Artificial Intelligence & Machine Learning

For Demo & Group Offer

Duration : 3 Months and 6 Months

Language : English/Kannada/Hindi/Urdu

Course Include:-

Generative AI
AI Essentials
Business Intelligence Tools
Machine Learning
Deep Learning
SQL
Low code programming

Call Now for No cost EMI

Unlock your potential without the financial strain! Embrace iPEC-Solutions flexible payment options, where you can split your fees into manageable installments.

JOB OPPORTUNITIES IN OUR COURSE

Machine Learning Engineer
Data Scientist
AI Research Scientist
Computer Vision Engineer
Natural Language Processing (NLP) Engineer
Big Data Analyst
Business Intelligence Analyst
AI Product Manager
Data Engineer
Robotics Engineer

Deep Learning Engineer
AI Ethics and Bias Analyst
AI Solutions Architect
AI Consultant
Predictive Analytics Specialist
Data Mining Specialist
AI Project Manager
AI Software Developer
IoT (Internet of Things) Data Scientist
Quantum Machine Learning Scientist

Business Analytics Manager
Statistical Analyst
Healthcare Data Analyst
Financial Data Scientist
Image Processing Engineer
Cloud AI Engineer
AI System Integration Specialist
Cybersecurity Data Scientist
Supply Chain Data Analyst
Social Media Data Scientist

Speech Recognition Engineer
Fraud Detection Analyst
A/B Testing Analyst
Sports Analytics Specialist
Behavioural Analyst
Geospatial Data Scientist
E-commerce Data Analyst
Marketing Analytics Manager
Energy Analytics Specialist
Augmented Reality (AR) Developer

Unlock Your Brilliance: Discover Our Exclusive Course Offerings

Comprehensive Project Showcase with Capstone Excellence.
Hands-On Practice Challenges.
Interactive Doubt Resolution Sessions.
Cutting-Edge, Industry-Aligned Curriculum.

Globally Acknowledged Certification.
Focused Module-Based Assignments.
Expert-Led Instructional Sessions.

Exclusive Interview Prep and Opportunities.
Vibrant Community Networking.
Responsive Email Assistance.

PRACTITIONER’S APPROACH TO ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

AI & ML Interactive Offline/ Online programs are intensive application oriented and based on real-world scenario. AI & ML foundation, master and professional, all are skill oriented, practical training program required for building various applications. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program. These courses are designed for both for freshers and experienced professionals from variety of backgrounds. No prior knowledge of statistics or modelling is assumed.

OBJECTIVES

Acquire advanced Analysis & Modelling
Stay Industry relevant and grow in your
Create AI/ML solutions for various business
Build and deploy production grade AI/ML

WHO SHOULD TAKE THIS COURSE?

Working professionals who intend to build their careers in the field of Data Science
Working professionals who intend to build their careers in the field of ML and AI
Professionals who are currently in the Big Data and data science domains
Professionals from quality and testing team
IT professionals in scripting and automation industry
Entrepreneurs
Fresh graduates and young professionals
Mid-level managers
Professionals working with Management Information System (MIS) and operations

BENEFITS OF THE COURSE

In-depth learning of AI and ML algorithms through practical examples
May build capability to implement tools built on AI platforms
The course enables you to learn and acquire the required skills in Python at analytics and program development
The course can help you create your own AI and ML solutions
Learn from the experienced professionals and industry experts

This specialization will help you to get a break into Artificial Intelligence and Machine Learning domain, with skills based on the most sought after tools and libraries, like Python, NumPy, Pandas, Scikit-Learn, NLTK, TextBlob, PyTorch, TensorFlow, Keras, etc. You will learn AI and Machine Learning starting from data handling, visualization, statistical modeling, machine learning and move towards advanced use of deep learning for AI-based applications like image processing, text data processing, chat-bots, time series, recommendation systems, machine translation, etc.

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly becoming popular among today’s organizations. AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Initially conceived as a technology that could mimic human intelligence, AI has evolved in ways that far exceed its original conception. With incredible advances made in data collection, processing and computation power, intelligent systems can now be deployed to take over a variety of tasks, enable connectivity and enhance productivity. An AI professional should feel at ease to build the algorithms necessary, work with various data sources and an innate ability to ask the right questions and ﬁnd the right answer. This module helps layout the canvas on which the rest of the modules are built.

Artificial Intelligence (AI):

AI refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. AI can be categorized into two types: Narrow AI (or Weak AI), which is designed for a specific task, and General AI (or Strong AI), which possesses the ability to perform any intellectual task that a human being can.

Machine Learning (ML):Machine learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming. ML relies on patterns and inference, allowing systems to improve their performance over time as they are exposed to more data. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Key Concepts and Techniques:Supervised Learning: Involves training a model on a labeled dataset, where the algorithm learns to map input data to corresponding output labels.

Unsupervised Learning: Involves working with unlabeled data, and the algorithm tries to find patterns and relationships within the data.

Reinforcement Learning: Involves training a model to make sequences of decisions by rewarding or penalizing the model based on the outcomes of those decisions.

Neural Networks: A key component in many ML models, especially deep learning. Neural networks are inspired by the structure of the human brain and consist of interconnected nodes (neurons) organized into layers.

Deep Learning: A sub field of ML that focuses on neural networks with multiple layers (deep neural networks). It has been particularly successful in tasks such as image and speech recognition.

Applications:AI and ML find applications in various domains, including:

Healthcare: Diagnosis, personalized medicine, and drug discovery.

Finance: Fraud detection, algorithmic trading, and customer service.

Retail: Customer recommendations, supply chain optimization, and inventory management.

Autonomous Vehicles: Self-driving cars that use AI and ML for navigation and decision-making.

Natural Language Processing (NLP): Understanding and generating human language, used in chat bots and language translation.

Challenges:Ethical Concerns: AI systems can inherit biases present in training data, raising ethical concerns.

Data Privacy: ML models often require large datasets, raising concerns about privacy and security.

Explainability: The “black box” nature of some AI models makes it challenging to understand their decision-making processes.

Natural Language Processing: NLP is a sub field of AI that focuses on the interaction between computers and human language. It involves tasks such as speech recognition, language translation, sentiment analysis, and chatbot development. NLP has made significant advancements, enabling machines to understand and generate human language more effectively.
Computer Vision:
Computer Vision is another key area within AI that involves teaching machines to interpret and make decisions based on visual data. Applications include image and video recognition, facial recognition, object detection, and autonomous vehicles.
Transfer Learning:
Transfer learning is a technique in machine learning where a model trained on one task is adapted for a different, but related, task. This helps leverage knowledge gained in one domain to improve performance in another, especially in scenarios where large labeled datasets are scarce.
Explainable AI (XAI):
The interpretability of AI models is crucial for gaining trust and understanding their decision-making processes. XAI focuses on developing models that can provide clear explanations for their predictions, making them more transparent and accountable.
Edge Computing in AI:
Edge computing involves processing data near the source of generation rather than relying on centralized cloud servers. In AI, edge computing is becoming more prominent, enabling devices like smartphones and IoT devices to perform AI tasks locally, reducing latency and improving efficiency.
AI in Cybersecurity:
AI is being employed to enhance cybersecurity by identifying and responding to threats in real-time. Machine learning algorithms can analyze patterns in network traffic to detect anomalies and potential security breaches, providing a proactive defence against cyberattacks.
AI Ethics and Bias Mitigation:
Addressing ethical concerns and biases in AI systems is crucial. Efforts are being made to develop ethical frameworks for AI development and deployment, and to implement techniques that mitigate biases present in training data.
Quantum Computing and AI:
The intersection of quantum computing and AI holds promise for solving complex problems at a scale that classical computers struggle with. Quantum machine learning aims to leverage the principles of quantum mechanics to enhance computational power for certain AI tasks.
AI and Creativity:
AI systems are increasingly being used in creative fields such as art, music, and literature. Generative models can create new content, and AI tools assist artists and creators in their work.
AI Governance and Regulations:
As AI technologies advance, there is a growing need for governance and regulations to ensure responsible and ethical use. Governments and organizations are working to establish guidelines and policies to manage the development and deployment of AI systems.

In the ever-evolving landscape of AI and ML, ongoing research and innovation continue to shape the future of these technologies, influencing how they are applied across diverse sectors and addressing the challenges they present.

Why IPEC

At iPEC, we pride ourselves on offering an offline learning experience that goes beyond the ordinary. Rooted in excellence, our methodology is tailored to provide students in India with the best possible education, enriched by the expertise of our experienced trainers.

Key Benefits of iPEC Learning Method Offline:

Engage in dynamic, face-to-face interactions with faculty and peers, fostering a collaborative learning environment. Our offline setup promotes active participation and meaningful discussions, enhancing the overall learning experience.

With smaller class sizes, our experienced faculty can provide individualized attention to students. This ensures a deeper understanding of the subject matter, allowing each student to progress at their own pace.

Immerse yourself in hands-on learning experiences facilitated by our knowledgeable faculty. From real-world case studies to practical applications, our offline method equips students with the skills needed to excel in their respective fields.

Build a strong professional network by connecting with classmates and experienced faculty members. Our offline setup encourages relationship-building, creating a supportive community that extends beyond the classroom.

Enjoy seamless access to a range of resources, including libraries, labs, and study spaces. Our offline learning environment provides the tools necessary for comprehensive understanding and research.

Our faculty members bring a wealth of knowledge and practical experience to the classroom. Hailing from diverse backgrounds, they not only possess academic expertise but also offer valuable insights gained from years of industry experience. The combination of their academic and practical knowledge creates a holistic learning environment that prepares students for real-world challenges.

Why iPEC for Offline Learning in India?

iPEC has a history of producing successful graduates who have excelled in their careers.

Our offline learning approach focuses not only on academic excellence but also on the holistic development of each student, nurturing well-rounded professionals.

Stay ahead in your chosen field with our industry-relevant curriculum, constantly updated to align with current trends and demands.

Artificial Intelligence & Machine Learning