AWS ML Engineer Course

AWS ML Engineer Course


Please register to enroll in this course.

18% GST Extra

If interested kindly fill the inquiry form

SKU: cid_227713 Category:

Training & Duration

  • Live classes (Monday to Friday)
  • 20 Days of Training

Course Features

    • Online lectures: Online live lectures.
    • Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.
Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.


1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Course Outline

AWS Essentials

  • Introduction to AWS
  • Major AWS Services
  • IAM,S3,EC2
  • AWS Sagemaker
  • Sagemaker Studio
  • Sagemaker canvas
  • Demo of using sagemaker canvas

Working with Arrays: Numpy

  • Introduction to NumPy
  • Creating an array
  • Class and Attributes of ndarray
  • Basic Operations
  • Indexing and Slicing
  • Stack operations

Exploratory Data Analysis(EDA): Pandas

  • Series and DataFrame
  • Access elements in Pandas Dataframe
  • Load csv file and Statistical analysis
  • Set/Reset index in pandas
  • iloc and loc operations
  • Delete and Add columns
  • Dealing with missing values
  • Dealing with categorical columns

Data Visualization:Matplotlib

  • Matplotlib Overview
  • Plot line plot in matplotlib
  • Subplot in matplotlib
  • Scatter plots
  • Histrogram and Bar graph
  • Plot correlation using heatmap

Machine Learning using AWS sagemaker and scikit-learn

Linear Regression

  • Simple Linear Regression
  • Loss functions for regression model
  • Calculate R-Squared
  • Multiple Linear Regression
  • AWS Sagemaker Linear Learner Algorithm

Logistic Regression

  • Overview of logistic regression
  • Loss function
  • Evaluation metric for classification model
  • Binary classification using scikit-learn
  • Classification(Bianry & Multi-class) using AWS Sagemaker Linear Learner

K-Nearest Neighbors(KNN)

  • KNN theory
  • Implementing KNN with scikit-learn
  • KNN Parameters
  • n_neighbors
  • metric
  • K-Nearest Neighbor in SagemakerHyperparameter optimization
  • Overview of Hyperparameter
  • Hyperparameter optimization Strategies
  • Bias Variance Tradeoff
  • L1 and L2 Regularization
  • Hyperparameter tunning using GridSearchCV
  • Perform hyperparameter optimization in Sagemaker

Classifiers: SVM, Naive bayes, Decision Tree and Random forest

  • Theory behind SVM, Naive bayes, Decsion Tree and Random forest
  • Text classification using Naive bayes
  • Binary classification using SVM
  • Multiclass classification using Random forest

AutoML and No-code ML

  • AutoGluon for regression type problems
  • AutoGluon for classification type problems
  • AWS sagemaker Autopilot

For inquiry call:  8953463074

Online Live Training Program 2023

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