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Artificial Intelligence Certification Training
One of the top providers of online IT training worldwide is VISWA Online Trainings. To assist beginners and working professionals in achieving their career objectives and taking advantage of our best services, We provide a wide range of courses and online training.
About Course
🌐 Artificial Intelligence (AI) Online Training
Artificial Intelligence (AI) is one of the most transformative technologies of the modern era, empowering machines to think, learn, and act intelligently. This course provides in-depth training in Artificial Intelligence concepts, machine learning, neural networks, natural language processing, and deep learning — equipping learners to design and deploy intelligent systems across industries like healthcare, finance, automation, and more.
Its Core Capabilities Include:
- Artificial Intelligence Fundamentals & Concepts:
Understand the foundations of Artificial Intelligence, including agents, environments, search algorithms, and knowledge representation. - Machine Learning (ML):
Learn supervised, unsupervised, and reinforcement learning techniques using real-world data. - Deep Learning:
Master neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) using TensorFlow and PyTorch. - Natural Language Processing (NLP):
Build applications that process and understand human language using text analysis and sentiment detection. - Computer Vision:
Develop image classification, object detection, and facial recognition systems using Artificial Intelligence frameworks. - Model Deployment & MLOps:
Deploy Artificial Intelligence and ML models in production using cloud platforms and automate workflows using MLOps principles. - Artificial Intelligence Ethics & Governance:
Understand ethical Artificial Intelligence , responsible data usage, and fairness in Artificial Intelligence model design.
📍 Bonus: Certification Tracks
- Microsoft Certified: Azure Artificial Intelligence Engineer Associate
- Google Cloud Professional Machine Learning Engineer
- IBM Applied Artificial Intelligence Professional Certificate
- Artificial Intelligence Professional (Viswa Online Trainings)
Artificial Intelligence Training Course Syllabus
Deep Learning: A revolution in Artificial Intelligence
Limitations of Machine Learning
What is Deep Learning?
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis
- What is Data Mining
Data
- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data & Sources
- Data Quality & Changes
- Data Quality Issues
- Data Quality Story
- What is Data Architecture
- Components of Data Architecture
- OLTP vs OLAP
- How is Data Stored?
Big Data
- Big Data Architecture
- Big Data Technologies
- Big Data Challenge
- Big Data Requirements
- Big Data Distributed Computing & Complexity
- Hadoop
- Map-Reduce Framework
- Hadoop Ecosystem
Data Science Deep Dive
- What Data Science is
- Why Data Scientists are in demand
- What is a Data Product
- The growing need for Data Science
- Large Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases
- Data Science Project Life Cycle & Stages
- Data Acquisition
- Where to source data
- Techniques
- Evaluating input data
- Data formats
- Data Quantity
- Data Quality
- Resolution Techniques
- Data Transformation
- File format Conversions
- Anonymization
Python
- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc.
- Starting Python
- Interpreter PATH
- Using the Interpreter
- Running a Python Script
- Using Variables
- Keywords
- Built-in Functions
- StringsDifferent Literals
- Math Operators and Expressions
- Writing to the Screen
- String Formatting
- Command Line Parameters and Flow Control.
- Lists
- Tuples
- Indexing and Slicing
- Iterating through a Sequence
- Functions for all Sequences
Operators and Keywords for Sequences
- Range() function
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets.
Numpy & Pandas
- Learning NumPy
- Introduction to Pandas
- Creating Data Frames
- GroupingSorting
- Plotting Data
- Creating Functions
- Slicing/Dicing Operations.
Deep Dive – Functions & Classes & Oops
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting Collections of Collections
- Classes & OOPs
Statistics
- What is Statistics
- Descriptive Statistics
- Central Tendency Measures
- The Story of Average
- Dispersion Measures
- Data Distributions
- Central Limit Theorem
- What is Sampling
- Why Sampling
- Sampling Methods
- Inferential Statistics
- What is Hypothesis testing
- Confidence Level
- Degrees of freedom
- What is pValue
- Chi-Square test
- What is ANOVA
- Correlation vs Regression
- Uses of Correlation & Regression
Machine Learning, Deep Learning & Artificial Intelligence using Python
- Introduction
- ML Fundamentals
- ML Common Use Cases
- Understanding Supervised and Unsupervised Learning Techniques
Clustering
- Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures
- Creating predictive models
- Understanding K-Means Clustering
- Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
- Case study
Implementing Association rule mining
- Case study
- Decision Tree Classifier
- How to build Decision trees
- What is Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Decision Tree
- Confusion Matrix
- Case study
- Random Forest Classifier
- Naive Bayes Classifier
- Problem Statement and Analysis
- Various approaches to solving a Data Science Problem
- Pros and Cons of different approaches and algorithms.
- Linear Regression
- study
- Introduction to Predictive Modeling
- Linear Regression Overview
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Case study
- Logistic Regression Overview
- Data Partitioning
- Univariate Analysis
- Bivariate Analysis
- Multicollinearity Analysis
- Model Building
- Model Validation
- Model Performance Assessment AUC & ROC curves
- Scorecard
- Support Vector Machines
- Case Study
- Introduction to SVMs
- SVM History
- Vectors Overview
- Decision Surfaces
- Linear SVMs
- The Kernel Trick
- Non-Linear SVMs
- The Kernel SVM
- Time Series Analysis Describe Time Series data
- Format your Time Series data
- List the different components of Time Series data
- Discuss different kind of Time Series scenarios
- Choose the model according to the Time series scenario
- Implement the model for forecasting
- Explain working and implementation of ARIMA model
- Illustrate the working and implementation of different ETS models
- Forecast the data using the respective model
- What is Time Series data?
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenarios based on which different Exponential
- The smoothing model can be applied
- Implement respective model for forecasting
- Visualizing and formatting Time Series data
- Plotting decomposed Time Series data plot
- Applying ARIMA and ETS model for Time Series forecasting
- Forecasting for given Time period
- Case Study
- Machine learning algorithms Python
- Various machine learning algorithms in Python
- Apply machine learning algorithms in Python
Artificial Intelligence Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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Live Virtual Training
- Schedule your sessions at your comfortable with timings
- Instructor-Led training real time projects
- Certification Guidance
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Self-Paced Learning
- Complete set of live-online training session recorded videos.
- Learn technology at your own pace.
- Get access for lifetime.
Corporate Training
- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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Artificial Intelligence Online Training FAQ'S
What is Artificial Intelligence?
AI is the simulation of human intelligence in machines that can learn, reason, and make decisions to perform tasks autonomously.
What is the difference between AI, Machine Learning, and Deep Learning?
AI is the broader concept of intelligent machines; ML is a subset that focuses on algorithms that learn from data, and Deep Learning is an ML technique based on neural networks.
What is supervised learning?
Supervised learning uses labeled data to train algorithms to predict outcomes or classify inputs.
What are neural networks?
Neural networks are computing systems inspired by the human brain, consisting of layers of interconnected nodes that process data patterns.
What are some real-world applications of AI?
AI is used in self-driving cars, recommendation systems, fraud detection, chatbots, and predictive maintenance.
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