What you would learn in Become an AWS Machine Learning Engineer in 30 Days-New 2022 course?
Machine Learning is the next step in the most exciting fields in technology to be in today! ML and AI will transform our lives how electricity changed our lives a century ago. ML is widely used in banking, finance as well as healthcare, transportation, and even technology. The field is rapidly growing with possibilities and career opportunities.
AWS is among the most used cloud computing platforms around the world, and many businesses rely on AWS for cloud computing requirements. AWS SageMaker is an entirely managed service provided by AWS that allows data scientists and AI practitioners to develop and test AI/ML models rapidly and efficiently.
This course is distinctive and different in many ways. It offers a variety of practice opportunities, including quizzes, as well as final capstone projects. Students will learn to develop models that can be used in production with the help of AWS. The course is broken down into eight main sections:
Section 1 (Days 1 to 3): we will study the following: (1) Start with an AWS and Machine Learning essentials "starter pack," which includes the most important AWS services like Simple Storage Service (S3) and Elastic Compute Cloud (EC2), Identity and Access Management (IAM) and CloudWatch, (2) The advantages that cloud computing offers, including the differences between availability zones and regions and the features available within AWS Free Tier Package, (3) AWS Cost-Free Tier Package. (3) What you need to know to create your account as a brand new one in AWS to set up multi-factor authentication (MFA) as well as browse through AWS Management Console, AWS Management Console (4) How to keep track of the billing dashboards, set alarms S3/EC2 instances limit on request and pricing are increased, (5) The fundamentals of Machine Learning and understand the distinction in Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS) and Deep Learning (DL), (6) Learn the distinction between unsupervised, supervised, and reinforcement learning. (7) the essential elements that make up any model of machine learning, which include models, data, and computation, (8) learn the basic concepts in Amazon SageMaker, SageMaker Components, and the training options provided by SageMaker with built-in algorithms, AWS Marketplace as well as custom ML algorithms. (9) Explore AWS SageMaker Studio to discover the differences between AWS SageMaker the JumpStart feature, SageMaker Autopilot, and SageMaker Data Wrangler. (10) Learn to create our first program on the cloud using Jupyter Notebooks. Then we'll go through an instructional session on AWS Marketplace object detection algorithms like Yolo V3, (11) Learn how to train our initial machine learning model with the new AWS SageMaker Canvas without writing any code!
2. (Days 4-5): we will learn the following: (1) Label images and texts using Amazon SageMaker GroundTruth, (2) understand the distinction between the different workforces using data labeling like public mechanical Turks as well as private labellers, and AWS third-party vendors that have been curated, (3) cover several successful companies that leveraged data to boost revenue, decrease costs, and streamline processes (4) examine the different types of data sources, and the distinction between insufficient and sound data, (5) learn about Json Lines formats and Manifest Files, (6) cover an in-depth tutorial on how to create an image labeling task within SageMaker, (7) auto-labeling workflow and discover the differences among SageMaker GroundTruth as well as GroundTruth Additionally, (8) learn how to create a labeling task using the bounding box (object identification and pixels-level semantic segmentation), (9) Label Text data with Amazon SageMaker GroundTruth.
Part 3 (Days 6-10): we will be taught: (1) how to conduct EDA, or exploratory data analysis (EDA), (2) master Pandas which is a robust open-source library that can perform the analysis of data using Python, (3) analyze corporate employee data by using Pandas within Jupyter Notebooks using AWS SageMaker Studio, (4) create the Pandas Dataframe Read CSV data with Pandas and do fundamental statistical analysis of the data, reset or set the Pandas DataFrame index choose particular columns in the DataFrame Add/delete column names from the DataFrame and perform Label/integer-based element selection and broadcasting, and carry out Pandas DataFrame sorting and order, (5) perform statistical analysis of data on real-world datasets, address missing data using pandas. Modify the pandas DataFrame datatypes, create an algorithm, then apply it to the Pandas DataFrame column. Execute Pandas operations and filtering, compute and display the correlation matrix, make use of the seaborn library to display heatmaps, and (6) analyze cryptocurrency prices and daily returns for Bitcoin (BTC), Ethereum (ETH), and the Litecoin (LTC), Cardano (ADA) and Ripple (XRP) by using the Matplotlib library and Seaborn libraries within AWS SageMaker Studio, (7) create data visualization by using Seaborn as well as Matplotlib library. plots can include line plots, pie charts, multi subplots, and pairplots, as well as heatmaps for correlations, count plots, Distribution plots (distplot), Histograms, and scatterplots. (8) Make use of Amazon SageMaker Data Wrangler in AWS to clean, prepare and present the data (9) learn about the strategies and tools used in feature engineering to understand the basics of Data Wrangler within AWS and perform one-hot encoding and normalization of Data visualization using Data Wrangler. You can export your Data Wrangler workflow to a Python script, write a custom formula that you can apply to a specific column of data, create summary tables using Data Wrangler, and create bias reports.
4. (Days 11-18): we will learn: (1) machine learning regression fundamentals, such as simple/multiple linear regression as reasonably as least sum of squares, (2) develop our first basic linear regression model using Scikit-Learn, (3) list all available algorithms built into SageMaker, (4) build the model, train it, and test it, then apply a machine-learning regression model using the SageMaker Linear Learner algorithms. (5) give a list of machine-learning regression KPIs like the Mean Absolute Error (MAE) and Mean Squared Error (MSE) and the Root Mean Squared Error (RMSE), Mean Percentage Error (MPE), and The Coefficient of Determination (R2) and an adjusted R2. (6) Start a training job using AWS Management Console, (6) launch a training job using AWS Management Console, and then set up an endpoint without writing programming code. (7) go over the concepts and reasoning behind the XGBoost algorithm and how you can use it to solve regression-related problems in Scikit-Learn using SageMaker built-in algorithms, (8) discover how to train an XGBoost algorithm within SageMaker by using AWS JumpStart and evaluate the trained regression models' performance, graph the residuals and then deploy an endpoint, and then make an inference.
Section 5 (Days 19-20): we will be taught: (1) hyperparameters optimization strategies, such as grid search, random search, and Bayesian optimization. (2) learn about the bias-variance trade-offs and regularization of L1 and L2, and (3) optimize hyperparameters using the Scikit-Learn library and SageMaker SDK.
Section 6 (Days 21-24): we will learn: (1) how to develop various classification algorithms, such as Logistic Regression and Support Vector Machine K-Nearest Neighbors and Random Forest Classifier, and (2) define the distinction between the various models of classifier KPIs, such as accuracy recall, precision F1-score (F1-score) as well as Receiver Operating Characteristics Curve (ROC) and Area under the Curve (AUC), (3) create an XG-boost and linear Learner methods in SageMaker to solve classification problems. (4) study the theories and logic of K Nearest Neighbors (KNN) within SageMaker and discover how to create the model, train it and test it. KNN Classifier Model in SageMaker.
Section 7 (Days 25-28): we will learn: (1) how to utilize the AutoGluon library to create prototypes of AI/ML models with just an only a few lines of code and (2) make use of AutoGluon to create various regression or classification models and then use the most effective ones, (3) leverage Amazon SageMaker Autopilot and SageMaker Canvas to train multiple models without writing code.
Section 8 (Days 29-30): we will learn: (1) how to define and use lambda functions in AWS, (2) understand Machine Learning workflow automation using AWS Lambda, Step Functions, and SageMaker Pipelines, and (3) learn how to define a Lambda function in the AWS Management Console, (4) be aware of the structure of Lambda functions, (5) learn how to create the test event in Lambda and track Lambda calls within CloudWatch, (6) define the Lambda function with Boto3 SDK. (7) examine the lambda function with Eventbridge (cloud watch event events), (8) understand the distinction between synchronous and asynchronous calls, and invoke a Lambda function with Boto3 SDK.
Build Models, Train, Test, and Deploy Machine Learning Models using AWS
Learn SageMaker Built-in Algorithms like Linear Learner, XG-Boost, Principal Component Analysis (PCA), and K-Nearest Neighborhoods
Create and Execute Text and Image Labeling Jobs Utilizing AWS SageMaker GroundTruth
Prepare clean and visualize data using AWS SageMaker Data Wrangler without code.
Optimize ML model hyperparameters using GridSearch, Bayesian & Random Search Optimization Techniques
Master Key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) and CloudWatch
Learn about the machine Learning workflow automation with AWS Lambda, Step Functions, and SageMaker Pipelines.
Learn to create a lambda function within the AWS management console. Also, learn the structure of Lambda functions and know-how to set up an event test within Lambda.
Learn Machine Learning Regressions as well as Classifier Models using No-code AWS Canvas
Learn how to leverage Amazon SageMaker Autopilot and SageMaker Canvas to train models without writing code.
Perform exploratory Data Analysis and Visualization Using Pandas, Searborn, and Matplotlib Libraries.
Know the KPIs of Regression Models such as RMSE, MSE MAE, R2, and Adjusted R2
Learn Classification Models' KPIs like Accuracy, Recall, Precision, F1 Score, ROC, and AUC
Create a Machine Learning Training Job Using AWS SageMaker JumpStart
Install an endpoint using Amazon SageMaker, perform Inference and generate predictions
Create the Lambda function with Boto3 SDK. Test the function with Eventbridge (cloud watch event)
Know the distinction between Asynchronous and Synchronous Lambda Functions invocations
Perform AI/ML Models Prototyping Using AutoGluon Library
How do you monitor the billing dashboard and set alarms? Pricing for S3/EC2 instances and request limits increase
Know the difference between Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS), and Deep Learning (DL)
Learn the basics about Amazon SageMaker, SageMaker Components Training options that include integrated algorithms AWS Marketplace as well as custom ML Algorithms
Make use of the Yolo V3 Object detection Algorithm that is available through the AWS Marketplace
Know the format and the Use of the case of Json Lines and Manifest Files
Learn about auto-labeling workflows and comprehend the distinction between SageMaker GroundTruth and GroundTruth Plus
Learn to define a labeling task using the use of bounding boxes (object detection) as well as Pixel-level Semantic Segmentation, as well as text data
Learn the difference between the workforces that label data in AWS like mechanical Turks and private labelers. AWS customized third-party vendors
Learn the distinction between Reinforcement, Supervised and Unsupervised Machine Learning Strategies
Create data visualizations using Matplotlib and Seaborn libraries. Plots can include line plots, pie charts, subplots as well as counterplots, pairplots, and as well as correlation heatmaps.
Data wrangler workflows can be exported into a Python script, then create a unique formula, apply it to a specific column of data, and generate summary tables/bias reports.
Find out how you can train an XG boost algorithm in SageMaker with the help of AWS JumpStart. Examine the trained results of the model, graph residuals, and set up an endpoint
Know the Bias-Variance Tradeoff Regularization of L1 and Strategies
Train/Test a variety of ML Classifiers, such as Logistic Regression as well as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest Classifiers
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