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
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
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
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