Softlogic Systems Artificial Intelligence and Machine Learning Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Artificial Intelligence and Machine Learning syllabus covers Python programming for AI, supervised and unsupervised learning, neural networks, natural language processing, and hands-on AI/ML projects. Our Artificial Intelligence and Machine Learning Course Content helps you learn Artificial Intelligence and Machine Learning step by Step with real-time projects and Interview Preparations.
Artificial Intelligence and Machine Learning Syllabus
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Syllabus for The Artificial Intelligence and Machine Learning Syllabus Course
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Introduction to Artificial Intelligence and Machine Learning
- Basics of AI and ML
- History and evolution of AI
- Types of AI (Narrow AI, General AI, Superintelligent AI)
- AI vs Machine Learning vs Deep Learning
- Applications of AI and ML
Python for AI and ML
- Python basics and libraries (NumPy, Pandas, Matplotlib)
- Data Preprocessing and Data Wrangling
- Exploratory Data Analysis (EDA)
- Data visualization techniques
Supervised Learning
- Linear Regression and Logistic Regression
- Classification Algorithms (K-Nearest Neighbors, Support Vector Machines)
- Decision Trees and Random Forest
- Model evaluation techniques (Cross-validation, ROC curve)
Unsupervised Learning
- Clustering techniques (K-Means, Hierarchical Clustering)
- Principal Component Analysis (PCA)
- Anomaly detection
- Dimensionality reduction
Deep Learning
- Introduction to Neural Networks
- Backpropagation and optimization
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
- Autoencoders and Generative Adversarial Networks (GANs)
Natural Language Processing (NLP)
- Text preprocessing techniques
- Tokenization, Lemmatization, and Stemming
- Word embeddings (Word2Vec, GloVe)
- Sentiment Analysis and Text Classification
- Advanced NLP models (Transformers, BERT)
Reinforcement Learning
- Basics of Reinforcement Learning
- Markov Decision Processes
- Q-Learning and Deep Q-Networks (DQN)
- Policy Gradient Methods
AI in Real-World Applications
- AI in healthcare, finance, and retail
- Autonomous vehicles and robotics
- AI in cybersecurity
- AI in social media and content recommendations
Model Deployment and Cloud Computing
- Model deployment techniques
- Cloud platforms for AI and ML (AWS, Google Cloud, Azure)
- API integration and model serving
- Monitoring and maintaining models
Ethics in AI and ML
- Ethical considerations in AI and ML
- Bias in machine learning models
- Fairness and transparency in AI
- Responsible AI and data privacy
Capstone Project
- Real-world project to implement AI and ML techniques learned
- Working with industry datasets
- Presenting the project and results
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