About
This comprehensive course covers the fundamentals and advanced techniques of Artificial Intelligence (AI) and Machine Learning (ML), focusing on Python programming, data analysis with Pandas and NumPy, and visualizations using Tableau. Students will explore the core concepts of Machine Learning, including Natural Language Processing (NLP) and Deep Learning, and gain hands-on experience with real-world industry scenarios. This course will equip learners with the knowledge and practical skills necessary to pursue careers in data science, machine learning, and AI, while also preparing them to tackle complex business problems using data-driven analysis.
Learning Objective
By the end of the course, participants will:
- Understand the role and responsibilities of Data Scientists and AI Engineers.
- Master tools and technologies including Python, NumPy, Pandas, Matplotlib, Seaborn, and Tableau.
- Gain hands-on experience building machine learning models and solving real-world industry problems.
- Develop expertise in advanced topics like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and NLP.
- Learn to optimize and scale machine learning models, deploy models using cloud platforms, and build APIs.
- Enhance their portfolio with case studies, capstone projects, and real-world applications in domains like healthcare, finance, and marketing.
Exam Information
- Duration: 180 minutes
- Number of Questions: 90
- Mode of Exam: Computer-Based Test
Curriculum
- 10 Sections
- 33 Lessons
- 40 Hours
Expand all sectionsCollapse all sections
- Introduction to Python4 Items
- Data Analysis with NumPy and Pandas4 Items
- Data Visualization3 Items
- Machine Learning Fundamentals3 Items
- Supervised Learning4 Items
- Unsupervised Learning3 Items
- Time Series Analysis and Big Data Tools3 Items
- Deep Learning and NLP3 Items
- Ethics in AI and Real-World Applications3 Items
- Data Preparation and Visualization with Tableau3 Items
Requirements
- A basic understanding of programming concepts is recommended, but not mandatory.
- Prior experience with Python or statistics will be helpful, but all necessary foundational knowledge will be covered in the course.
Target audiences
- Aspiring Data Scientists, Machine Learning Engineers, and AI Professionals.
- Graduates or professionals looking to transition into the AI and Machine Learning fields.
- Business Analysts, Statisticians, or anyone interested in leveraging AI/ML techniques for data-driven decision-making.
- Professionals working in industries like healthcare, finance, retail, and marketing who want to apply AI/ML techniques to solve real-world business problems.