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Machine Learning (ML) Labs: Training For Professionals

Code a new ML solution, one line at a time, in a risk-free environment where data and algorithms become one.

(ML-LABS.AA1) / ISBN : 978-1-64459-455-1
This course includes
Hands-On Labs
AI Tutor (Add-on)
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About This Course

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. 

Skills You’ll Get

  • 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.

Download Course Outline

1

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
2

NumPy

  • Creating a Multi-Dimensional Array Using numpy
  • Creating a One-Dimensional Array Using numpy
3

Visualization Libraries

  • Creating a Scatter Plot Using matplotlib
4

Machine Learning Libraries

  • Using scikit-learn
  • Applying Box-Cox Transformation
5

Extracting, Transforming, and Loading Data

  • Handling the Missing Values
  • Performing Data Cleaning
6

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
7

Developing Classification Models

  • Performing Logistic Regression
  • Performing Bagging
  • Creating a Decision Tree
  • Creating a Confusion Matrix
  • Creating a Contingency Table
8

Developing Regression Models

  • Performing Linear Regression on the Salary Dataset
9

Developing Clustering Models

  • Performing K-Means Clustering

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Satisfy a curious mind by exploring more about our Machine Learning practical course.

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This Machine Learning course is designed for beginners and intermediate learners. No prior machine learning experience is required.

The primary programming language used in this hands-on Machine Learning course is Python.

You’ll work with various real-world datasets, including those from Kaggle and other open-source repositories.

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

Our Machine Learning lab exercises will develop the skills and knowledge needed to land a job as a machine learning engineer, data scientist, or AI researcher.

Build Intelligent Systems

Join our Machine Learning labs online to develop practical skills to create powerful AI models.

R 3609.3

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