Artificial Intelligence/Machine Learning

What is AI/ML?

• The term "artificial intelligence" describes the progress made in creating computer systems capable of carrying out tasks that normally require human intelligence.
• Machine Learning is a branch of artificial intelligence that focuses on creating statistical models and algorithms that let computers learn from experience and become more proficient at a given task without needing to be explicitly programmed.

Why learn AI/ML?

• The demand for AI/ML professionals is steadily increasing across industries.
• AI/ML offers a wide range of career opportunities, including roles such as data scientist, machine learning engineer, AI researcher, and more.
• Many companies are integrating AI/ML into their products and services to gain a competitive edge, leading to a high demand for skilled individuals who can develop and deploy these technologies.

What do we Provide?

• We provides a robust foundation in core concepts, mathematics,and algorithms essential for machine learning applications By mastering feature engineering, model validation, deep learning, NLP, reinforcement learning, and case studies, students develop expertise in implementing diverse AI/ML techniques, preparing them to tackle real-world challenges effectively.

Syllabus

Module 1: Introduction to AI/ML +

• Introduction to AI and ML
• Application of AI and ML
• History and Evolution of AI/ML
• Basics of Python Programming

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Module 2: Mathematics for ML +

• Linear Algebra
• Calculus
• Probability and Statistics

Module 3: Machine Learning Basics +

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Model representation and Evaluation
• Basic Application of CSS
• Overfitting
• Underfitting
• Bias-Variance Tradeoff

Module 4: Algorithms and Models +

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines (SVM)
• JClustering algorithms
• Neural networks
• Deep Learning

Module 5: Feature Engineering and Selection +

• Data preprocessing and Cleaning
• Feature scaling and Normalization
• Feature Selection Techniques

Module 6: Model Evaluation and Validation +

• Cross-Validation
• Evaluation Metrics
• Accuracy
• Precision
• Recall
• F1 Score
• ROC curves

Module 7: Deep Learning +

• Introduction to Neural network architectures
• CNNs
• RNNs
• LSTM
• Transfer Learnings
• Optimization Techniques

Module 8: Natural Language Processing +

• Text Processing
• Word Embeddings
• Sentiment Analysis
• Named Entity Recognition (NER)
• Language Modeling

Module 9: Reinforcement Learning +

• Markov Decision Processes (MDP)
• Q-learning
• Policy Gradients

Module 10: Case Studies +

• Image Recognition
• Recommendation Systems
• Autonomous Vehicles
• Healthcare Applications
• Finance and Trading

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InfoStack Software Development, Training and Research Center, Solapur