Advanced Data Analytics with ML & AI Overview
Course Overview
Immerse yourself in the cutting-edge field of Advanced Data Analytics! Our intensive course seamlessly integrates Machine Learning and Artificial Intelligence into data analysis. Perfect for data analysts proficient in Python, you’ll delve into machine learning fundamentals, explore AI integration, and gain hands-on experience in neural networks. Elevate your skills and empower your career in the data-driven future.
MODULES
Module 1: Introduction to Data Science with Pandas
- Overview of Data Science workflow and its components
- Basics of Pandas: Series, DataFrame, data manipulation
Module 2: Data Cleaning and Preprocessing
- Data cleaning techniques using Pandas
- Handling missing values and outliers
- Transforming and reshaping data with Pandas
Module 3: Exploratory Data Analysis (EDA) with Pandas and Visualization
- Advanced data exploration using Pandas
- Visualization techniques with Matplotlib and Seaborn
- Statistical analysis of data using Pandas
Module 4: Introduction to Machine Learning with Scikit-learn and Pandas
- Basics of Scikit-learn library for machine learning
- Data preprocessing for ML models using Pandas
Module 5: Supervised Learning with Pandas and Scikit-learn
- Introduction to Supervised Learning
- Linear Regression with Pandas and Scikit-learn
- Logistic Regression with Pandas and Scikit-learn
Module 6: Supervised Learning Continued
- Decision Trees and Random Forests with Pandas and Scikit-learn
- Support Vector Machines (SVM) with Pandas and Scikit-learn
Module 7: Advanced Data Manipulation with Pandas
- Grouping and aggregation operations with Pandas
- Merging and joining datasets using Pandas
- Case studies and practical exercises with Ensemble Methods: Gradient Boosting, XGBoost
Module 8: Unsupervised Learning with Pandas and Scikit-learn
- K-Means Clustering with Pandas and Scikit-learn
- Clustering techniques using Pandas and Scikit-learn: K-Means, DBSCAN
- Dimensionality reduction methods with Pandas and Scikit-learn: PCA, t-SNE
- Applications of unsupervised learning in real-world scenarios
Module 9: Feature Scaling and Model Selection in AI-based application with Pandas and Scikit-learn
- Feature scaling techniques with Pandas
- Hyperparameter tuning and model selection using Scikit-learn: GridSearchCV, RandomizedSearchCV
- Implementing Neural Networks with Pandas and Scikit-learn (basics)
Module 10: Application and Integration
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- Integrating learned concepts in a comprehensive project
- Chatbot Development for Customer Support
- Create an AI-powered chatbot that handles customer queries by understanding natural language and providing solutions.
- Energy Consumption Forecasting
- Analyze historical energy usage data to forecast future consumption patterns, aiding in resource allocation.
- Predictive Maintenance for Industrial Equipment
- Use sensor data from machines to predict maintenance needs and prevent breakdowns.
- Recommendation System for E-commerce
- Build an AI-driven recommendation engine using customer behavior and purchase history data to suggest products.
- Final review and discussion of the course content
