AI Engineering Course
AI engineering is a dynamic field focused on the development and deployment of artificial intelligence (AI) solutions. It involves designing, building, and optimizing systems that can mimic human intelligence, learn from data, and make informed decisions. As the demand for AI-powered technologies continues to grow, AI engineers play a crucial role in bringing these intelligent systems to life.
The process of AI engineering starts by understanding what a business needs and finding ways to use AI to solve problems. AI engineers work closely with the people involved to set project goals, gather and prepare the right data, and choose the best algorithms and models. They use programming languages like Python and specialized AI tools like TensorFlow or PyTorch to build and train AI models.
These engineers possess a combination of technical skills, including programming, machine learning, data analysis, and software engineering. It also requires a solid understanding of ethical considerations and responsible AI practices.
Python and AI
Python is a versatile and widely-used programming language known for its simplicity and readability. Created by Guido van Rossum and first released in 1991, Python emphasizes code readability, making it easier to write and understand compared to other programming languages. It has gained popularity for its extensive range of libraries and frameworks, which provide ready-to-use tools for various purposes, such as data analysis, web development, artificial intelligence, and more.
One of the key strengths of Python is its ease of use and beginner-friendly nature. The language has a clear and intuitive syntax that reduces the learning curve for newcomers. Python's readability encourages clean and concise code, improving both development speed and code maintainability. It has become the go-to programming language for AI due to its simplicity, versatility, and extensive ecosystem of libraries and frameworks specifically designed for AI development.
Python AI refers to the application of artificial intelligence techniques and algorithms using the Python programming language. It has become a popular choice for developing AI applications due to its simplicity, readability, and extensive libraries and frameworks available for AI development. It also provides a wide range of libraries and tools that enable developers to implement AI algorithms efficiently.
What Are The Career Options You Can Pursue After Studying AI Engineering?
AI Engineer/Developer:AI engineers/developers design, develop, and implement AI systems and solutions by working with machine learning algorithms, deep learning models, and AI frameworks.
- Machine Learning Engineer: Machine learning engineers work on data preprocessing, feature engineering, model training, and optimization to develop predictive models that can make data-driven decisions.
- Data Scientist: Data scientists utilize AI techniques, statistical models, and machine learning algorithms to solve business problems, make predictions, and drive data-driven decision-making.
- Research Scientist:Research scientists conduct innovative research and develop new algorithms and methodologies.
- Robotics Engineer: Robotics engineer work on designing, developing, and programming robotic systems that incorporate AI technologies.
- AI Product Manager:AI product managers collaborate with cross-functional teams to define product vision, prioritize features, and ensure alignment with customer needs and business objectives.
These are just a few examples of the many roles available in the field of AI engineering. The demand for skilled professionals in this area continues to grow as organizations recognize the transformative potential of AI technologies.
AI Engineering Salary
Here is a list of popular AI engineering job profiles and their expected salaries in India based on recruitment data from major multinational companies:
|Rs. 5-7 Lakhs
|Rs. 8-10 Lakhs
|Machine Learning Engineer
|Rs. 7-7 Lakhs
|AI Research Scientist
|Rs. 7-9 lakhs
|Rs. 2-5 lakhs
|AI Product Manager
|Rs. 15-21 lakhs
Cyber security professionals work across these areas to develop comprehensive strategies and implement security measures to safeguard against cyber threats.
Why Should You Learn AI Engineering?
Learning AI engineering offers numerous benefits and exciting opportunities in today's rapidly evolving technological landscape. Here are some compelling reasons why you should consider learning it:
High Demand:AI is in high demand across numerous industries. Organizations are increasingly integrating AI technologies to gain a competitive edge, improve operational efficiency, and deliver innovative solutions. By learning AI engineering, you position yourself to take advantage of a growing job market with abundant career opportunities.
- Cutting-Edge Technology:AI is at the forefront of technological advancements. Learning AI engineering allows you to work with state-of-the-art tools, frameworks, and algorithms that drive intelligent systems. You get to be part of creating innovative solutions that can revolutionize industries and positively impact people's lives.
- Problem-Solving: It focuses on solving complex problems using data-driven approaches. You'll learn how to leverage data, build models, and develop algorithms to analyze patterns, make predictions, and derive meaningful insights. This skill set enables you to tackle a wide range of real-world challenges across different industries.
- Cross-Disciplinary Skills:AI engineering is a multidisciplinary field that combines aspects of computer science, mathematics, statistics, and domain-specific knowledge. By learning it, you acquire a versatile skill set that can be applied across industries, making you an attractive candidate for diverse roles.
- Future-Proof Career:AI is poised to shape the future. By gaining expertise in AI engineering, you future-proof your career by aligning yourself with an industry that will continue to grow and evolve. The skills and knowledge you acquire can adapt to emerging technologies and trends, ensuring your professional relevance in the long run.
- Societal Impact: AI has the potential to drive positive change and address societal challenges. Learning AI engineering allows you to contribute to solving critical problems in areas such as healthcare, sustainability, education, and more. You can make a meaningful impact by developing AI-driven solutions that benefit individuals and society as a whole.
The course curriculum provides a comprehensive and cutting-edge approach to the field of Artificial Intelligence (AI). Designed to meet the growing demand for skilled AI professionals, this curriculum covers a wide range of topics to equip students with the necessary knowledge and skills. The curriculum is as follows:
Orientation and introduction to the program
- Familiarization with the internship structure and expectations
- Familiarization with BharatML-A custom-built ChatGPT bot for teaching programming in Malayalam, with inbuilt Python IDE
- Overview of Python programming language and its applications in AI
- Understanding Python syntax, variables, and data types
- Control flow statements: if, else, loops
- Functions and modules in Python
- Working with data structures: lists, tuples, dictionaries, and set
- File I/O operations in Python
- Introduction to NumPy and its Applications in scientific computing
- Introduction to AI and Machine Learning
- Overview of key concepts: supervised learning, unsupervised learning, and reinforcement learning
- Hands-on exercises and projects using Python for basic ML tasks
- Introduction to Pandas for data manipulation and analysis
- Data preprocessing and cleaning techniques
- Exploratory data analysis using Python libraries
- Introduction to scikit-learn for machine learning tasks
- Supervised learning algorithms: regression, classification, and ensemble methods
- Model evaluation and validation techniques
- Unsupervised learning algorithms: clustering and dimensionality reduction
- Deep learning basics: neural networks, activation functions, and backpropagation
- Introduction to Keras and TensorFlow libraries for deep learning
- Advanced deep learning concepts: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models
- Transfer learning and fine-tuning pre-trained models
- Hands-on projects and assignments applying deep learning techniques, including image classification and object detection using CNNs
- Introduction to real-world AI applications
- Exploring computer vision projects: image classification, object detection, and instance segmentation
- Hands-on projects using Google Colab and popular libraries like OpenCV and TensorFlow Object Detection API
- Introduction to Natural Language Processing (NLP) and chatbot development
- Building language models using recurrent neural networks (RNNs) and LSTM cells
- Introduction to chatbot architectures and frameworks like Seq2Seq and Transformers
- Introduction to ChatGPT and its architecture
- Understanding the mathematics behind transformers and attention mechanisms
- Hands-on exercises and projects to build a simple chatbot using ChatGPT-like techniques
- Final project presentation and demonstration
- Feedback and evaluation from industry experts
- Certification and completion of the internship program
Introduction to Python and AI Fundamentals
Advanced Python and AI Techniques
Industrial Projects and Certification
- The course curriculum covers Python programming, AI fundamentals, machine learning, deep learning, computer vision projects, and chatbot development. Students will learn Python syntax, data structures, control flow statements, and file I/O operations. They will explore supervised and unsupervised learning, reinforcement learning, and advanced topics such as Pandas for data manipulation, scikit-learn for machine learning, and deep learning with Keras and TensorFlow. Hands-on projects include image classification, object detection, and chatbot development. The curriculum concludes with real-world AI applications, a final project presentation, and certification.
- While a strong background in computer science or programming is beneficial, it is not mandatory. Basic knowledge of programming concepts and mathematics, such as linear algebra and calculus, can be helpful. Additionally, having an understanding of statistics and data analysis is advantageous in the field of AI Engineering.
- The course lasts for 2 months, with classes held 5 days a week, and each class lasting for 2 hours.
- The fee for this course is ₹15,000. This amount covers the cost of instruction, course materials, and any applicable administrative fees.
- Yes, it is possible to take the AI Engineering course without a computer science or programming background. However, having some foundational knowledge in programming and computer science concepts can make it easier to grasp AI Engineering concepts and methodologies.
Duration: 2 Months
Schedule: 5days a week, 2hours/day
Course Fee: 15000₹