AWS Machine Learning Certification Exam | Complete Guide

AWS Machine Learning Certification Exam | Complete Guide

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 17 Hours | 9.08 GB

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

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:

  • How to select proper ML technique to solve a given business problem?
  • Which AWS service could work best for a given problem?
  • How to design, implement and scale secure ML solutions?
  • 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.

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
Table of Contents

INTRODUCTION, DATAML LINGO, AWS DATA STORAGE
1 What makes this course unique
2 AWS Machine Learning Exam Overview
3 Course Outline
4 Guidelines and Best Practices
5 Section Introduction
6 What is Machine Learning and AI – Part 1
7 What is Machine Learning and AI – Part 2
8 Amazon Web Services
9 AIML Data Lingo – Labeled vs. unlabeled
10 AIML Data Lingo – Data Types
11 Database vs. datalake vs. warehouse
12 AWS Storage S3 DynamoDB RDS
13 BONUS Learning Path
14 GET YOUR BONUS MATERIALS
15 Section 1 Slides

AMAZON S3
16 Section Introduction
17 Amazon S3 Partitions and Tags
18 S3 Storage Tiers and LifeCycle Polices
19 S3 Encryption
20 S3 Security – Part 1
21 S3 Security – Part 2
22 Additional Information
23 GET YOUR BONUS MATERIALS
24 Section 2 Slides

AWS DATA MIGRATION, GLUE, PIPELINE, STEP and BATCH
25 Section Introduction
26 AWS Glue – part #1
27 AWS Glue – part #2
28 AWS Data Pipeline
29 AWS Data Migration Service DMS
30 AWS Batch
31 AWS Step Function
32 GET YOUR BONUS MATERIALS
33 Section 3 Slides

DATA STREAMING AND KINESIS
34 Section Introduction
35 Kinesis Overview
36 AWS Kinesis Video Streams – Part 1
37 AWS Kinesis Video Streams – Part 2
38 AWS Kinesis Data Streams – Part 1
39 AWS Kinesis Data Streams – Part 2
40 AWS Kinesis Firehose
41 AWS Kinesis Analytics – Part 1
42 AWS Kinesis Analytics – Part 2
43 GET YOUR BONUS MATERIALS
44 Section 4 Slides

JUPYTER NOTEBOOK, SCIKIT-LEARN, PYTHON PACKAGES, AND DISTRIBUTIONS
45 Section Introduction
46 Jupyter Notebooks and Scikit Learn
47 Python Packages (Pandas, Numpy, Matplotlib and Seaborn)
48 Data Visualization
49 Distributions (Normal, Standard, Poisson, Bernoulli)
50 Time Series
51 GET YOUR BONUS MATERIALS
52 Section 5 Slides

ATHENA, QUICKSIGHT, EMR
53 Section Introduction
54 Athena – Part 1
55 Athena – Part 2
56 Amazon Quicksight – Part 1
57 Amazon Quicksight – Part 2
58 Elastic Map Reduce – Part 1
59 Elastic Map Reduce – Part 2
60 EMR and Hadoop
61 EMR and Spark
62 GET YOUR BONUS MATERIALS
63 Section 6 Slides

FEATURE ENGINEERING
64 Introduction to Feature Engineering
65 Feature Engineering Overview
66 Amazon SageMaker GroundTruth
67 Feature Selection
68 Scaling
69 Imputation
70 Outliers
71 One Hot Encoding
72 Binning
73 Log Transformation
74 Shuffling, Feature Splitting, Unbalanced Datasets
75 Text Feature Engineering overview
76 Bag of words, punctuation, and dates (easy ones!)
77 Term Frequency Inverse Document Frequency (TF-IDF)
78 N-Grams (Unigram vs. Bigram vs. Trigram)
79 Orthogonal Sparse Bigram (OSB)
80 Cartesian Product Transformation
81 GET YOUR BONUS MATERIALS
82 Section 7 Slides

MACHINE AND DEEP LEARNING BASICS – PART #1
83 Section Introduction
84 Artificial Neural Networks Basics Single Neuron Model
85 Activation Functions
86 Multi-Layer Perceptron Model
87 How do Artificial Neural Networks Train
88 ANN Parameters Tuning – Learning rate and batch size
89 Tensorflow playground
90 Gradient Descent and Backpropagation
91 Overfitting and Under fitting
92 How to overcome overfitting
93 Bias Variance Trade-off
94 L2 Regularization
95 L1 Regularization
96 GET YOUR BONUS MATERIALS
97 Section 8 Slides

MACHINE AND DEEP LEARNING BASICS – PART #2
98 Section Introduction
99 Artificial Neural Networks Architectures
100 Convolutional Neural Networks
101 Recurrent Neural Networks
102 Vanishing Gradient Problem
103 Long Short Term Memory (LSTM) Networks
104 Model Performance Assessment – Confusion Matrix
105 Model Performance Assessment – Precision, recall, F1-score
106 Model Performance Assessment – ROC, AUC, Heatmap, and RMSE
107 Transfer Learning
108 Ensemble Learning – Bagging and Boosting
109 K Fold Cross Validation
110 GET YOUR BONUS MATERIALS
111 Section 9 Slides

MACHINE AND DEEP LEARNING IN AWS – PART #1
112 Section Introduction
113 AWS SageMaker
114 AWS SageMaker Part 2
115 Deep Learning on AWS
116 SageMaker Built-in algorithms overview
117 Object Detection
118 Image classification
119 Semantic Segmentation
120 Linear Learner
121 Factorization Machines
122 XGboost
123 Seq2Seq
124 DeepAR
125 Blazing Text
126 GET YOUR BONUS MATERIALS
127 Section 10 Slides

MACHINE AND DEEP LEARNING IN AWS – PART #2
128 Section Introduction
129 SageMaker Built-in Algorithms Overview
130 Random Cut Forest
131 K Nearest Neighbors KNN
132 K Means
133 Principal Component Analysis PCA
134 IP Insights
135 Reinforcement Learning
136 Neural Topic Model NTM
137 LDA
138 Object2Vec
139 Multi Model
140 Automatic Model Tuning
141 GET YOUR BONUS MATERIALS
142 Section 11 Slides

AWS HIGH LEVEL AIML SERVICES
143 Section Introduction
144 SageMaker AIML High Level Services
145 Top 5 AIML Services
146 ReKognition
147 Amazon Comprehend and Comprehend Medical
148 Translate
149 Transcribe
150 Polly
151 Forecast
152 Lex
153 Personalize
154 Textract
155 AWS DeepLens
156 AWS DeepRacer
157 GET YOUR BONUS MATERIALS
158 Section 12 Slides

ML IMPLEMENTATION AND OPERATION
159 Introduction
160 SageMaker Components Review
161 SageMaker Model Deployment
162 Resources and Instance Types
163 Online vs. Offline Validation
164 Production Variants and Canary Deployment
165 SageMaker Neo
166 AWS IoT Greengrass
167 Docker Containers
168 AWS Security Overview
169 In-Transit and Rest Encryption
170 AWS CloudWatch
171 AWS CloudTrail
172 Section 13 Slides

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