AI gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies implement fields of AI like machine learning to advance their business. By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Who should attend? 

• Software Engineers
• Enthusiasts about AI

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• have a broad understanding of machine learning, its concepts, and its methods.
• Implementing fundamental machine learning algorithms such as back propagation and k-means clustering.
• Tackling tasks such as multi-class classification and anomaly detection.
• The ability to use Octave and Matlab to complete practical projects involving optical character recognition using a wide variety of approaches.
• Get real-world experience on how to implement AI in the field of your work.

• Introduction to Machine Learning
  • Applications of machine learning
  • What is machine learning?
  • Supervised learning
  • Unsupervised learning
  • Jupyter Notebooks
  • Linear regression model
  • Cost function formula
  • Cost function intuition
  • Visualizing the cost function
  • Visualization examples
  • Gradient descent
  • Implementing gradient descent
  • Gradient descent intuition
  • Learning rate
  • Gradient descent for linear regression
  • Running gradient descent

• Regression with multiple input variables
  • Multiple features
  • Vectorization
  • Gradient descent for multiple linear regression
  • Feature scaling
  • Checking gradient descent for convergence
  • Choosing the learning rate
  • Feature engineering
  • Polynomial regression

• Classification
  • Logistic regression
  • Decision boundary1
  • Cost function for logistic regression
  • Simplified Cost Function for Logistic Regression
  • Gradient Descent Implementation
  • The problem of overfitting
  • Addressing overfitting
  • Cost function with regularization
  • Regularized linear regression
  • Regularized logistic regression