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 Build 30+ ML Projects in 30 Days in AWS, Master SageMaker JumpStart, Canvas, AutoPilot, DataWrangler, Lambda & S3
Do you want to become an AWS Machine Learning Engineer Using SageMaker in 30 days?
Do you want to build super powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start?
Are you an absolute beginner and want to break into AI, ML and Cloud Computing and looking for a course that includes everything you need?
Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently?
If the answer is yes to any of these questions, then this course is for you!
Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospects.
AWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS SageMaker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.
This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows:
Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) and CloudWatch, (2) The benefits of cloud computing, the difference between regions and availability zones and what’s included in the AWS Free Tier Package, (3) How to setup a brand-new account in AWS, setup a Multi-Factor Authentication (MFA) and navigate through the AWS Management Console, (4) How to monitor billing dashboard, set alarms, S3/EC2 instances pricing and request service limits increase, (5) The fundamentals of Machine Learning and understand the difference between Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS) and Deep Learning (DL), (6) Learn the difference between supervised, unsupervised and reinforcement learning, (7) List the key components to build any machine learning models including data, model, and compute, (8) Learn the fundamentals of Amazon SageMaker, SageMaker Components, training options offered by SageMaker including built-in algorithms, AWS Marketplace, and customized ML algorithms, (9) Cover AWS SageMaker Studio and learn the difference between AWS SageMaker JumpStart, SageMaker Autopilot and SageMaker Data Wrangler, (10) Learn how to write our first code in the cloud using Jupyter Notebooks. We will then have a tutorial covering AWS Marketplace object detection algorithms such as Yolo V3, (11) Learn how to train our first machine learning model using the brand-new AWS SageMaker Canvas without writing any code!
Section 2 (Days 4 – 5): we will learn the following: (1) Label images and text using Amazon SageMaker GroundTruth, (2) learn the difference between data labeling workforces such as public mechanical Turks, private labellers and AWS curated third-party vendors, (3) cover several companies’ success stories that have leveraged data to maximize revenues, reduce costs and optimize processes, (4) cover data sources, types, and the difference between good and bad data, (5) learn about Json Lines formats and Manifest Files, (6) cover a detailed tutorial to define an image classification labeling job in SageMaker, (7) auto-labeling workflow and learn the difference between SageMaker GroundTruth and GroundTruth Plus, (8) learn how to define a labeling job with bounding boxes (object detection and pixel-level Semantic Segmentation), (9) Label Text data using Amazon SageMaker GroundTruth.
Section 3 (Days 6 – 10): we will learn: (1) how to perform exploratory data analysis (EDA), (2) master Pandas, a super powerful open-source library to perform data analysis in Python, (3) analyze corporate employee information using Pandas in Jupyter Notebooks in AWS SageMaker Studio, (4) define a Pandas Dataframe, read CSV data using Pandas, perform basic statistical analysis on the data, set/reset Pandas DataFrame index, select specific columns from the DataFrame, add/delete columns from the DataFrame, Perform Label/integer-based elements selection, perform broadcasting operations, and perform Pandas DataFrame sorting/ordering, (5) perform statistical data analysis on real world datasets, deal with missing data using pandas, change pandas DataFrame datatypes, define a function, and apply it to a Pandas DataFrame column, perform Pandas operations, and filtering, calculate and display correlation matrix, use seaborn library to show heatmap, (6) analyze cryptocurrency prices and daily returns of Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Cardano (ADA) and Ripple (XRP) using Matplotlib and Seaborn libraries in AWS SageMaker Studio, (7) perform data visualization using Seaborn and Matplotlib libraries, plots include line plot, pie charts, multiple subplots, pairplot, count plot, correlations heatmaps, distribution plot (distplot), Histograms, and Scatterplots, (8) Use Amazon SageMaker Data wrangler in AWS to prepare, clean and visualize the data, (9) understand feature engineering strategies and tools, understand the fundamentals of Data Wrangler in AWS, perform one hot encoding and normalization, perform data visualization Using Data Wrangler, export a data wrangler workflow into Python script, create a custom formula and apply it to a given column in the data, generate summary table tables in Data Wrangler, and generate bias reports.
Section 4 (Days 11 – 18): we will learn: (1) machine learning regression fundamentals including simple/multiple linear regression and least sum of squares, (2) build our first simple linear regression model in Scikit-Learn, (3) list all available built-in algorithms in SageMaker, (4) build, train, test and deploy a machine learning regression model using SageMaker Linear Learner algorithm, (5) list machine learning regression algorithms KPIs such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Percentage Error (MPE), Coefficient of Determination (R2), and adjusted R2, (6) Launch a training job using the AWS Management Console and deploy an endpoint without writing any code, (7) cover the theory and intuition behind XG-Boost algorithm and how to use it to solve regression type problems in Scikit-Learn and using SageMaker Built-in algorithms, (8) learn how to train an XG-boost algorithm in SageMaker using AWS JumpStart, assess trained regression models performance, plot the residuals, and deploy an endpoint and perform inference.
Section 5 (Days 19 – 20): we will learn: (1) hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization, (2) Understand bias variance trade-off and L1 and L2 regularization, (3) perform hyperparameters optimization using Scikit-Learn library and using SageMaker SDK.
Section 6 (Days 21 – 24): we will learn: (1) how to train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier, (2) list the difference between various classifier models KPIs such as accuracy, precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), (3) train an XG-boost and Linear Learner algorithms in SageMaker to solve classification type problems, (4) learn the theory and intuition behind K Nearest Neighbors (KNN) in SageMaker and learn how to build, train and test a KNN classifier model in SageMaker.
Section 7 (Days 25 – 28): we will learn: (1) how to use AutoGluon library to perform prototyping of AI/ML models using few lines of code, (2) leverage AutoGluon to train multiple regression and classification models and deploy the best one, (3) leverage Amazon SageMaker Autopilot and SageMaker Canvas to train multiple models without writing any code.
Section 8 (Days 29 – 30): we will learn: (1) how to define and invoke lambda functions in AWS, (2) understand Machine Learning workflow automation using AWS Lambda, Step functions and SageMaker Pipelines, (3) learn how to define a lambda function in AWS management console, (4) understand the anatomy of Lambda functions, (5) learn how to configure a test event in Lambda, and monitor Lambda invocations in CloudWatch, (6) define a Lambda function using Boto3 SDK, (7) test the lambda function using Eventbridge (cloudwatch events), (8) understand the difference between synchronous and asynchronous invocations, and Invoke a Lambda function using Boto3 SDK.
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