AWS Machine Learning - Specialty Training in Kolkata for MLS-C01 Certification.

AWS Machine Learning Speciality Training Topics Covered:

Section 1: Data Engineering

• Create data repositories for Machine Leaning.
• Identify data job styles and job types (for example, batch load, streaming).
• Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).
• Transform data in transit (ETL, AWS Glue, Amazon EMR, AWS Batch).
• Handle ML-specific data by using MapReduce (for example, Apache Hadoop, Apache Spark, Apache Hive).

Section 2: Exploratory Data Analysis

• Sanitize and prepare data for modeling.
• Identify and extract features from datasets, including from data sources such as text, speech, image, public datasets.
• Analyze and evaluate feature engineering concepts (for example, binning,tokenization, outliers, synthetic features,one-hot encoding, reducing dimensionality of data).
• Create graphs (for example, scatter plots, time series, histograms, box plots).
• Interpret descriptive statistics (for example, correlation, summary statistics,p-value).
• Perform cluster analysis (for example, hierarchical, diagnosis, elbow plot,cluster size).

Section 3: Modeling - Frame business problems as ML problems.

• Determine when to use and when not to use ML.
• Know the difference between supervised and unsupervised learning.
• Select from among classification, regression, forecasting, clustering, and recommendation models.
• XGBoost, logistic regression, k-means, linear regression, decision trees,random forests, RNN, CNN, ensemble, transfer learning.
• Express the intuition behind models.
• Train ML models.
• Perform hyperparameter optimization.
• Evaluate metrics (area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score).
• Compare models by using metrics (for example, time to train a model, quality of model, engineering costs).

Section 4: Machine Learning Implementation and Operations

• Build ML solutions for performance, availability, scalability,resiliency, and fault tolerance.
• Recommend and implement the appropriate ML services and features for a given problem.
• Determine when to build custom models and when to use Amazon SageMaker built-in algorithms.
• Apply basic AWS security practices to ML solutions.
• Deploy and operationalize ML solutions.
• Expose endpoints and interact with them.

Section 5: Project - Create and deploy a Machine Leaning Model

• Requirement Analysis
• Draft Solution
• Build Model
• Test and Train Model
• Deploy Model - MLOps Concepts
• Test End Point

AWS machine learning certification preparation - A deep dive into SageMaker, generative AI, data engineering, modeling & more

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What are some real-world applications projects of AWS machine learning?

• Customer Churn Prediction: Build a model using AWS SageMaker to predict customer churn based on historical data, enabling businesses to take proactive measures to retain customers.
• Image Recognition for Healthcare: Develop an AWS ML model that analyzes medical images (e.g., X-rays) to detect abnormalities and assist healthcare professionals in diagnosis.
• Recommendation Engine: Create a recommendation system using AWS Personalize that suggests products, movies, or content to users based on their preferences and behavior.
• Natural Language Processing (NLP) Chatbot: Build a conversational chatbot using AWS Lex and Lambda that understands and responds to customer queries, enhancing customer support.
• Anomaly Detection in IoT Data: Utilize AWS IoT Analytics and ML services to identify anomalies in sensor data from IoT devices, enabling predictive maintenance and fault prevention.
• Sentiment Analysis for Social Media: Develop an AWS Comprehend-based application that analyzes social media posts to gauge public sentiment about a brand or product.
• Predictive Maintenance for Manufacturing: Implement an AWS ML solution that predicts when industrial machinery needs maintenance, reducing downtime and maintenance costs.
• Fraud Detection: Create a fraud detection system using AWS Fraud Detector that analyzes transactions in real-time to identify potentially fraudulent activities in financial transactions.
• Inventory Optimization: Use AWS Forecast to build a demand forecasting model that helps retailers optimize inventory levels and reduce overstock or stockouts.
• Voice Recognition: Develop a voice-controlled application using AWS Polly and Transcribe that converts spoken language into text and performs actions based on voice commands.
• Energy Consumption Forecasting: Use AWS SageMaker to build a model that forecasts energy consumption patterns, helping energy companies optimize power generation and distribution.
• Automated Resume Screening: Employ AWS Textract and Comprehend to automate the screening of job applicants' resumes, matching candidates to job requirements.