Machine Learning Labs

This Course Includes:

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 visualisation 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 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: Visualisation Libraries

  • matplotlib
  • Seaborn
  • Plotly
  • Bokeh
  • Other Visualisation 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


  • 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


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

Visualisation Libraries

  • Creating a Scatter Plot Using matplotlib

Machine Learning Libraries

  • 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

Exam FAQs

FAQ's are not Available for this course.




9+ Lessons

Delivery Method:




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