Data Analysis and Visualization using Python for Machine Learning

Participants will learn how to use Python to perform data analysis and visualization for machine learning. At the same time, the participants will learn how to use these tools to load, manipulate, and analyze data and visualize data using libraries like Matplotlib and Seaborn. It also explains data analysis like regression, clustering and classification using scikit-learn. By the end of this course, the participants will have a solid understanding of Python for data analysis and visualization in machine learning and be able to apply these skills to real-world data science problems and be well-prepared to continue their learning and development in this exciting field.

Introduction to Numpy 1. Creating arrays 2. Using arrays and scalars 3. Indexing Arrays 4. Array Transposition 5. Universal Array Function 6. Array Processing 7. Array Input and Output Introduction to Pandas 1. Data Frames 2. Index objects 3. Selecting Entries 4. Data Alignment 5. Rank and Sort 6. Summary Statistics 7. Missing Data 8. Index Hierarchy Data Visualization Using Matplotlib 1. Matplotlib Overview 2. Bar Plots 3. Line Plots 4. Scatter Plots 5. Histograms 6. Box Plots and Violin Plots 7. Style and Presentation Data Visualization Using Seaborn 1. Seaborn Overview 2. Categorical Plots 3. Relational Plots 4. Distribution Plots 5. Regression Plots 6. Matrix Plots 7. Multi Plot Grids 8. Style and Presentation Data Analysis Using Machine Learning with SciKit Learn 1. Linear Regression 2. Logistic Regression 3. k Nearest Neighbor 4. Support Vector Machines 5. Decision Trees and Random Forests Hands-on with examples from different fields 1. Case Study 1 2. Case Study 2