Instructor-led Sessions
36 Hours of Online Live Instructor-Led Classes. Weekend Class : 12 sessions of 3 hours each. Weekday Class : 18 sessions of 2 hours each.
Validate your skills as an MACHINE LEARNING expert
Achieving an MACHINE LEARNING certification exemplifies your expertise, commitment and can advance your career. It’s an industry-recognized credential that demonstrates your ability to design, deploy and operate applications and infrastructure on MACHINE LEARNING.
We designed this cloud architect certification training for anyone seeking to learn the major components of Amazon Web Services (AWS). By the end of the course, you’ll be prepared to pass the associate-level AWS Certified Solutions Architect certification exam. The AWS certification is a must-have for any IT professional, and an AWS certified solution architects take home about $120,000 per year.
This course emphasizes AWS cloud best practices and recommended design patterns to help you think through the process of architecting optimal IT solutions on AWS. Case studies throughout the course showcase how some AWS customers have designed their infrastructures and the strategies and services they implemented.
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Training Features
Lifetime Access
You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.
Real-life Case Studies
Live project based on any of the selected use cases, involving implementation of the various Machine Learning services.
24 x 7 Expert Support
We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.
Assignments
Live project based on any of the selected use cases, involving implementation of the various Machine Learning services.
Certification
Towards the end of the course, you will be working on a project. Iteanz certifies you as an Machine Learning Expert based on the project.
Course Outline
Chapter 1:Installation and configuration
Chapter 2:Data Preprocessing
Chapter 3:Regression Techniques
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Linear Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Model Performance
Chapter 4:Classification Techniques
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naïve Bayes Classification
- Decision Tree Classification
- Random Forest Classification
- Evaluating Classification Model Performance
Chapter 5:Natural Language Processing (NLP)
- Basic of NLP
- Language preprocessing Techniques
- Auto summarizing the given text document
Chapter 6:Clustering Techniques
- K-Means Clustering
- K-mini Batch Clustering
- Hierarchical Clustering
Chapter 7:Elbow Method
Chapter 8:Curve Smoothening Techniques
Chapter 9:Association Rule Learning
Chapter 10:Reinforcement Learning
Chapter 11:Basics of Numpy and panda
Chapter 12:Deep Learning
- Basics/what is Deep Learning
Chapter 13:Artificial Neural Networks
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
Chapter 14:Statistics Basics
- Standard Deviation
- Variance
- Co-Variance
- T-distribution
- Pearson Correlation Coefficient (PCC)/ Correlation Coefficient
Chapter 15:Model Selection
Why iteanz for MACHINE LEARNING training?
- Top technical trainers
- Real Time and Hands on Experience Training
- On Time Course Completion
- Comprehensive curriculum
- Innovative & interactive Training
- Superb satisfaction scores
- High Certification Pass Rate
Prerequisites
- Basic Linux Commands (Optional)
- You will need to set up an AWS Account (We help you to Setup)
- A Windows, Linux or Mac PC/Laptop
- 4G or At least 4MBPS Internet Speed
Intended Audience
- Machine Learning Absolute Beginners. No prior Machine Learning experience necessary
- Existing Solutions Architects
- Programmers Interested in Deploying Applications on Machine Learning