Training & Duration
- Live classes (Monday to Friday)
- 20 Days of Training
Course Features
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- 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.
Certification
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
