AWS Machine Learning Certification Exam | Complete Guide

500+ Slides | 200+ Questions | Become Certified in AWS ML| SageMaker|Data Engineering | Visualization | Model Deployment

What you'll learn:

• Data Engineering
• Data types, Python Libraries (pandas, Numpy, scikit Learn, MatplotLib, Seaborn), data distributions, timeseries, Feature Engineering (imputation, binning, encoding, and normalization)
• AWS Services and Algorithms
• Amazon SageMaker, Amazon S3 Storage services, AWS Glue
• AWS Kinesis Services (Kinesis firehose, Kinesis video streams, Kinesis data streams, Kinesis analytics)
• Redshift, Redshift Spectrum, DynamoDB, Athena, Amazon Quicksight, Elastic Map Reduce (EMR)
• Rekognition, Lex, Polly, Comprehend, Translate, transcribe, BlazingText Word2Vec, DeepAR, Factorization Machines, Gradient Boosted Trees (XGBoost)
• Image Classification (ResNet), IP Insights, K-Means Clustering, K-Nearest Neighbor (k-NN)
• Latent Dirichlet Allocation (LDA), Linear Learner (Classification), Linear Learner (Regression)
• Neural Topic Modelling (NTM), Object2Vec, Object Detection, Principal Component Analysis (PCA), Random Cut Forest, Semantic Segmentation, and Seqence2Sequence
• Machine and Deep Learning Basics

Update 01/02/2020: Section #13 on Machine Learning Implementation and Operations is released.

Machine and Deep Learning are the hottest tech fields to master right now! Machine/Deep Learning techniques are widely adopted in many fields such as banking, healthcare, transportation and technology. Amazon has recently introduced the AWS machine Learning Certification Speciality exam and its quite challenging! AWS Certified Machine Learning Specialty is targeted at data scientists and developers who design, train and deploy AI/ML models to solve real-world challenging problems.

The bad news: this exam is a very challenging AWS exam since it tests the candidate’s knowledge on multiple aspects such as (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS services solution to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyperparameters tuning. You need to answer these questions in order to pass the exam:

o How to select proper ML technique to solve a given business problem?

o Which AWS service could work best for a given problem?

o How to design, implement and scale secure ML solutions?

o How to choose the most cost-effective solution?

The good news: With over 500+ slides and over 50 practice questions, this course is by far the most comprehensive course on the market that provides students with the foundational knowledge to pass the AWS Machine Learning Certification exam like a pro! This course covers the most important concepts without any fillers or irrelevant information.