Looking to master Artificial Intelligence and Machine Learning? This updated AI and ML syllabus provides a complete roadmap to help you build industry-ready skills. From foundational Python programming and data pre-processing to cutting-edge topics like neural networks, deep learning, and natural language processing, this guide covers it all.
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
1
- 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
2
- Python basics and libraries (NumPy, Pandas, Matplotlib)
- Data Preprocessing and Data Wrangling
- Exploratory Data Analysis (EDA)
- Data visualization techniques
Supervised Learning
3
- 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
4
- Clustering techniques (K-Means, Hierarchical Clustering)
- Principal Component Analysis (PCA)
- Anomaly detection
- Dimensionality reduction
Deep Learning
5
- 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)
6
- Text preprocessing techniques
- Tokenization, Lemmatization, and Stemming
- Word embeddings (Word2Vec, GloVe)
- Sentiment Analysis and Text Classification
- Advanced NLP models (Transformers, BERT)
Reinforcement Learning
7
- Basics of Reinforcement Learning
- Markov Decision Processes
- Q-Learning and Deep Q-Networks (DQN)
- Policy Gradient Methods
AI in Real-World Applications
8
- 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
9
- 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
10
- Ethical considerations in AI and ML
- Bias in machine learning models
- Fairness and transparency in AI
- Responsible AI and data privacy
Capstone Project
11
- Real-world project to implement AI and ML techniques learned
- Working with industry datasets
- Presenting the project and results
Breakdown of Artificial Intelligence and Machine Learning Syllabus Course Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
June 2025
Week days
(Mon-Fri)
Online/Offline
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
Course Fee
June 2025
Week ends
(Sat-Sun)
Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
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