What you'll get
- 8+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Understand core machine learning concepts, including different learning approaches, their benefits, and limitations.
- Develop proficiency in essential Python libraries for data manipulation, analysis, and visualization, such as NumPy, Pandas, Matplotlib, and Scikit-learn, while building a solid foundation in Python programming.
- Implement machine learning algorithms in Scikit-learn, including model training, prediction, and evaluation.
- Apply machine learning techniques to real-world problems, such as movie review analysis, to gain practical insights and experience the complete workflow.
- Become familiar with additional data science libraries, including SciPy, Seaborn, and Plotly.
- In-depth knowledge of machine learning concepts and types of learning, with this course’s distinct emphasis on connecting foundational theory directly to industry-relevant applications.
- Gain practical experience with algorithms such as regression, classification, Naive Bayes, decision trees, and support vector machines.
- Understand and apply feature engineering techniques to enhance model performance in machine learning tasks.
- Build a strong Python foundation for data science and machine learning.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with Scikit Learn | 8h 37m | ✔ | View Curriculum |
Description
This course offers an in-depth, practical introduction to machine learning using Python. It progresses from foundational concepts to practical applications, covering key Python libraries for data manipulation, visualization, and machine learning. Participants will learn to preprocess data, train and evaluate models, and apply machine learning techniques to real-world tasks such as sentiment analysis of movie reviews.
Introduction to Machine Learning
Learners explore the core principles of machine learning, including its advantages, limitations, and practical applications across different domains.
Learners explore the core principles of machine learning, including its advantages, limitations, and practical applications across different domains.
NumPy
Participants develop proficiency with NumPy, a key library for numerical computing, learning to create and manipulate arrays, perform indexing and slicing, and handle complex data structures.
Participants develop proficiency with NumPy, a key library for numerical computing, learning to create and manipulate arrays, perform indexing and slicing, and handle complex data structures.
NumPy Array
Learners gain the ability to create NumPy arrays, execute indexing and slicing operations, and manage multi-dimensional arrays for efficient data handling.
Matplotlib
The course covers Matplotlib, enabling learners to create a variety of plots and charts to effectively visualize datasets.
The course covers Matplotlib, enabling learners to create a variety of plots and charts to effectively visualize datasets.
Pandas
Participants master Pandas for data analysis, cleaning, and manipulation, gaining the skills to work efficiently with structured data.
Participants master Pandas for data analysis, cleaning, and manipulation, gaining the skills to work efficiently with structured data.
Scikit-learn
Learners dive into Scikit-learn to implement machine learning algorithms, train models, make predictions, and assess model performance.
Learners dive into Scikit-learn to implement machine learning algorithms, train models, make predictions, and assess model performance.
Learning and Predicting
The training emphasizes hands-on model building, enabling participants to train models and generate predictions on new data.
The training emphasizes hands-on model building, enabling participants to train models and generate predictions on new data.
Cross Validation
Participants learn cross-validation techniques to evaluate models and minimize overfitting.
Participants learn cross-validation techniques to evaluate models and minimize overfitting.
Movie Review Analysis
A practical project guides learners through preprocessing text data, building sentiment analysis models, and evaluating performance, providing real-world experience in applying machine learning.
A practical project guides learners through preprocessing text data, building sentiment analysis models, and evaluating performance, providing real-world experience in applying machine learning.
Reference Files
Participants receive supporting files and resources for continued learning and practice beyond the course.
Participants receive supporting files and resources for continued learning and practice beyond the course.
Participants build strong Python skills and confidence in machine learning through conceptual lessons, hands-on demonstrations, and interactive exercises. By the end of the course, they will be able to work with diverse datasets and perform meaningful analyses using machine learning algorithms.
Requirements
- Python Programming: Develop a solid understanding of Python fundamentals.
- Data Preprocessing: Acquire skills to clean and prepare data for analysis and machine learning.
Target Audience
- Python developers seeking to advance their Python expertise.
- Data scientists aiming to strengthen their machine learning and data analysis skills.
- Computer engineers interested in applying Python to practical computing projects.
- Researchers, including academics and professionals, who use Python for data-driven research.
- Students interested in Python programming and data science.
