Enroll Course: https://www.udemy.com/course/become-an-aws-machine-learning-engineer-in-30-days-new-2022/
Are you looking to break into the booming fields of Machine Learning (ML), Artificial Intelligence (AI), and Cloud Computing? Do you want to build powerful, production-level ML applications on AWS but don’t know where to start? Or perhaps you’re an entrepreneur seeking to leverage ML to boost your business? If any of these resonate with you, then the ‘AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT’ course on Udemy is an absolute must-have.
This comprehensive course is designed for absolute beginners and aspiring professionals alike, aiming to equip you with the skills to become an AWS Machine Learning Engineer within a month. It masterfully blends the power of AWS SageMaker, a fully managed service for ML, with the revolutionary capabilities of ChatGPT for code automation.
The course is meticulously structured into eight sections, taking you from the absolute essentials to advanced deployment strategies:
**Section 1: AWS & ML Essentials (Days 1-3)**
Dive into the core of AWS, understanding key services like S3, EC2, IAM, and CloudWatch. You’ll learn about cloud computing benefits, AWS account setup, security best practices (including MFA), and navigating the AWS Management Console. The fundamentals of AI, ML, Data Science, and Deep Learning are clarified, along with the types of machine learning (supervised, unsupervised, reinforcement). Crucially, you’ll get acquainted with Amazon SageMaker, its components, training options, and explore SageMaker Studio, JumpStart, Autopilot, and Data Wrangler. You’ll even write your first cloud code with Jupyter Notebooks and train your first model using SageMaker Canvas without writing a single line of code!
**Section 2: Data Labeling with SageMaker GroundTruth (Days 4-5)**
Master the art of data preparation with Amazon SageMaker GroundTruth. Learn about image and text labeling, different workforce options, and the importance of data quality. You’ll delve into labeling job definitions for image classification and object detection, understanding concepts like bounding boxes and semantic segmentation, and even explore auto-labeling workflows.
**Section 3: Exploratory Data Analysis with Pandas & Visualization (Days 6-10)**
This section is a deep dive into data wrangling using the powerful Pandas library. You’ll learn to perform comprehensive exploratory data analysis (EDA), manipulate DataFrames, handle missing data, and conduct statistical analysis on real-world datasets, including cryptocurrency price analysis. Data visualization using Matplotlib and Seaborn will be covered extensively, along with mastering SageMaker Data Wrangler for data preparation, feature engineering, and bias reporting.
**Section 4: Machine Learning Regression (Days 11-18)**
Understand the fundamentals of regression, from simple to multiple linear regression. You’ll build, train, and deploy your first regression models using SageMaker’s built-in algorithms like Linear Learner and XGBoost. The course covers key regression metrics (MAE, MSE, R2, etc.) and how to assess model performance, including plotting residuals and deploying endpoints for inference.
**Section 5: Hyperparameter Optimization & Regularization (Days 19-20)**
Learn crucial techniques for optimizing your ML models, including grid search, randomized search, and Bayesian optimization. The concepts of bias-variance trade-off and regularization (L1, L2) are explained, and you’ll practice hyperparameter tuning using both Scikit-Learn and the SageMaker SDK.
**Section 6: Machine Learning Classification & ChatGPT Integration (Days 21-24)**
Explore various classification algorithms like Logistic Regression, SVM, KNN, and Random Forest. You’ll understand classification metrics (accuracy, precision, recall, F1-score, ROC/AUC) and apply SageMaker’s Linear Learner and XGBoost for classification tasks. A significant bonus is the integration of ChatGPT as a programming assistant to automate coding tasks.
**Section 7: Automated ML with AutoGluon & SageMaker (Days 25-28)**
Discover the power of automated machine learning (AutoML) with the AutoGluon library. You’ll learn to rapidly prototype and deploy models with minimal code, training multiple regression and classification models and selecting the best performer. SageMaker Autopilot and Canvas are also covered for no-code model training.
**Section 8: ML Workflow Automation (Days 29-30)**
The final section focuses on automating your ML workflows using AWS Lambda, Step Functions, and SageMaker Pipelines. You’ll learn to define and invoke Lambda functions, understand their anatomy, configure test events, monitor invocations, and use the Boto3 SDK for function interaction.
**Why We Recommend This Course:**
This course is exceptionally well-rounded, offering a practical, hands-on approach to becoming an AWS ML Engineer. The inclusion of real-world case studies, quizzes, and capstone projects ensures a deep understanding and practical application of the learned concepts. The progressive structure, starting from AWS fundamentals and moving to advanced SageMaker features and workflow automation, makes it accessible for beginners. The integration of ChatGPT is a forward-thinking addition, preparing you for the future of AI-assisted development.
If you’re serious about a career in ML and want to master one of the leading cloud platforms, this course is an invaluable investment. It provides a clear roadmap to building production-ready ML applications and positions you at the forefront of the AI revolution.
Enroll Course: https://www.udemy.com/course/become-an-aws-machine-learning-engineer-in-30-days-new-2022/