A Guide to Master Artificial Intelligence Course in Delhi

How Mastering Artificial Intelligence Course Helps to Grow?

• Gain In-Demand Skills: Courses cover essential topics like machine learning, data preparation, deep learning, and open CV, equipping participants with the skills required to tackle real-world AI challenges.

• Certification and Industry Exposure: Certified and industry-experienced trainers provide insights into the latest trends and best practices, enhancing your credibility and employability in the competitive job market.

• Career Advancement: From beginner to expert-level training, courses serve to individuals at different proficiency levels, empowering them to transition into coveted roles such as machine learning engineer, data scientist, AI researcher, and more.
  
  

Course Curriculum

• Machine Learning: Fundamentals of machine learning algorithms and techniques.

• Data Preparation: Preprocessing and cleaning of data for analysis and modeling.

• Deep Learning: Advanced neural network architectures and algorithms.

• OpenCV: Computer vision library for image and video analysis.
  
  

Course Highlights

• Project-Based Training: Hands-on projects provide practical experience and foster problem-solving skills.

• Certified Trainers: Industry experts offer guidance and mentorship throughout the course.

• Best-in-Class Curriculum: Comprehensive training curriculum designed to meet industry standards.

• Online & Offline Modes: Flexibility to choose between online and offline training modes based on convenience.
  
  

Tools Covered

Tools To Be Covered In AI Course 

Tools To Be Covered In AI Course 

• Python: It's extensively used in AI and machine learning for tasks such as data manipulation, algorithm implementation, and building AI models due to its rich ecosystem of libraries.

• Pandas: It provides data structures like Data Frame, which make it easier to handle structured data, perform data preprocessing tasks, and manipulate datasets efficiently.

• NumPy: It provides support for multidimensional arrays, mathematical functions, linear algebra operations, random number generation, and integration with C/C++ and Fortran code.

• Scikit Learn: It offers a simple and efficient toolset for data mining and data analysis tasks, including classification, regression, clustering, dimensionality reduction, and model evaluation.

• SQL: In the context of AI, knowledge of SQL is essential for retrieving and managing data stored in databases, which is often required for training machine learning models.

• TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It's widely used for building and training deep learning models, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more.

• SciPy: SciPy is a scientific computing library for Python. It provides modules for optimization, integration, interpolation, linear algebra, signal processing, and other numerical routines, complementing NumPy's functionality and extending Python's capabilities for scientific computing.

• OpenCV: OpenCV (Open Source Computer Vision Library) is a popular library for computer vision tasks such as image and video analysis, object detection, feature extraction, and image processing. It offers a wide range of functions and algorithms for building computer vision applications.

• Git: It enables collaborative development, version control, branching, merging, and code management, facilitating teamwork and codebase management in AI projects.
  
  

Job Roles

• Machine Learning Engineer

• Data Scientist

• AI Researcher

• NLP Engineer

• Computer Vision Engineer

• Robotics Engineer

• AI Consultant

Conclusion