Artificial Intelligence (AI)
Internship course in artificial intelligence (AI) is an excellent opportunity for students to gain practical experience in the field. The curriculum for an AI internship should combine theoretical knowledge with hands-on projects and real-world applications. Course curriculum for an AI internship program:
- 4.2
- 79,182 Students
- Placement Assistance
- 6 Weeks, 1 hr/day (flexible timings)
Key Course Features
8 Weeks, 1 hr/day (flexible timings)
Lesson Videos: 40
Internship Certification
Placement Assistance
Affordable fee and EMI facility
Live Classes and Self-Paced Learning modes
Industry live projects under the supervision of 15+ experienced trainer
Course material, quizzes, assignments to assist in learning
Why to take Artificial Intelligence (AI) Course:
- Basic understanding of AI concepts and terminology.
- Exposure to various machine learning and deep learning techniques.
- Practical experience with building and evaluating AI models.
- Awareness of ethical considerations in AI.
- Insight into the future of AI and potential career paths.
- The importance of hands-on projects to reinforce learning.
Industry recognized Artificial Intelligence (AI) certification
- Upskill Intern certification is trusted by 10,000+ companies in industry for hiring
- Get physical copy of certificate to your address
Course Curriculum
- Definition of AI and its significance.
- Historical developments in AI.
- Overview of AI applications in various industries.
- Ethical considerations in AI.
- Introduction to machine learning.
- Types of machine learning: supervised, unsupervised, reinforcement learning.
- Key machine learning terminology.
- Hands-on: Basic linear regression example.
- Logistic regression for classification.
- Decision trees and random forests.
- Model evaluation and metrics (accuracy, precision, recall).
- Hands-on: Building a simple classification model.
- Clustering techniques (K-Means, hierarchical).
- Dimensionality reduction (PCA).
- Hands-on: Clustering and dimensionality reduction exercises.
- Introduction to neural networks.
- Activation functions (ReLU, Sigmoid).
- Building a simple feedforward neural network.
- Hands-on: Implementing a neural network in Python.
- Basics of CNNs for image processing.
- Convolution and pooling layers.
- Hands-on: Building a CNN for image classification.
- Introduction to NLP.
- Text preprocessing techniques.
- Word embeddings (Word2Vec, GloVe).
- Hands-on: NLP tasks like sentiment analysis.
- Introduction to reinforcement learning (RL).
- Markov Decision Processes (MDPs).
- Q-learning algorithm.
- Hands-on: Building a basic RL agent.
- AI ethics and responsible AI.
- Bias and fairness in AI.
- Future trends in AI (quantum computing, AI in healthcare).
- Career opportunities and further learning resources.
- Students work on a small AI project or complete a practical exercise.
- Presentation of projects and insights.
- Course review and resources for further learning.
About Trainer
Sanjay has 12+ years of rich experience in physical design domain. He worked on complete RTL to GDSII flow of cutting edge technology nodes 7nm, 14nm, 28nm from block level and top level. His work involves floorplanning, power planning, clock tree sysnthesis (CTS), DFM, RC extraction, STA and signal integrity (crosstalk) analysis, Formal and physical verification of complex chip sets such as Modem, Memories, PCIe, PHY.
He is also managing an ASIC Backed Design team from RTL to tapeout/Signoff including managing tools flows and design issues. He is our expert for giving hands-on tool and theory knowledge to students.
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