AI for Data Analysis
( 25 Modules )

Module #1
Introduction to AI for Data Analysis
Overview of the course and importance of AI in data analysis

Module #2
Foundations of Machine Learning
Introduction to machine learning, types of machine learning, and key concepts

Module #3
Python for Data Analysis
Introduction to Python and its libraries for data analysis (Pandas, NumPy, etc.)

Module #4
Data Preprocessing for AI
Importance and techniques for data preprocessing, handling missing values, and data normalization

Module #5
Supervised Learning
Introduction to supervised learning, regression, and classification

Module #6
Linear Regression
In-depth explanation of linear regression, simple and multiple regression

Module #7
Logistic Regression
In-depth explanation of logistic regression, binary and multi-class classification

Module #8
Decision Trees and Random Forests
Introduction to decision trees, random forests, and ensemble learning

Module #9
Support Vector Machines (SVMs)
Introduction to SVMs, kernel methods, and soft margin SVMs

Module #10
Unsupervised Learning
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #11
K-Means Clustering
In-depth explanation of k-means clustering, algorithms, and applications

Module #12
Hierarchical Clustering
In-depth explanation of hierarchical clustering, advantages, and disadvantages

Module #13
Principal Component Analysis (PCA)
In-depth explanation of PCA, applications, and dimensionality reduction

Module #14
t-Distributed Stochastic Neighbor Embedding (t-SNE)
In-depth explanation of t-SNE, applications, and dimensionality reduction

Module #15
Deep Learning for Data Analysis
Introduction to deep learning, neural networks, and convolutional neural networks

Module #16
Natural Language Processing (NLP) for Text Analysis
Introduction to NLP, text preprocessing, and sentiment analysis

Module #17
Image Analysis with Computer Vision
Introduction to computer vision, image preprocessing, and object detection

Module #18
Time Series Analysis with AI
Introduction to time series analysis, forecasting, and prophet library

Module #19
Big Data Analytics with AI
Introduction to big data analytics, Hadoop, and Spark

Module #20
AI for Data Visualization
Introduction to data visualization, dashboard creation, and Tableau

Module #21
Model Evaluation and Selection
Importance of model evaluation, metrics, and selection techniques

Module #22
Model Deployment and Monitoring
Introduction to model deployment, API creation, and model monitoring

Module #23
AI Ethics and Bias
Importance of AI ethics, bias detection, and fairness in AI models

Module #24
Case Studies in AI for Data Analysis
Real-world case studies and applications of AI in data analysis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in AI for Data Analysis career

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AI for Data Analysis ( 25 Modules )

AI for Data Analysis
( 25 Modules )