Machine Learning Labs

Experience the power of hands-on learning in Machine Learning Labs. This interactive course offers engaging lessons and immersive labs where you’ll gain practical experience in performing various machine-learning tasks. From working with Pandas DataFrames to exploring visualization libraries and popular machine learning libraries like Scikit-learn, you’ll develop the skills needed to excel in the dynamic field of machine learning.

Lessons

9+ Lessons |

TestPrep

 

Hand on lab

25+ LiveLab | 25+ Video tutorials | 27+ Minutes

Lessons 1: Pandas

• About DataFrames
• Creating DataFrames
• Interacting with DataFrame Data
• Manipulating DataFrames
• Manipulating Data
• Interactive Display
• Summary

Lessons 2: NumPy

• Installing and Importing NumPy
• Creating Arrays
• Indexing and Slicing
• Element-by-Element Operations
• Filtering Values
• Views Versus Copies
• Some Array Methods
• Broadcasting
• NumPy Math
• Summary

Lessons 3: Visualization Libraries

• matplotlib
• Seaborn
• Plotly
• Bokeh
• Other Visualization Libraries
• Summary

Lessons 4: Machine Learning Libraries

• Popular Machine Learning Libraries
• How Machine Learning Works
• Learning More About Scikit-learn
• Summary

Lessons 5: Extracting, Transforming, and Loading Data

• Topic A: Extract Data
• Topic B: Transform Data
• Topic C: Load Data
• Summary

Lessons 6: Designing a Machine Learning Approach

• Topic A: Identify Machine Learning Concepts
• Topic B: Test a Hypothesis
• Summary

Lessons 7: Developing Classification Models

• Topic A: Train and Tune Classification Models
• Topic B: Evaluate Classification Models
• Summary

Lessons 8: Developing Regression Models

• Topic A: Train and Tune Regression Models
• Topic B: Evaluate Regression Models
• Summary

Lessons 9: Developing Clustering Models

• Topic A: Train and Tune Clustering Models
• Topic B: Evaluate Clustering Models
• Summary

Hands-on LAB Activities

Pandas

• Using the read_csv() Function
• Filtering a DataFrame Based on Index
• Indexing a DataFrame
• Sorting a DataFrame
• Creating a Series from a Dictionary Using pandas

NumPy

• Creating a Multi-Dimensional Array Using numpy
• Creating a One-Dimensional Array Using numpy

Visualization Libraries

• Creating a Scatter Plot Using matplotlib

Machine Learning Libraries

• Using scikit-learn
• Applying Box-Cox Transformation

Extracting, Transforming, and Loading Data

• Handling the Missing Values
• Performing Data Cleaning

Designing a Machine Learning Approach

• Performing Chi-Square Test
• Performing Two-Way ANOVA
• Calculating the Euclidean Distance between Two Series
• Performing Feature Selection Using Chi-Square Test
• Performing One-Way ANOVA
• Performing the Goodness of Fit Test

Developing Classification Models

• Performing Logistic Regression
• Performing Bagging
• Creating a Decision Tree
• Creating a Confusion Matrix
• Creating a Contingency Table

Developing Regression Models

• Performing Linear Regression on the Salary Dataset

Developing Clustering Models

• Performing K-Means Clustering