AI Master Course
Unlock the Power of AI with Our Live Training
Are you ready to take your career to the next level and become an expert in AI?
Join our AI live training program and receive hands-on training from industry professionals. Our program is designed to equip you with the skills and knowledge you need to become an AI expert.
Program Highlights
Our AI Live Training Program is designed to help beginners and professionals learn the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) through interactive online sessions led by experienced instructors. Our program is tailored to provide you with a comprehensive understanding of AI, covering everything from the basics to the latest cutting edge advancements in the field of Artificial Intelligence.
- Live Training
- 24*7 Dedicated Support*
- Real World Case Studies
- Experienced Mentors
- Assessments
- Mock Interviews
- Personalized Mentorship and Support
- Career Advancement Options
- Network of AI Professionals
Course Curriculum
Our AI Live Training Program is taught by experienced instructors in the field of AI. They will guide you through a comprehensive tailored curriculum designed by the team, that covers everything from the basics of AI to the latest cutting edge advancements in the field of AI. You will learn how to use the latest tools and techniques in AI to solve real-world problems and create intelligent systems.
Module 1 : Introduction
- Course Overview
- Understanding AI
- Introduction to Machine Learning
- Introduction to Deep Learning
- Differences Between AI, ML and DL
- Applications of AI and Related Fields
- Current Trends in AI
- Understanding Job Roles in the Field of AI
- Why AI
Module 2 : Python
- Introduction to Python
- Why Python
- Installation of Python
- Setting up Anaconda Distribution
- Identifiers, Variables
- Keywords, Comments
- Standard Inputs and Outputs
- Data types
- Operators
- Conditional Statements
- Loops
- Data Structures
- Functions
- Recursions
- Exceptional Handling
- Debugging
- Regular Expressions
- Multithreading
- . . .
Module 3 : Python for AI
- NumPy
- Pandas
- Seaborn
- Matplotlib
- Plotly
- . . .
Module 4 : Foundational Math
- Role of Math in AI
- Basics of Linear Algebra
- Basics of Calculus
- Basics of Statistics
- Basics of Probability
- . . .
Module 5 : Probability & Statistics
- Introduction to Probability and Statistics
- Basics
- Understanding Data
- Statistical Metrics
- Percentiles
- Inter-Quartile Range
- Skewness, Kurtosis, SNV
- Random Variables
- Probability
- Conditional Probability
- Independent and Mutually Exclusive Events
- Bayes Theorem
- Distributions
- How Distributions are Used
- Distributions Based on Domain
- Understanding Covariance
- Pearson Correlation Coefficient
- Spearman Rank Coefficient
- Correlation
- Using Correlations
- Understanding Difference between Correlation and Covariance
- Confidence Interval
- Hypothesis Testing
- Standardization
- Normalization
- Kernel Density Estimation
- Sampling
- Central Limit Theorem
- . . .
Module 6 : Linear Algebra
- Introduction
- Why Linear Algebra
- Use in this domain
- Introduction to Equations
- Introduction to Vectors
- Introduction to Matrices
- Matrices Concepts
- Distance and Different types of Distances
- Measurement Methods at different spaces
- . . .
Mock Interview 1
Module 7 : Machine Learning
- Introduction to Machine Learning
- Types of Machine Learning Algorithms
- Understanding the Flow of ML
- Waterflow vs AGILE Models
- Applications
- Formulating a Business Problem
- Looking at the Required Resources
- How to Relate a Business Problem to Machine Learning Problem
- Understanding the Key Issues in Different Domains
- Model Performance Metrics
- EDA
- Understanding Different Plots
- Inferring from Plots
- Plotting Tools
- Understanding Nulls
- Imputation Techniques
- Understanding and Handling Outliers
- Dimensionality Reduction
- Splitting the Data
- Understanding Train. Test and Validation Datasets and Importance
- Ideal Splitting Techniques and Ratios
- Vectorizing and Its Importance
- Data Leakage
- Handling Data Leakage
- Vectorizing Techniques
- Understanding the Math and Operation of Each Vectorizing Technique
- Limitations
- Feature Engineering
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Understanding Classification and Regression Algorithms
- Understanding Bias, Variance
- Bias Variance Trade-off
- ML Algorithms
- Tuning
- Understanding Math Behind
- Ensemble Models
- . . .
Mock Interview 2
Real World Case Studies
Module 8 : Deep Learning
- Introduction to Deep Learning
- History
- Why Now ?
- Biological Inspiration
- Understanding Human Brain
- Application of Deep Learning
- Advantages of Deep Learning
- Limitations of Deep Learning
- Understanding Computational Resources
- Understanding Biological and Artificial Neurons
- Building Neurons
- MLPs
- Understanding the Math Behind (Notation, Back Propagation . . .)
- Activation Functions
- Training MLP
- Vanishing and Exploding Gradient Problem
- Bias – Variance Trade-off
- Concept of Drop Out
- Batch Normalization
- Regularization
- Weight Initialization Techniques
- Optimizers
- How to Train a Deep Learning Model
- Understanding Deep Learning Toolkits
- Tensorflow
- CNNs
- RNNs
- . . .
Mock Interview 3
Real World Case Studies
Module 9 : Cloud Computing
- Introduction to Cloud Computing
- Basic Architecture
- Cloud Computing Services
- Advantages
- Workspaces
- Types of Services
- AWS
- Introduction
- Creating an AWS Account
- Understanding Management Console
- Elastic Compute Cloud
- Understanding Roles and Policies
- Auto Scaling
- Lambda
- Virtual Private Cloud
- Route 53
- Amazon S3
- Cloud Front
- Amazon Database Services
- Azure
- Introduction
- Creating an Azure Account
- Overview
- Understanding about Dashboard
- Component
- Resource Groups
- Storage
- Azure Data Bricks
- Azure Data Factory
- . . .
Module 10 : Deployment and Other Technologies
- Understanding Deployment
- Intro to
- Tableau or PowerBI
- Excel
- PySpark
- Flask
- Dashboard Designing
- . . .
Mock Interview Series and Career Guidance
Real World
Case Studies
One of the most exciting components of our AI Online Training Course is the case studies section. These case studies are carefully curated to showcase the diversity of AI applications and help you understand how AI is being used in different industries.
Medical Image Analysis
In this case study, you will learn how AI can be used to analyzing medical images and diagnosis. You will work with real medical images and learn how to train an AI model to identify in detecting tumors or lesions.
Text Classification
In this case study, you will learn how AI can be used to analyzing medical images and diagnosis. You will work with real medical images and learn how to train an AI model to identify in detecting tumors or lesions.
Computer Vision based Monitoring System
In this case study, you will learn how AI an ML can be used in Text based analytics and classification. You will work with real world datasets and learn how to analyze, clean, feature engineer and train an model on to classify the data.
These case studies are just a few examples of the diverse applications of AI that you will explore during our course. By the end of the training, you will have a comprehensive understanding of AI algorithms, tools, and techniques, and will be able to apply them to solve real-world problems.
What you will Learn
Take the First Step Towards a Promising Career in AI and ML
Join Us Today for Our Live Training Program
Enrolling in our AI Live Training Program will not only give you the skills and knowledge needed to apply AI and ML techniques to real-world problems but will also help you stay ahead of the curve in this fast-evolving field.
Join us today for our AI Live Training Program and take the first step towards a promising career in AI and ML!
For Downloading the Detailed Course Curriculum
Tech Stack
At Arduos, we use a variety of technologies and tools to help our students learn the latest and cutting edge techniques in the field of Artificial Intelligence.
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Frequently Asked Questions
What is the AI Master Course about?
The AI Live training course is a comprehensive training program that covers the basics and advanced concepts of Artificial Intelligence and Machine Learning. The course includes live sessions, hands-on projects, and interactive discussions to provide a comprehensive learning experience.
Who should attend the AI Master Course ?
The AI Live training Course is suitable for professionals, students, and anyone who wants to learn about AI and ML. It is ideal for individuals who want to start a career in AI or want to enhance their knowledge in this field.
What is the duration of the AI Live training Course?
The duration of the AI Live training Course is ideally 12 Months. Some batches may spill over for a few days or a few weeks.
What is the format of the AI Live training Course?
The training is conducted online, through live sessions, interactive discussions, and hands-on projects. Participants can attend the sessions from their own location and interact with the instructor.
Is prior experience in AI or ML required to attend the AI Master Course?
Prior experience in AI or ML is not a mandate, but would be an advantage if you have idea on that. Participants should have basic knowledge of mathematics, programming, and statistics.
Will I receive a certificate upon completion of the AI Master Course?
Yes, upon successful completion of the Course, participants will receive a certificate of completion.
Can I ask questions during the AI Master Course?
Yes, participants are encouraged to ask questions during the live sessions and interactive discussions. The instructor will be available to answer your questions and provide additional guidance.