Artificial Intelligence and Machine Learning Courses Online
Artificial Intelligence and Machine Learning are the most popular technologies contributing to the global digital transformation. Join Artificial Intelligence and Machine Learning Courses and become an Artificial Intelligence Engineer to get way to an exciting, developing profession.
About Artificial Intelligence and Machine Learning Course
(Instructor-Led Online Training)
Artificial Intelligence is a broader concept of machines depicting humans and carrying out smart tasks easily, whereas Machine Learning is its subset maximizing the performance of a machine. The combined study of it makes you familiar with the technologies incorporated and their importance today and in the future.
At the very beginning of our Artificial Intelligence and Machine Learning courses, we will understand AI/ML and its relevance to networking, use of python in AI/ML, and fundamental of AI/ML.
Then you will be introduced to the Language Models (LMs), Predictive AI, and Generative AI adding core practical exposure to your skills on how AI and ML-based projects are implemented in organizations.
So, if you are someone seeking a career as an Artificial Intelligence Engineer, our training is the best choice for you. Any knowledge of Programming or advanced mathematics isnโt compulsory to join the AI and ML Courses, so anyone interested in learning and innovating in the field of automation can enroll with us.
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BATCH
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Batch 1
Weekends Batch
(Saturday & Sunday)
For 8 Weeks
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Batch 2
Weekdays Batch
(Monday to Thursday)
For 4 Weeks
12
READY TO LEVEL UP?
Upcoming Batches Are Now Open!
DATE
Batch 1
Weekends Batch
(Saturday & Sunday)
TIME
For 8 Weeks
SEATS LEFT
Sold out
DATE
Batch 2
Weekdays Batch
(Monday to Thursday)
TIME
For 4 Weeks
SEATS LEFT
12
COURSE OUTLINE
Introduction to AI/ML and Its Relevance to Networking
Overview of AI and ML
- Definition and key concepts
- History and evolution of AI and ML
- Differences between AI, ML, and Deep Learning
AI and ML in the Networking Domain
- Importance of AI and ML in modern networks
- Use cases: Traffic prediction, anomaly detection, network optimization
Basic Concepts of Networking
- Review of essential networking concepts relevant to AI/ML
- The relationship between network data and machine learning
Python for AI/ML and Networking
Introduction to Python
- Overview of Python and its use in AI/ML
- Data types, variables, and operators
- Control structures (loops, conditions, functions)
- Working with files and handling exceptions
- Python libraries for data science: NumPy, Pandas, Matplotlib
- Python libraries for network automation: Netmiko, Ncclient, Requests
Working with Network Dataย
- Parsing and analyzing network logs
- Basic data manipulation techniques with Pandas
Data Handling and Processing
Understanding Network Data Formats
- JSON, XML, and YAML in network automation
- Syslogs, SNMP data, and NetFlow records
Working with Data in Python
- Using Pandas for data manipulation and cleaning
- Parsing JSON and XML configurations
- Storing network data in CSV and databases
Data Visualization for Network Insights
- Using Matplotlib and Seaborn to plot network performance data
- Creating dashboards to monitor traffic trends and latency patterns
Fundamentals of Machine Learning
Understanding Machine Learning
- Supervised vs. Unsupervised learning
- Key algorithms: Linear regression, decision trees, k-means clustering
Feature Engineering for Network Dataย
- Extracting meaningful features from network logs
- Preprocessing data: Normalization, handling missing values
Introduction to Predictive AI for Networking
What is Predictive AI?
- Predictive analytics and machine learning
- How predictive AI differs from traditional machine learning
Network Data for Predictive AI
- Collecting and processing network data
- Feature selection and engineering
Predictive AI Use Cases in Networking
- Traffic prediction
- Network failure forecasting
- Load balancing optimization
Fundamentals of Language Models and AI
Introduction to Language Models (LMs):
- What are language models (LMs), and how do they work?
- The architecture behind LMs: Large Language Models (LLMs), n-grams, RNNs, and Transformers
- The importance of large-scale datasets for training LMs
Creating Language Models:
- Overview of the process to train language models
- Fine-tuning LMs for specific use cases
Applications of Language Models in Networking:
- Automated log analysis, chatbots for network support
- AI-driven configuration writing using natural language inputs
Generative AI and its Application in Network Engineering
Introduction to Generative AI
- What is Generative AI?
- Overview of Generative Adversarial Networks (GANs) and Transformer models
- Generative AIโs role in simulating network environments
Generative AI for Network Configuration and Scripting
- Automated generation of network scripts
- AI-driven configuration templates for complex networks
Lab Outline
- Installing Python, VSCode, Jupyter Notebook, and necessary libraries
- Basic scripting and automation with Python andย Ansible
- Data manipulation using sample network data collected from Cisco IOS devices
- Automating simple tasks on Cisco IOS and IOS XE using Machine Learning Models
- Using Scikit-learn for network data analysis
- Hands-on with linear regression and clustering on network data
- Using Scikit-learn to build predictive models for traffic patterns
- Using historical network data to predict future performance
- Applying predictive models to Cisco IOS
- Using generative AI to create network automation scripts for Cisco Infrastructure
- Applying generated configurations to real networks for dynamic and adaptive network setups
What is AI and ML course training?
Artificial Intelligence is the study of making computers do tasks that at present humans do better like Driving a Car, Playing Chess, Assisting and more. Whereas, Machine Learning is an application of AI that givesย the ability to machines to learn on their own, without being explicitly programmed and improve performance through past experiences.
Artificial Intelligence and Machine Learning Course is designed for everyone who wants to learn and innovate in the field of automation. This training makes you familiar with AI and ML popular technologies and their importance today and in the future.
The Beginner level of AI and ML Courses deal with the conceptual understanding of the fundamentals of Artificial Intelligence and Machine Learning concepts. In this Foundation-level training, you learn about AI and ML applications and their real-life use cases in different industries, including banking, healthcare, automation, e-commerce etc.
Additionally, you get core practical exposure on how AI and ML based projects are implemented at organizations under expertโs guidance of experienced industry mentors.
Even if you are a professional with a non-technical background, you can get hands-on with Artificial Intelligence & Machine Learning experience without any knowledge of Programming.
Key Objectives of AI and ML Course
- To make you understand Artificial Intelligence and key components of ML model.
- Evaluating and Interpreting machine learning algorithm types.
- Developing basic Supervised and Unsupervised learning models.
- Understanding how to apply various methods to test Machine Learning training models.
- Makes you able to work on application development projects based on machine learning at your organization.
Who can attend AI and ML Course?
The AIย and Machine Learning course is designed for all those who are interested in learning AI and ML techniques inย theย big data domain andย writeย intelligent applications.
The most common attendees of this course are Higher Management employees, Developers & Engineers starting work on AI and ML projects.
Prerequisites for AI and ML Course
Curiosity to learn Artificial Intelligence and Machine Learning.
Learn from Industry Experts
Abhijit Bakale
Mr. Abhijit is the brain behind our training modules, lab setup, and course materials at PyNet Labs. He plays a big role in making sure our training is practical, updated, and easy to follow.ย He is a Cisco Certified Systems Instructor (CCSI #35944) and also holds the highly respected DevNet Expert certification (#20230021). In total, he has earned 14 Cisco certifications, showing just how skilled and experienced he is. Mr. Abhijit has delivered over 20,000 hours of training and has helped thousands of students grow in their careers. His deep knowledge and teaching style make learning complex topics much easier.ย Recently, he was invited by Cisco DevNet to speak at a live webinar on Network Services Orchestrator (NSO), which shows the high level of trust and recognition he has in the networking world.ย With his vast experience and passion for teaching, Mr. Abhijit continues to inspire and guide learners every day.
Chirag Dhall
Mr. Chirag is a skilled and enthusiastic trainer at PyNet Labs. He has been with the company since the beginning and has played a key role in shaping many of our training programs. He delivers training for CCNA, CCNP ENCOR, CCNP ENARSI, CCIE Enterprise, Cisco SD-WAN, and other networking courses.ย He holds two top-level Cisco certificationsโCCIE Enterprise Infrastructure (#68677) and CCSI (#36137)โwhich show his deep knowledge and teaching skills.ย So far, he has trained more than 3,500 students. Whether someone is just starting out or already working in the industry, Mr. Chirag is great at explaining tough topics in a simple and easy-to-understand way. His friendly teaching style and strong understanding of networking make him a favorite among students.ย
Sohel Japanwala
Mr. Sohel is a knowledgeable and dedicated trainer at PyNet Labs, with strong expertise in both Networking and Data Science. He has trained over 1,000 students, helping them build solid skills and confidence in their careers.ย He delivers training on CCNA, ENCOR, ENARSI, BGP, OSPF, MPLS, CCNP SCOR, FTD, and many other networking topics. Alongside this, he also teaches Data Science and Data Analytics, making him a well-rounded trainer with a unique mix of skills.ย Students appreciate Mr. Sohel for his friendly and supportive teaching style. Whether someone is just starting out or already working in the field, he makes complex topics simple and easy to understand. His clear explanations and dedication to student success have earned him a strong reputation.
Sudhangshu Bag
Mr. Sudhangshu Bag is a highly experienced trainer with over 9 years of expertise in the Networking field. His expertise are in CCNA, CCNP ENCOR, CCNP ENARSI, CompTIA, and Red Hat, making him a versatile and well-qualified instructor.ย In addition to Networking, he has strong skills in Linux, AWS, and computer networks, which add great value to his training sessions. So far, he has delivered more than 1,000 hours of training at PyNetLabs, helping students understand both the basics and advanced topics with ease.ย Mr. Sudhangshu is known for his calm teaching style, deep technical knowledge, and genuine passion for helping others learn. His students appreciate how he explains topics clearly and supports them throughout their learning journey.ย
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Frequently Asked Questions
Q1. Is their any Cisco Certification for AI and ML?
Cisco will now offer a new elective for its CCDE (Cisco Certified Design Expert) certification focused on AI infrastructure validating the skills of experienced Network Engineers in designing data center networks that can handle the demanding needs of AI workloads.
The very first time, CCDE AI Infrastructure elective will be available for testing is on February 9, 2025 at Cisco Live.
Q2. Does AI/ML skills offer high-payingย job roles?
Some of the common job roles you get after AI and ML Course are – Machine Learning Engineer, Data Scientist, AI Research Scientist, AI/ML Architect, and Computer Vision Engineer. And, yes, they are considered high-paying due to the specialized skills required and the growing demand for professionals in these fields.
Q3. How can AI and ML Training help me?
If you are a Network Engineer, this training will help you in:
- Implementing different AI/ML use-cases and minimize the burden on infrastructure resources
- Building high-performance generative AI network fabrics
- Ensuring the security, sustainability and compliance of networks
- Making appropriate trade-offs between cost and power
- Matching compute power and cloud needs to measure carbon use
Q4. Which Cisco products use AI and ML?
Cisco products that leverage AI and ML for enhanced network performance, security, and management include:
- Cisco Catalyst Center (DNA Center)
- Cisco Nexus Dashboard Insights (NDI)
- Cisco Meraki
- Cisco AppDynamics
- Cisco ThousandEyes
- Cisco Secure Network Analytics
