Our Machine Learning Course Syllabus

Qualified academic and industry specialists crafted our machine learning certification course. This online training plan covers the following topics.
- What is Machine Learning?
- Supervised Learning
- Unsupervised Learning
- Machine Learning Honor Code
- How to Use Discussion Forums
- Linear Regression with One Variable
- Linear Algebra Review
- Multiple Features
- Gradient Descent for Multiple Variables
- Features and Polynomial Regression
- Normal Equation
- Non Invertibility
- Basic Operations
- Moving Data Around
- Computing Data
- Plotting Data
- Control Statements
- Classification
- Hypothesis Representation
- Decision Boundary
- Cost Function
- Simplified Cost Function and Gradient Descent
- Advanced Optimization
- Multiclass Classification: One-vs-all
- The Problem of Overfitting
- Cost Function
- Regularized Linear Regression
- Regularized Logistic Regression
- Non-linear Hypotheses
- Neurons and the Brain
- Model Representation
- Examples and Intuitions I
- Multiclass Classification
- Cost Function
- Backpropagation Algorithm
- Backpropagation Intuition
- Implementation Note: Unrolling Parameters
- Gradient Checking
- Random Initialization
- Putting It Together
- Autonomous Driving
- Evaluating a Hypothesis
- Model Selection and Train/Validation/Test Sets
- Diagnosing Bias vs. Variance
- Regularization and Bias/Variance
- Learning Curves
- Prioritizing What to Work On
- Error Analysis
- Error Metrics for Skewed Classes
- Trading Off Precision and Recall
- Data For Machine Learning
- Error Analysis
- Optimization Objective
- Large Margin Intuition
- Mathematics Behind Large Margin Classification
- Kernels I, Kernels II
- Using an Support Vector Machine
- Introduction to Unsupervised Learning
- K-Means Algorithm
- Optimization Objective
- Random Initialization
- Choosing the Number of Clusters
- Data Compression
- Data Visualization
- Principal Component Analysis Problem Formulation
- Principal Component Analysis Algorithm
- Reconstruction from Compressed Representation
- Choosing the Number of Principal Components
- Advice for Applying PCA
- Problem Motivation
- Developing and Evaluating an Anomaly Detection System
- Gaussian Distribution
- Multivariate Gaussian Distribution
- Anomaly Detection vs. Supervised Learning
- Anomaly Detection using the Multivariate Gaussian Distribution
- Introduction to Recommendation
- Problem Formulation
- Content Based Recommendations
- Collaborative Filtering
- Collaborative Filtering Algorithm
- Vectorization: Low Rank Matrix Factorization
- Implementational Detail: Mean Normalization
- Learning With Large Datasets
- Stochastic Gradient Descent
- Mini-Batch Gradient Descent
- Stochastic Gradient Descent Convergence
- Online Learning
- Map Reduce and Data Parallelism
- Problem Description and Pipeline
- Sliding Windows
- Getting Lots of Data and Artificial Data
- Ceiling Analysis