Lesson 1: Introduction to Artificial Intelligence and Machine Learning
Learning objectives: In this lesson, you will recognize the importance of data economy and
understand the emergence and applications of Artificial Intelligence and Machine Learning.
- Artificial Intelligence
- Machine Learning
- Machine Learning algorithms
- Applications of Machine Learning
Lesson 2: Techniques of Machine Learning
Learning objectives: In this lesson, you will take a step further, to understand the techniques of
Machine Learning such as supervised, unsupervised, semi-supervised and reinforcement learning.
- Supervised learning
- Unsupervised learning
- Semi-supervised and Reinforcement learning
- Bias and variance trade-off
- Representation learning
Lesson 3: Data Preprocessing
Learning objectives: In this lesson, you will learn how to prepare the data for machine learning
algorithms with feature engineering, feature scaling, data sets and dimensionality reduction.
- Data preparation
- Feature engineering
- Feature scaling
- Dimensionality reduction
Lesson 4: Math Refresher
Learning objectives: This lesson will take you into your past and help you brush up on those math
and statistics concepts highly necessary to understand the Machine Learning algorithms.
- Concepts of linear algebra
- Eigenvalues, eigenvectors, and eigendecomposition
- Introduction to Calculus
- Probability and statistics
Lesson 5: Regression
Learning objectives: In this lesson, you will unleash the real power of Machine Learning with
polynomial regression, linear regression, random forest, decision tree regression, gradient
descent, and regularization.
- Regression and its types
- Linear regression: Equations and algorithms
Lesson 6: Classification
Learning objectives: In this lesson, you will learn about classification, logistic regression, K-nearest
neighbors, support vector machines, and Naive Bayes.
- Meaning and types of classification
- Logistic regression
- K-nearest neighbors
- Support vector machines
- Kernel support vector machines
- Naive Bayes
- Decision tree classifier
- Random forest classifier
Lesson 7: Unsupervised learning: Clustering
Learning objectives: In this lesson, you will learn and implement a few more algorithms within the
unsupervised learning category.
- Clustering algorithms
- K-means clustering
Lesson 8: Introduction to Deep Learning
Learning objectives: This last lesson of the course, gives you a peek into the world of deep
learning and how it is related to machine learning.
- Meaning and importance of Deep Learning
- Artificial Neural Networks