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ML-LABS.AA1
Machine Learning Labs
Code a new ML solution, one line at a time, in a risk-free environment where data and algorithms become one.
- Practice in 25 Hands-On Labs — nothing to install
- 9 Interactive Lessons and 43 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
25 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Skills you'll get
What you will be able to do
Let’s play with algorithms, shall we? Our Machine Learning specialization labs offer a non-production environment where you can challenge yourself with real-world activities.
You’ll tinker with data, train your own models, and watch as ML algorithms come to life.
We’ll guide you through the code and concepts. So roll up your sleeves, grab a cup of coffee, and start coding.
- Master machine learning basics and complex concepts, wrapped up in one course.
- Develop a profound understanding of data preprocessing and feature engineering to upskill.
- Implement various machine learning algorithms (regression, classification, clustering).
- Utilize Python programming for data manipulation and analysis using NumPy, Pandas, and Matplotlib.
- Build predictive models using popular libraries (Scikit-learn, TensorFlow, PyTorch).
- Fine-tune models using hyperparameter tuning and cross-validation.
- Use model performance metrics to measure accuracy, precision, recall, and F1-score.
Target Career Roles
- Machine Learning Engineer
- Data Scientist
- Research Scientist in Artificial Intelligence
- Data Engineer
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
9 Interactive Lessons · 43 topics01 Pandas 7 topics · 5 LiveLab +
- About DataFrames
- Creating DataFrames
- Interacting with DataFrame Data
- Manipulating DataFrames
- Manipulating Data
- Interactive Display
- Summary
5 LiveLab in this lesson — see the labs panel →
02 NumPy 10 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
03 Visualization Libraries 6 topics · 1 LiveLab +
- matplotlib
- Seaborn
- Plotly
- Bokeh
- Other Visualization Libraries
- Summary
1 LiveLab in this lesson — see the labs panel →
04 Machine Learning Libraries 4 topics · 2 LiveLab +
- Popular Machine Learning Libraries
- How Machine Learning Works
- Learning More About Scikit-learn
- Summary
2 LiveLab in this lesson — see the labs panel →
05 Extracting, Transforming, and Loading Data 4 topics · 2 LiveLab +
- Topic A: Extract Data
- Topic B: Transform Data
- Topic C: Load Data
- Summary
2 LiveLab in this lesson — see the labs panel →
06 Designing a Machine Learning Approach 3 topics · 6 LiveLab +
- Topic A: Identify Machine Learning Concepts
- Topic B: Test a Hypothesis
- Summary
6 LiveLab in this lesson — see the labs panel →
07 Developing Classification Models 3 topics · 5 LiveLab +
- Topic A: Train and Tune Classification Models
- Topic B: Evaluate Classification Models
- Summary
5 LiveLab in this lesson — see the labs panel →
08 Developing Regression Models 3 topics · 1 LiveLab +
- Topic A: Train and Tune Regression Models
- Topic B: Evaluate Regression Models
- Summary
1 LiveLab in this lesson — see the labs panel →
09 Developing Clustering Models 3 topics · 1 LiveLab +
- Topic A: Train and Tune Clustering Models
- Topic B: Evaluate Clustering Models
- Summary
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
25 LiveLabs- 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
- Creating a Scatter Plot Using matplotlib
- Using scikit-learn
- Applying Box-Cox Transformation
- Handling the Missing Values
- Performing Data Cleaning
- 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
- Performing Logistic Regression
- Performing Bagging
- Creating a Decision Tree
- Creating a Confusion Matrix
- Creating a Contingency Table
- Performing Linear Regression on the Salary Dataset
- Performing K-Means Clustering
03 / FAQs
Questions before you start
What is this course level?+
What programming languages will be used?+
What kind of datasets will I work with?+
What are the career opportunities for machine learning professionals?+
Enrolling in our Real-world Machine Learning course can provide numerous career benefits, such as:
- Enhanced expertise
- Improved job prospects
- Increased earning potential
- Career Advancement
- Networking opportunities
Which roles can I pursue after taking this training?+
Keep going
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Build Intelligent Systems
Join our Machine Learning labs online to develop practical skills to create powerful AI models.
- 1 year of full access
- 25 LiveLab included
- Certificate of completion
No credit card required