Artificial Intelligence & Machine Learning (AI/ML)
40 HoursHybrid4.9
Explore the exciting world of Artificial Intelligence and Machine Learning through practical, industry-focused training.
About this course
Explore the exciting world of Artificial Intelligence and Machine Learning through practical, industry-focused training. Learn Python programming, machine learning algorithms, deep learning, Natural Language Processing, Computer Vision, Generative AI, and Large Language Models. Gain hands-on experience in building AI applications, chatbots, predictive models, and intelligent automation solutions. Learn model deployment, MLOps, cloud AI services, and real-world project development.
Curriculum & Topics
9 modules · 64 topics
- 1.Python Fundamentals, Advanced Programming & Object-Oriented Programming
- 2.NumPy, Pandas & Data Manipulation
- 3.Data Structures, Functions, Modules & Exception Handling
- 4.Data Cleaning, Preprocessing & Feature Engineering
- 5.Data Visualization Using Matplotlib, Seaborn & Plotly
- 6.Python Environments, Virtual Environments & Dependency Management
- 1.Descriptive Statistics, Probability & Statistical Distributions
- 2.Linear Algebra, Vectors, Matrices & Mathematical Operations
- 3.Differential Calculus, Gradients & Optimization Fundamentals
- 4.Exploratory Data Analysis (EDA) & Data Quality Assessment
- 5.Missing Values, Outlier Detection & Data Transformation
- 6.Feature Selection, Encoding, Scaling & Dimensionality Reduction
- 1.Supervised vs. Unsupervised Learning
- 2.Linear Regression, Logistic Regression & Regularization
- 3.Decision Trees, Random Forests & Ensemble Learning
- 4.Support Vector Machines (SVM), K-Nearest Neighbors (KNN) & Naive Bayes
- 5.Clustering Algorithms: K-Means, Hierarchical Clustering & DBSCAN
- 6.Model Evaluation, Cross-Validation, Hyperparameter Tuning & Model Selection
- 7.Classification, Regression & Clustering Use Cases
- 1.Artificial Neural Networks (ANN) & Perceptron Models
- 2.Forward Propagation, Backpropagation & Gradient Descent
- 3.TensorFlow, Keras & PyTorch Fundamentals
- 4.Convolutional Neural Networks (CNN) for Image Processing
- 5.Recurrent Neural Networks (RNN), LSTM & Sequence Modeling
- 6.Transfer Learning, Model Fine-Tuning & Performance Optimization
- 7.Deep Learning Applications in Computer Vision and Predictive Analytics
- 1.Natural Language Processing (NLP) Fundamentals
- 2.Text Cleaning, Tokenization, Stemming & Lemmatization
- 3.Text Vectorization: Bag of Words, TF-IDF & Word Embeddings
- 4.Sentiment Analysis, Text Classification & Information Extraction
- 5.Image Classification, Object Detection & Image Segmentation
- 6.OpenCV, YOLO & Transformer-Based Vision Models
- 7.Real-World NLP and Computer Vision Applications
- 1.Generative AI Fundamentals, Model Architectures & Use Cases
- 2.Large Language Models: GPT, Llama & Other Open-Weight Models
- 3.Transformer Architecture, Attention Mechanisms & Tokenization
- 4.Prompt Engineering, Structured Outputs & Model Evaluation
- 5.Embeddings, Vector Databases & Semantic Search
- 6.Retrieval-Augmented Generation (RAG) Architecture and Implementation
- 7.Fine-Tuning, Parameter-Efficient Fine-Tuning (PEFT) & LoRA Fundamentals
- 8.AI Agents, Tool Calling & Multi-Step Workflows
- 1.Scikit-learn, TensorFlow, PyTorch & Hugging Face Ecosystem
- 2.OpenAI API and Other LLM Provider Integrations
- 3.AI Application Development Using FastAPI or Flask
- 4.LangChain and LlamaIndex Fundamentals
- 5.Vector Database Integration and Knowledge Retrieval
- 6.Building AI Chatbots, Document Q&A Systems & Intelligent Assistants
- 7.Model Serving, API Integration, Authentication & Error Handling
- 1.ML Lifecycle Management and Experiment Tracking
- 2.Git, GitHub, Version Control & Collaborative Development
- 3.Model Serialization, Packaging & Deployment
- 4.Docker, Containers & Deployment Environments
- 5.CI/CD Pipelines for Machine Learning Applications
- 6.Model Monitoring, Data Drift, Model Drift & Performance Tracking
- 7.Cloud AI Services: Microsoft Azure AI, AWS AI/ML & Google Cloud AI
- 8.Model Versioning, Reproducibility, Scalability & Production Monitoring
- 1.End-to-End Machine Learning Project Development
- 2.Predictive Analytics, Recommendation Systems & Forecasting
- 3.Computer Vision, NLP & Generative AI Project Implementation
- 4.Enterprise AI Automation and Business Process Applications
- 5.Model Explainability, Bias Assessment & Responsible AI
- 6.AI Security, Data Privacy & Model Risk Management
- 7.Model Performance Optimization, Inference Cost & Resource Management
- 8.Capstone Project: Development, Deployment & Technical Documentation
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