What you'll get
- 49+ Hours
- 7 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
- Download Curriculum
Synopsis
- Offers in-depth training in Artificial Intelligence and Machine Learning, covering ensemble techniques and decision-tree models using R and Python.
- Provides one-year access, enabling learners to study and revise at their convenience.
- Suitable for anyone aiming to build a career in AI; prior basic understanding of AI is helpful but not mandatory.
- Includes practical project work along with a Certificate of Completion for each course module.
- Certifications are verifiable and come with unique URLs that can be added to resumes or LinkedIn profiles to showcase enhanced analytics skills.
- Delivered as a self-paced video learning program for maximum flexibility.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Artificial Intelligence with Python - Beginner Level | 2h 51m | ✔ | View Curriculum |
| Artificial Intelligence with Python - Intermediate Level | 4h 34m | ✔ | View Curriculum |
| Artificial Intelligence with Python | 6h 15m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Artificial Intelligence and Machine Learning Training Course | 12h 8m | ✔ | View Curriculum |
| Machine Learning with R | 20h 25m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
| Card Purchase Prediction using R | 2h 28m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| No courses found in this category. | |||
Description
This program offers a complete learning pathway into Artificial Intelligence and Machine Learning, guiding learners through essential concepts, advanced methodologies, and real-world applications using both Python and R. The course is structured to build strong technical foundations while steadily advancing toward more complex AI systems and machine learning models.
Section 1: Introduction to Artificial Intelligence with Python
Section 1 lays the groundwork for the AI journey with beginner and intermediate Python courses. Participants learn essential AI techniques—including neural networks, NLP, and computer vision—while applying them through hands-on Python projects.
Section 2: Advanced Artificial Intelligence and Machine Learning
Section 2 moves beyond the basics and explores deeper AI and ML topics. Through the "Artificial Intelligence and Machine Learning" course, participants study advanced techniques such as deep learning and reinforcement learning. Practical exercises and real-world projects help them apply these methods to develop intelligent solutions for complex problems.
Section 3: Specialized Topics in Machine Learning with R
This section highlights the capabilities of the R programming language in machine learning. Learners examine a variety of algorithms with a strong focus on supervised learning and logistic regression. The module also includes a hands-on project, "Project on R - Card Purchase Prediction," enabling learners to apply concepts to a realistic business scenario.
Section 4: Mock Tests and Quizzes
Section 4 provides structured assessments to help learners gauge their understanding of all covered topics. Mock tests and quizzes allow participants to track their progress, reinforce key concepts, and identify areas that may require additional focus as they prepare for further growth in the AI domain.
The course is designed to provide learners with a solid, hands-on grasp of AI and ML, catering to both newcomers and professionals looking to enhance their skills.
Sample Certificate
Requirements
- Fundamental programming knowledge is recommended, with prior experience in Python and R considered an advantage.
- Comfort with mathematics, including algebra, calculus, linear algebra, and probability.
- Exposure to statistics: descriptive stats, probability distributions, and hypothesis testing.
- Familiarity with fundamental machine learning concepts, including supervised and unsupervised learning, is advantageous.
- A curious mindset and openness to learning are essential for success in the rapidly changing world of AI and ML.
Target Audience
- Aspiring data scientists and ML professionals seeking core skills.
- Software developers looking to expand into AI and ML.
- Students in CS, data science, engineering, or related fields seeking practical training.
- Professionals aiming to transition into AI, ML, or data science roles.
- Entrepreneurs and business owners want to leverage AI for innovation.
- Practitioners in AI/ML seeking to deepen expertise.
- Technology enthusiasts and anyone curious about AI and ML.
