Getting Started- Installing a Python Data Science Environment
- Using and understanding IPython (Jupyter) Notebooks
- Python basics - Part 1
- Understanding Python code
- Importing modules
- Python basics - Part 2
- Running Python scripts
Statistics and Probability Refresher, and Python Practice- Types of data
- Mean, median, and mode
- Using mean, and mode in Python
- Standard deviation and variance
- Probability density function and probability mass function
- Types of data distributions
- Percentiles and moments
Matplotlib and Advanced Probability Concepts- A crash course in Matplotlib
- Covariance and correlation
- Conditional probability
- Bayes' theore
Algorithm Overview- Data Prep
- Linear Algorithms
- Non-Linear Algorithms
- Ensembles
Predictive Models- Linear regression
- Polynomial regression
- Multivariate regression and predicting car prices
- Multi-level models
Applied Machine Learning with Python- Machine learning and train/test
- Using train/test to prevent overfitting of a polynomial regression
- Bayesian methods - Concepts
- Implementing a spam classifier with Na€¹ve Bayes
- K-Means clustering
Recommender Systems- What are recommender systems
- Item-based collaborative filtering
- How item-based collaborative filtering works
- Finding movie similarities
- Improving the results of movie similarities
- Making movie recommendations to people
- Improving the recommendation results
More Applied Machine Learning Techniques- K-nearest neighbors - concepts
- Using KNN to predict a rating for a movie
- Dimensionality reduction and principal component analysis
- A PCA example with the Iris dataset
- Data warehousing overview
- Reinforcement learning
Dealing with Data in the Real World- Bias/variance trade-off
- K-fold cross-validation to avoid overfitting
- Data cleaning and normalization
- Cleaning web log data
- Normalizing numerical data
- Detecting outliers
Apache Spark - Machine Learning on Big Data- Installing Spark
- Spark introduction
- Spark and Resilient Distributed Datasets (RDD)
- Introducing MLlib
- Decision Trees in Spark with MLlib
- K-Means Clustering in Spark
- TF-IDF
- Searching Wikipedia with Spark MLlib
- Using the Spark 2.0 DataFrame API for MLlib
Testing and Experimental Design- A/B testing concepts
- T-test and p-value
- Measuring t-statistics and p-values using Python
- Determining how long to run an experiment for
- A/B test gotchas
GUIs and REST- Build a UI for your Models
- Build a REST API for your Models
What the Future Holds, 1. What is AI and Machine Learning
¢ Is machine learning difficult
¢ What is artificial intelligence
¢ Difference between AI and machine learning
¢ Machine learning examples
2. Types of Machine Learning
¢ Three different types of machine learning: supervised, unsupervised, and reinforcement learning
¢ Difference between labeled and unlabeled data
¢ The difference between regression and classification, and how they are used
3. Linear Regression
¢ Fitting a line through a set of data points
¢ Coding the linear regression algorithm in Python
¢ Using Turi Create to build a linear regression model to predict housing prices in a real dataset
¢ What is polynomial regression
¢ Fitting a more complex curve to nonlinear data
¢ Examples of linear regression
4. Optimizing the Training Process
¢ What is underfitting and overfitting
¢ Solutions for avoiding overfitting
¢ Testing the model complexity graph, and regularization
¢ Calculating the complexity of the model
¢ Picking the best model in terms of performance and complexity
5. The perceptron Algorithm
¢ What is classification
¢ Sentiment analysis
¢ How to draw a line that separates points of two colors
¢ What is a perceptron
¢ Coding the perceptron algorithm in Python and Turi Create
6. Logistic Classifiers
¢ Hard assignments and Soft assignments
¢ The sigmoid function
¢ Discrete perceptrons vs. Continuous perceptrons
¢ Logistic regression algorithm for classifying data
¢ Coding the logistic regression algorithm in Python
7. Measuring Classification Models
¢ Types of errors a model can make
¢ The confusion matrix
¢ what are accuracy, recall, precision, F-score, sensitivity, and specificity
¢ what is the ROC curve
8. The Naive Bayes Model
¢ What is Bayes theorem
¢ Dependent and independent events
¢ The prior and posterior probabilities
¢ Calculating conditional probabilities
¢ using the naive Bayes model
¢ Coding the naive Bayes algorithm in Python
9. Decision Trees
¢ What is a decision tree
¢ Using decision trees for classification and regression
¢ Building an app-recommendation system using users' information
¢ Accuracy, Gini index, and entropy
¢ Using Scikit-Learn to train a decision tree
10. Neural Networks
¢ What is a neural network
¢ Architecture of a neural network: nodes, layers, depth, and activation functions
¢ Training neural networks
¢ Potential problems in training neural networks
¢ Techniques to improve neural network training
¢ Using neural networks as regression models
11. Responsible AI: Navigating the Grey Areas
¢ Understanding Ethical Implications in AI
¢ Grasp the moral complexities in recommendation systems.
¢ Bias and Fairness in Recommenders
¢ Dissect potential biases in AI-driven recommendations.
12. Introduction to Generative AI
¢ Understanding Generative AI
¢ How Generative AI fits into the broader AI and Machine Learning landscape
¢ Differences between generative and discriminative models
¢ Introduction to Generative Adversarial Networks (GANs)
¢ Understanding the concept of latent space in generative models
¢ Basic structure and components of GANs: generator and discriminator
13. OPTIONAL: Applications of Generative AI in Business
¢ Improving customer experience: Using generative AI for personalized content creation, such as emails, ads, and product descriptions
¢ Product development: Using GANs for generating new ideas for products, fashion designs, and more
¢ Data augmentation: How generative models can create additional training data for other machine learning models, improving their performance
¢ Content creation: Using AI for generating realistic images, music, text, and more
¢ Risk management: Using generative AI to simulate different business scenarios and outcomes
¢ Healthcare: Generating synthetic medical data for research while preserving patient privacy
Bonus Content / Time Permitting
14. Bonus: Support vector machine and the Kernel methods
¢ What a support vector machine
¢ Which of the linear classifiers for a dataset has the best boundary
¢ Using the kernel method to build nonlinear classifiers
¢ Coding support vector machines and the kernel method in Scikit-Learn
15. Bonus: Ensemble learning
¢ What ensemble learning is
¢ Using bagging to combine classifiers
¢ Using boosting to combine classifiers
¢ Ensemble methods: random forests, AdaBoost, gradient boosting, and XGBoost
16. Bonus: Real-World Example: Data Engineering and ML
¢ Cleaning up and preprocessing data to make it readable by our model
¢ Using Scikit-Learn to train and evaluate several models
¢ Using grid search to select good hyperparameters for our model
¢ Using k-fold cross-validation to be able to use our data for training and validation simultaneously
Download Overview