AI Driven Machine Learning with Python: Theory and Case Studies
Learning Path | 4 Course Series
Embark on a comprehensive exploration of AI-driven machine learning with Python in this course. Delve into theoretical foundations and practical applications across two dynamic sections. In the first section, master essential machine learning concepts and techniques using Python, with a focus on data preprocessing, model training, and evaluation. Then, immerse yourself in real-world case studies, tackling diverse challenges and honing your skills through hands-on projects.
Offer ends in:
What you'll get
- 12+ Hours
- 4 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Essential machine learning concepts and techniques using Python.
- Proficiency in data preprocessing, model training, and evaluation with Python libraries.
- Practical application of machine learning through real-world case studies and hands-on projects.
- Skills to tackle diverse challenges and excel in AI-driven machine learning.
- You will learn how to use data science and machine learning with Python.
- You will create data pipeline workflows to analyze, visualize, and gain insights from data.
- You will build a portfolio of data science projects with real world data.
- You will be able to analyze your own data sets and gain insights through data science.
- Master critical data science skills.
- Understand Machine Learning from top to bottom.
- Replicate real-world situations and data reports.
- Learn NumPy for numerical processing with Python.
- Conduct feature engineering on real world case studies.
- Learn Pandas for data manipulation with Python.
- Create supervised machine learning algorithms to predict classes.
- Learn Matplotlib to create fully customized data visualizations with Python.
Content
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Section 1: Machine Learning with Python 2024
Courses No. of Hours Certificates Details Machine Learning with Python 2024 5h 17m ✔ Machine Learning with Python Case Study - Covid19 Mask Detector 2h 05m ✔ Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔ -
Section 2: Projects and Case Studies on Machine Learning
Courses No. of Hours Certificates Details Projects and Case Studies on Machine Learning with Python 4h 5m ✔
Description
This course offers a comprehensive exploration of machine learning concepts and practical applications using Python. Divided into two sections, participants will first delve into the theoretical foundations of machine learning with Python, followed by hands-on case studies and projects to apply their knowledge in real-world scenarios.
Section 1: Machine Learning with Python 2024
Participants will begin by mastering the essentials of machine learning with Python. They will learn key concepts, algorithms, and techniques for data preprocessing, model training, and evaluation. Through practical demonstrations and exercises, participants will gain proficiency in implementing machine learning algorithms using popular Python libraries such as Scikit-learn and TensorFlow.
Machine Learning with Python Case Study #1
In this section, participants will dive into a detailed case study that applies machine learning techniques to a specific domain or problem. They will explore real-world datasets, preprocess data, select appropriate algorithms, and evaluate model performance. Through guided instruction and hands-on exercises, participants will learn how to apply machine learning concepts to solve practical problems.
Machine Learning Python Case Study #2
Participants will further expand their skills with another case study focused on a different application of machine learning. They will tackle challenges related to data analysis, feature engineering, model selection, and performance evaluation. By working through real-world scenarios, participants will deepen their understanding of machine learning principles and best practices.
Section 2: Projects and Case Studies on Machine Learning
In this section, participants will engage in projects and case studies that cover various aspects of machine learning. They will work on hands-on exercises and projects to reinforce their understanding of machine learning concepts and techniques. By applying their knowledge to diverse datasets and problem domains, participants will enhance their practical skills and gain confidence in their ability to tackle real-world machine learning challenges.
Throughout the course, participants will benefit from a blend of theoretical lectures, practical demonstrations, and interactive exercises. By the end of the course, participants will have the knowledge and skills to apply machine learning algorithms effectively to a wide range of problems and domains using Python.
Requirements
- No prior knowledge of machine learning required
- Basic knowledge of Python
Target Audience
- Anyone who wants to learn about data and analytics
- Data Engineers
- Analysts
- Architects
- Software Engineers
- IT operations
- Technical managers
Course Ratings
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The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills
Amit Sanjay Tiwari