be able to use our data for training and validation simultaneously