The course is typically structured around the major pillars of Generative AI and Data Science: 1. Python Programming & Data Analysis Build a strong foundation in Python programming and learn how to collect, clean, process, and analyze data using industry-standard libraries. Gain hands-on experience with real-world datasets and data manipulation techniques. Key Topics: Python, NumPy, Pandas, Jupyter Notebook, Google Colab, Data Cleaning, Data Wrangling, and Exploratory Data Analysis (EDA). 2. Database Management & Data Visualization Learn to retrieve, manage, and visualize data using SQL and powerful Business Intelligence tools. Transform raw data into meaningful insights through interactive dashboards and visual reports. Key Topics: MySQL, SQL Queries, Power BI, Tableau, Matplotlib, Seaborn, Data Visualization, Dashboard Development, and Business Reporting. 3. Machine Learning & Predictive Analytics Understand machine learning algorithms and build predictive models using industry-leading frameworks. Learn how to evaluate model performance and solve real-world business problems through data-driven decision-making. Key Topics: Scikit-learn, XGBoost, LightGBM, Regression, Classification, Clustering, Model Evaluation, Feature Engineering, and Predictive Analytics. 4. Deep Learning & Generative AI Master Deep Learning concepts and build Generative AI applications using Large Language Models (LLMs). Learn Prompt Engineering, AI model integration, and AI application development using modern frameworks. Key Topics: TensorFlow, PyTorch, Hugging Face, OpenAI API, LangChain, Ollama, LM Studio, Prompt Engineering, LLMs, AI Agents, and Generative AI Applications. 5. AI Deployment, MLOps & Industry Projects Learn to deploy AI and Machine Learning models, track experiments, collaborate using GitHub, and work on real-world industry projects. Gain practical experience with AI workflows from development to production. Key Topics: MLflow, Apache Spark, GitHub, VS Code, Jira, NotebookLM, Model Deployment, MLOps, AI Project Development, and Industry Case Studies. Skills You Will Gain Python Programming, SQL, Data Analysis, Data Visualization, Statistics, Machine Learning, Deep Learning, Generative AI, Prompt Engineering, Large Language Models (LLMs), OpenAI API Integration, LangChain, AI Application Development, MLOps, Power BI, Tableau, Predictive Analytics, Real-World AI Projects, and Industry Case Studies. Potential Career Paths Data Analyst, Data Scientist, AI Engineer, Machine Learning Engineer, Generative AI Engineer, Prompt Engineer, Business Intelligence Analyst, AI Application Developer, NLP Engineer, MLOps Engineer, Research Analyst, and AI Solutions Architect.

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