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  • Supplied by a certified expert with over nine years of experience in artificial intelligence.
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Curriculum in Artificial Intelligence

Foundation of AI & ML
  • Introduction to Data Science, AI&ML
  • R Essentials
  • Statistical Analysis
  • Python for Artificial Intelligence
  • Python Essentials
  • Python Environment Setup
  • Python Data Types
  • Python Looping and Control Statements
  • Object-Oriented Programming Concepts
  • Database Connection
  • Python Libraries for Artificial Intelligence
  • Numpy
  • Scipy
  • Pandas
  • MatPlot
  • Data Management
  • Data Acquisition
  • Data Pre-processing and Preparation
  • Data Transformation and Quality
  • Handling Text Data
  • Big Data Fundamentals
  • Big Data Frameworks(Spark, Hadoop, NoSQL)
  • SAS-Data Analytics
  • SAS Introduction
  • SAS Functions
  • SAS Operators
  • SAS Procedures
  • SAS Graphs
  • SAS Macros
  • SAS Format
  • Statistical Decision Making
  • Data Visualisation
  • Sampling and Estimation
  • Inferential Statistics
  • Predictive Analytics
  • Linear Regression
  • Multiple Linear Regression
  • Non-Linear Regression
  • Forecasting Models
  • Machine Learning
  • ML Foundations
  • Clustering
  • Classification(Naive Bayes Classifier, K-Nearest Neighbors)
  • Association Rule Mining
  • Artificial Intelligence
  • Foundations of AI
  • Convolution Neural Networks
  • Recurrent Neural Networks
  • Deep Learning with Keras and TensorFlow
  • Deep Learning Libraries
  • Keras API
  • TensorFlow
  • Deep Learning Algorithms
  • Advanced Deep Learning and Computer Vision
  • Distributed and Parallel Computing
  • Deploying Deep Learning Models
  • Reinforcement Learning
  • Generating Images with Neural Style
  • Object Detection through Convolutional Neural Networks
  • Cloud Computing and AWS
  • Introduction to Cloud Computing and AWS
  • Storage Volumes and Elastic Compute
  • Virtual Private Cloud
  • Simple Storage Services
  • AWS Lambda and Amazon Machine Learning
  • Tableau 10
  • Introduction to Data Visualisation
  • Tableau Architecture
  • Working with Data Blending
  • Creation of Sets
  • Calculations, Expression, and Parameters
  • Dashboards, Stories, and Filters
  • Tableau Prep
  • Curriculum in Artificial Intelligence

    Artificial Intelligence Is Preferred by Over 35% of Developers. Artificial Intelligence is the most widely used and in-demand programming language in the IT industry.

    • Introduction to Data Science, AI&ML
    • R Essentials
    • Statistical Analysis
    • Python Essentials
    • Python Environment Setup
    • Python Data Types
    • Python Looping and Control Statements
    • Object-Oriented Programming Concepts
    • Database Connection
    • Numpy
    • Scipy
    • Pandas
    • MatPlot
    • Data Acquisition
    • Data Pre-processing and Preparation
    • Data Transformation and Quality
    • Handling Text Data
    • Big Data Fundamentals
    • Big Data Frameworks(Spark, Hadoop, NoSQL)
    • SAS Introduction
    • SAS Functions
    • SAS Operators
    • SAS Procedures
    • SAS Graphs
    • SAS Macros
    • SAS Format
    • Data Visualisation
    • Sampling and Estimation
    • Inferential Statistics
    • Linear Regression
    • Multiple Linear Regression
    • Non-Linear Regression
    • Forecasting Models
    • ML Foundations
    • Clustering
    • Classification(Naive Bayes Classifier, K-Nearest Neighbors)
    • Association Rule Mining
    • Foundations of AI
    • Convolution Neural Networks
    • Recurrent Neural Networks
    • Deep Learning Libraries
    • Keras API
    • TensorFlow
    • Deep Learning Algorithms
    • Distributed and Parallel Computing
    • Deploying Deep Learning Models
    • Reinforcement Learning
    • Generating Images with Neural Style
    • Object Detection through Convolutional Neural Networks
    • Introduction to Cloud Computing and AWS
    • Storage Volumes and Elastic Compute
    • Virtual Private Cloud
    • Simple Storage Services
    • AWS Lambda and Amazon Machine Learning
    • Introduction to Data Visualisation
    • Tableau Architecture
    • Working with Data Blending
    • Creation of Sets
    • Calculations, Expression, and Parameters
    • Dashboards, Stories, and Filters
    • Tableau Prep

    Artificial Intelligence Training Projects

    Develop Your Artificial Intelligence Skills With Useful and Interesting Projects.

     

    Customer Churn Prediction

    Build a model to predict whether customers are likely to churn (stop using a service) based on their behavior and demographics.

     

    Simple Chatbot

    Build a basic conversational agent using rule-based or retrieval-based techniques.

     

    Handwritten Digit Recognition

    Develop a model to classify handwritten digits using machine learning algorithms like logistic regression or k-nearest neighbors.

     

    Reinforcement Learning for Games

    Train AI agents to play complex video games using reinforcement learning algorithms like Deep Q-Networks or Policy Gradient methods.

     

    Language Translation

    Create a neural machine translation system capable of translating text between multiple languages.

     

    Video Action Recognition

    Develop a model to recognize and classify human actions in videos, such as walking, running, or sitting.

     

    Reinforcement Learning for Games

    Train AI agents to play complex video games using reinforcement learning algorithms like Deep Q-Networks or Policy Gradient methods.

     

    Language Translation

    Create a neural machine translation system capable of translating text between multiple languages.

     

    Video Action Recognition

    Develop a model to recognize and classify human actions in videos, such as walking, running, or sitting.

    Key Highlights

    Our Instructor

    Get advice from specialists who have certifications and are currently working.

    Artificial Intelligence Training Overview

    Artificial intelligence (AI) is a subfield of computer science concerned with the development of systems and technologies capable of performing activities that would normally require human intelligence. These tasks include comprehending natural language, identifying patterns in data, generating predictions, and learning from experience. AI systems are designed to emulate human cognitive skills such as reasoning, problem solving, perception, and decision-making, allowing them to automate activities, aid people in decision-making processes, and handle complicated issues more effectively. AI includes a wide range of techniques and approaches, such as machine learning, deep learning, natural language processing, computer vision, and robotics. As AI technologies progress, they have the potential to disrupt industries, change the way we live and work, and tackle some of society's most urgent issues.

    Additional Information

    The Most Recent Artificial Intelligence Tools

    • Hugging Face provides a library and platform for developers to access state-of-the-art natural language processing (NLP) models, including BERT, GPT-2, and various transformer-based models. These models enable tasks such as text classification, summarization, sentiment analysis, and language translation.
    • GPT-3, developed by OpenAI, is one of the most powerful language generation models to date. It offers advanced capabilities for generating human-like text and completing natural language processing tasks, making it a valuable tool for applications such as chatbots, text generation, and content creation.
    • AlphaFold, developed by DeepMind, is a deep learning-based system for protein folding prediction. It accurately predicts the 3D structure of proteins from their amino acid sequences, enabling breakthroughs in structural biology, drug discovery, and personalized medicine.
    • PyTorch Lightning is a lightweight wrapper for PyTorch that simplifies the training process of deep learning models. Facebook AI has developed PyTorch Lightning, offering a high-level interface for organizing code, handling distributed training, and logging experiment metrics, making it easier for developers to build and train AI models.

    What goals are achieved in a Artificial Intelligence Course?

    In an artificial intelligence (AI) course, several goals are typically achieved to equip participants with the knowledge, skills, and techniques needed to understand, develop, and apply AI technologies effectively. Some of the key goals achieved in an AI course include:

    • Participants gain a solid understanding of fundamental concepts, principles, and methodologies in artificial intelligence, including machine learning, deep learning, natural language processing, computer vision, robotics, and reinforcement learning.
    • Participants learn about various AI algorithms and techniques used for tasks such as classification, regression, clustering, dimensionality reduction, sequence prediction, image recognition, and decision-making. They gain hands-on experience in implementing and applying these algorithms to real-world problems.
    • Participants learn how to design, develop, and train AI models using programming languages such as Python and AI libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras. They gain practical experience in building and optimizing AI models for different applications and domains.
    • Participants learn how to evaluate the performance of AI models using metrics such as accuracy, precision, recall, F1 score, and area under the curve (AUC). They gain insights into techniques for model validation, cross-validation, hyperparameter tuning, and bias-variance tradeoff to ensure robust and reliable AI solutions.

    Future works for Artificial Intelligence

    The future of artificial intelligence (AI) holds immense potential for transformative advancements across various domains, driving innovation, solving complex problems, and improving quality of life. Some key areas of future work for artificial intelligence include:

    • AI is expected to revolutionize healthcare by enabling personalized medicine, disease diagnosis, treatment recommendation, drug discovery, and genomic analysis. AI-powered medical imaging, predictive analytics, and remote patient monitoring systems will improve patient outcomes, reduce healthcare costs, and enhance access to healthcare services.
    • AI technologies will play a crucial role in the development of autonomous vehicles, drones, and transportation systems. AI-powered navigation, perception, decision-making, and control algorithms will enhance safety, efficiency, and sustainability in transportation, leading to reduced accidents, congestion, and emissions.
    • AI will contribute to the development of smart cities by optimizing infrastructure, energy, transportation, and public services. AI-driven solutions for traffic management, energy efficiency, waste management, and public safety will create more sustainable, livable, and resilient urban environments.
    • AI can help address climate change by optimizing energy usage, predicting environmental trends, and facilitating climate modeling and simulation. AI-powered solutions for renewable energy, carbon capture, climate monitoring, and disaster response will contribute to global efforts to mitigate climate change and build a more sustainable future.

    What new Artificial Intelligence frameworks are there?

    Several new artificial intelligence (AI) frameworks have emerged in recent years, offering developers innovative tools and capabilities for building AI-powered applications. Some of the new AI frameworks include:

    • PyTorch is an open-source machine learning framework developed by Facebook's AI Research lab (FAIR). It is widely used for deep learning research and development due to its dynamic computation graph and user-friendly interface. PyTorch provides flexibility and scalability for building neural networks and is popular among researchers and practitioners.
    • TensorFlow is an open-source machine learning framework developed by Google Brain. TensorFlow 2.0 introduced several improvements, including eager execution, Keras integration as the high-level API, and support for dynamic computation graphs. TensorFlow 2.0 offers ease of use, flexibility, and performance for building and deploying machine learning models.
    • JAX is an open-source numerical computing library developed by Google Research. It provides composable function transformations in Python for high-performance machine learning research. JAX is particularly well-suited for building custom neural network architectures, training models with automatic differentiation, and leveraging hardware accelerators such as GPUs and TPUs.
    • Flax is an open-source neural network library developed by Google Research. It is built on top of JAX and provides a high-level interface for building and training neural networks. Flax emphasizes simplicity, flexibility, and composability, making it easy for developers to experiment with different model architectures and training techniques.
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    Artificial Intelligence Training Objectives

    Acquiring an authorized artificial intelligence certification involves completing a recognized training program or course and passing an assessment or exam to demonstrate proficiency in AI concepts, techniques, and tools. These certifications are typically offered by reputable organizations, institutions, or technology companies and serve as validation of your AI skills and knowledge.

  • Validating your expertise and proficiency in AI technologies.
  • Enhancing your credibility and marketability as an AI professional.
  • Increasing your job prospects and opportunities for career advancement.
  • Providing access to exclusive job opportunities and higher salary prospects.
  • Offering a structured framework for acquiring and mastering AI skills.
  • For Individuals
  • For Organizations
  • Knowledge of programming languages such as Java, R, or Python. grasp of mathematical ideas including probability theory, linear algebra, and calculus.
  • Knowledge of AI principles and methodologies, such as computer vision, natural language processing, deep learning, and machine learning.
  • Proficiency in utilizing AI technologies and libraries, including TensorFlow, PyTorch, scikit-learn, and OpenCV.
  • Critical-thinking and problem-solving abilities to take on initiatives and difficulties using AI in the real world.
  • Obtaining an artificial intelligence certification can potentially lead to a salary increase, as it enhances your qualifications and marketability in the job market. AI professionals with certifications often command higher salaries compared to those without certifications, especially for specialized roles or in-demand skill sets.

  • Machine Learning Engineer
  • Data Scientist
  • AI Researcher
  • Natural Language Processing (NLP) Engineer
  • Computer Vision Engineer
  • AI Software Developer
  • AI Consultant
  • AI Product Manager
  • Robotics Engineer
  • Business Intelligence Analyst
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    Industry Statistics

    Jobs / Month

    248

    Avg. Salary

    ₹ 12,55,200

    Job Roles

    AI/ML Engineer

    Data Scientist

    Robotics Engineer

    AI Consultant

    Artificial Intelligence Certification

    Certificate
    GET A SAMPLE CERTIFICATE
  • Validation of skills and expertise in AI technologies.
  • Access to exclusive job opportunities and higher salary prospects.
  • Structured learning and skill development in specialized areas of AI.
  • Recognition by employers and industry stakeholders.
  • Yes, there are multiple types of artificial intelligence certifications available, offered by various organizations, institutions, and technology companies. These certifications may focus on different aspects of AI, such as machine learning, deep learning, natural language processing, computer vision, robotics, and AI ethics. Some popular AI certification providers include Google, IBM, Microsoft, Amazon Web Services (AWS), NVIDIA, Coursera, edX, and ACTE.

    While holding an artificial intelligence certification can significantly enhance your job prospects and increase your competitiveness in the job market, it does not guarantee employment on its own. Job opportunities ultimately depend on various factors, including your experience, skills, education, networking, and the demand for AI professionals in the industry.

    Yes, you are allowed to seek multiple artificial intelligence course certifications to broaden your skill set, specialize in different areas of AI, and increase your marketability in the job market. Pursuing multiple certifications demonstrates your commitment to continuous learning and professional development and allows you to acquire expertise in various AI technologies, methodologies, and applications.

  • Machine Learning Engineer
  • Data Scientist
  • AI Researcher
  • Natural Language Processing (NLP) Engineer
  • Computer Vision Engineer
  • AI Software Developer
  • AI Consultant
  • AI Product Manager
  • Robotics Engineer
  • Business Intelligence Analyst
  • AI Ethics Specialist
  • Yes, many artificial intelligence certification exams offer an online version that allows candidates to take the exam remotely from their own computers. Online proctoring services ensure the integrity and security of the testing process by monitoring candidates via webcam and screen sharing.

  • While real-world experience can be beneficial for obtaining an artificial intelligence certification, it is not always a strict requirement. Certification programs typically focus on assessing knowledge and skills in AI concepts, techniques, and tools, rather than practical experience. However, having hands-on experience with AI projects and applications can enhance your understanding and readiness for certification exams.
  • The investment of time and money in an ACTE artificial intelligence certification can be justified based on various factors, including the reputation of the certification program, the relevance of the skills learned, and the demand for certified professionals in the job market. ACTE is a reputable training institute known for providing high-quality education and industry-relevant certifications. An artificial intelligence certification from ACTE can enhance your qualifications, improve job prospects, and validate your expertise in AI technologies.

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    The Preferred Partner for 100+ Organizations' Hiring

    Learn from the certified and real time working professionals.

    • Over 100 firms that are looking for top talent for their open positions have come to rely on ACTE as their go-to partner.

    • Businesses have confidence in our ability to match them with the best individuals because of our considerable expertise and proven track record of success.

    • In this section, we'll examine the primary elements influencing this trust and examine how our constant commitment to excellence regularly results in remarkable results for our clients.

    Corporate Clients

    Artificial Intelligence Course Duration and Fees

    Level Course Duration Fees Structure
    Basic 1 - 1.5 Months ₹7,000 - ₹9,000
    Advanced 1.5 - 2 Months ₹7,000 - ₹10,000

    Job Opportunities in Artificial Intelligence

    Artificial Intelligence is preferred by more than 35% of developers. The most widely used and in-demand programming language in the tech industry is Artificial Intelligence.

    Salary In Artificial Intelligence
    Reach Our Placement Officer

    You can Work as a

    AI Product ManagerAI Ethics SpecialistAI ArchitectAI TrainerAI Curriculum DeveloperAI Project ManagerData Engineer

    Upcoming In-Demand Jobs

    Data AnalystData ScientistAI ProgrammerAI Systems EngineerAI Software Developer

    Student Testimonials

    TESTIMONIALS

    Students Feedback

    Continuous improvement is at the core of our mission, as we consistently seek to elevate the quality of the learning environment.

    People Love To Learn With Us

    90%

    Students successfully complete their courses.

    9/10

    Users reported higher learning outcomes.

    Recently Placed Students

    Artificial Intelligence Training FAQ's

    Boost Your Coding Ability: Essential Artificial Intelligence Training for Novices and Experienced

  • Artificial Intelligence Engineer
  • Machine Learning Engineer
  • Data Scientist
  • Natural Language Processing (NLP) Engineer
  • Computer Vision Engineer
  • Robotics Engineer
  • AI Research Scientist
  • AI Consultant
  • AI Product Manager
  • Data Analyst
  • Business Intelligence Analyst
  • AI Ethics Specialist
  • AI Architect
  • AI Project Manager
  • AI Trainer
  • Feature selection is crucial in Artificial Intelligence because it helps improve model performance, interpretability, and efficiency by selecting the most relevant features or variables from the dataset. Additionally, feature selection aids in reducing computational resources, speeding up training and inference processes, and enhancing model scalability. Overall, proper feature selection contributes to the development of more accurate, robust, and interpretable AI models.

  • Data quality and quantity
  • Model interpretability
  • Ethical and societal concerns
  • Scalability and performance
  • Continuous learning
  • Deployment and integration
  • Data splitting
  • Cross-validation
  • Performance metrics
  • Hyperparameter tuning
  • Bias-variance tradeoff
  • Ensembling
  • Version control
  • Documentation
  • Code organization
  • Environment management
  • Automation
  • Testing
  • Reproducible pipelines
  • Data limitations
  • Interpretability
  • Computational resources
  • Ethical concerns
  • Transfer learning limitations
  • Adversarial attacks
  • Regulatory and legal issues
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  • Set clear goals
  • Stay organized
  • Engage actively
  • Practice regularly
  • Seek support
  • Stay motivated
  • Review and reflect
  • Flexibility
  • Accessibility
  • Variety of options
  • Cost-effectiveness
  • Interactive learning
  • Global community
  • Convenience
  • Accessibility
  • Flexibility
  • Self-paced learning
  • Hands-on experience
  • Certification
  • Course content
  • Instructor expertise
  • Learning format
  • Reviews and testimonials
  • Accreditation and certification
  • Cost and value
  • Yes, online Artificial Intelligence training can be effective for beginners, as it offers several advantages such as flexibility, accessibility, and self-paced learning. Beginners can start with introductory courses that cover foundational concepts, basic techniques, and hands-on exercises to build a solid understanding of AI principles and methodologies. Online platforms often provide resources, tutorials, and support to help beginners navigate the learning journey and develop essential skills in Artificial Intelligence.

    Yes, many online Artificial Intelligence courses offer participants certificates upon completion of the course. These certificates serve as evidence of your participation, completion, and proficiency in the course content and can be showcased on your resume, LinkedIn profile, or portfolio to demonstrate your skills and qualifications to employers and peers. However, the availability of certificates may vary depending on the course provider, program format, and completion requirements. It's advisable to check the course details and certification criteria before enrolling in an online Artificial Intelligence course.

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    Training in artificial intelligence (AI) for organizations and their staff to learn about AI principles, methods, resources, and applications pertinent to their sector or organizational requirements is known as corporate AI training. This might take the form of seminars, workshops, or courses.

  • Enhanced employee skills
  • Increased innovation
  • Improved decision-making
  • Enhanced productivity
  • Stay competitive
  • AI is transforming industries
  • Addressing skill gaps
  • Driving innovation
  • Mitigating risks
  • Introductory courses
  • Specialized workshops
  • Hands-on projects
  • Certification programs
  • In-person workshops or seminars conducted by AI experts or trainers.
  • Online courses or e-learning platforms offering self-paced modules and interactive tutorials.
  • Blended learning approaches combining online resources with in-person sessions for a comprehensive learning experience.
  • Customized training programs tailored to the specific needs and objectives of the organization.
  • Working on real-world datasets
  • Developing AI models
  • Project-based learning
  • Experimentation and evaluation
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