Machine Learning Engineer Certification Program (CMLE)

Foundations of Machine Learning

• Introduction to Machine Learning Paradigms
  

• Key Machine Learning Use Cases and Real-World Applications
  

• Setting Up the ML Environment: Python, Anaconda, and Jupyter Notebooks

Mathematics for Machine Learning

• Linear Algebra: Matrices, Vectors, Eigenvalues, and Eigenvectors

• Calculus: Derivatives, Gradients, and Optimization Techniques

• Probability and Statistics: Probability Distributions, Bayesian Inference

Supervised Learning Algorithms

• Regression Models: Linear, Logistic, Ridge, and Lasso
  

• Classification Algorithms: Decision Trees, Random Forest, SVMs
  

• Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC Curves

Unsupervised Learning Algorithms

• Clustering Techniques: K-Means, Hierarchical Clustering, DBSCAN
  

• Dimensionality Reduction: PCA, t-SNE, LDA
  

• Anomaly Detection: Gaussian Mixture Models, Isolation Forests
  

Deep Learning Architectures

• Neural Networks: Feedforward and Backpropagation
  

• Convolutional Neural Networks (CNNs) and Their Applications
  

• Recurrent Neural Networks (RNNs), LSTMs, and GRUs
  

Scalable and Distributed Machine Learning

• Introduction to Big Data Tools: Apache Spark, Hadoop
  

• Using TensorFlow Extended (TFX) for Large Scale ML
  

• Deploying ML Models on Cloud Platforms: AWS SageMaker, Azure ML Studio

Model Deployment and Monitoring

• Building and Optimizing ML Pipelines

• Continuous Integration and Deployment (CI/CD) for ML Models

• Model Performance Monitoring and Management

Capstone Project

• Choose a Domain-Specific Problem (Finance, Healthcare, Retail)

• Develop an End-to-End ML Solution (Data Collection, Modeling, Deployment)

• Present the Solution with Detailed Documentation and Performance Analysis