Machine Learning Engineer Certification Program (CMLE)
Become a Certified Expert in Machine Learning Engineering
Skills you will gain:
80+ Hours Video
15 Live Mentor Sessions
e-LMS Content & Hands-on Sheet
24*7 Email Support
One Dedicated Co-ordinator
Aim:
Enroll now to become a certified Machine Learning Engineer and master essential skills in developing, deploying, and maintaining machine learning models. Build expertise in areas like deep learning, neural networks, and model optimization, and kickstart your career in the high-demand ML sector!
Program Objectives:
- To provide a strong foundation in Machine Learning concepts and tools.
- To train participants in building and deploying scalable ML models.
- To emphasize best practices in data preprocessing, model evaluation, and deployment.
- To ensure participants are equipped to address ethical considerations in ML.
- To prepare participants for certification and advanced career opportunities in ML engineering.
What you will learn?
1. 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
2. Mathematics for Machine Learning
- Linear Algebra: Matrices, Vectors, Eigenvalues, and Eigenvectors
- Calculus: Derivatives, Gradients, and Optimization Techniques
- Probability and Statistics: Probability Distributions, Bayesian Inference
3. 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
4. Unsupervised Learning Algorithms
- Clustering Techniques: K-Means, Hierarchical Clustering, DBSCAN
- Dimensionality Reduction: PCA, t-SNE, LDA
- Anomaly Detection: Gaussian Mixture Models, Isolation Forests
5. Deep Learning Architectures
- Neural Networks: Feedforward and Backpropagation
- Convolutional Neural Networks (CNNs) and Their Applications
- Recurrent Neural Networks (RNNs), LSTMs, and GRUs
6. 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
7. Model Deployment and Monitoring
- Building and Optimizing ML Pipelines
- Continuous Integration and Deployment (CI/CD) for ML Models
- Model Performance Monitoring and Management
8. 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
Intended For :
- Aspiring Machine Learning Engineers
- Data scientists and software developers
- Students and professionals in AI, computer science, and engineering
- Entrepreneurs and business leaders exploring AI-driven solutions
Career Supporting Skills