Training on Python for Advanced Data Analysis and Machine Learning

Module 1: Python Fundamentals for Data Analysis

• Deep dive into NumPy: array operations, linear algebra, random number generation
• Pandas: advanced data manipulation, time series analysis, performance optimization

Module 2: Exploratory Data Analysis (EDA) and Feature Engineering

• In-depth EDA techniques: correlation analysis, hypothesis testing, outlier detection
• Feature selection, creation, and transformation for model building

Module 3: Statistical Modeling with Python

• Linear regression, logistic regression, and model evaluation
• Time series analysis: ARIMA, forecasting
• Hypothesis testing and statistical inference

Module 4: Machine Learning Foundations

• Supervised and unsupervised learning overview
• Model evaluation metrics and cross-validation
• Hyperparameter tuning and model selection

Module 5: Classification Algorithms

• Decision trees, random forests, support vector machines
• Model interpretation and explainability

Module 6: Clustering Algorithms

• K-means, hierarchical clustering, DBSCAN
• Cluster evaluation and visualization

Module 7: Natural Language Processing (NLP)

• Text preprocessing, tokenization, stemming, and lemmatization
• Sentiment analysis, text classification, and topic modeling

Module 8: Deep Learning with Python

• Introduction to neural networks and deep learning
• Building and training neural networks using TensorFlow/Keras
• Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)

Module 9: Big Data Processing with Python

• Introduction to Apache Spark and PySpark
• Distributed data processing and analysis
• Handling large datasets efficiently

Module 10: Data Visualization and Communication

• Advanced data visualization techniques with Plotly and Seaborn
• Interactive dashboards and storytelling
• Effective communication of data insights to stakeholders