Looking for an awesome platform where you can take a course in IBM artificial intelligence? What career path do you want to go for? In this article, we have provided you with detailed information that will provide answers to your questions. One thing you are sure to get from this article is clarity. So, stick around, grab a pen and paper with a cold glass of water, and enjoy this educational article.
There are quite a number of cool platforms that offer a variety of courses and learning resources for those interested in IBM Artificial Intelligence. However, in this article we will narrow it down to the top 5 learning platforms. Kindly note that this is based on the writer’s research. You are hereby advised to “DYOR” before taking any major step.
IBM Artificial Intelligence Courses
- IBM Skills Network: This platform offers a range of free and paid courses on AI, data science, machine learning, and other related fields. Some popular courses include:
- AI for Everyone: This is an introductory course that covers the basics of AI and its applications.It typically takes about 4-6 weeks to complete, assuming a few hours of study per week.
- Machine Learning with Python: This course focuss on the fundamentals of machine learning using Python.This course can take approximately 6-8 weeks to complete, depending on the learner’s pace.
2.Coursera: Coursera is quite popular.IBM partners with Coursera to offer specialized programs and courses in AI. Some notable offerings are:
- IBM Artificial Intelligence Engineering Professional Certificate: This is comprehensive program that covers machine learning, deep learning, and AI applications. It includes several courses such as:
- Machine Learning with Python
- Deep Learning with TensorFlow
- Artificial Intelligence Capstone Project with Deep Learning
This program consists of six courses and a capstone project. It usually takes around 6-7 months to complete if you dedicate about 3-5 hours per week.
- IBM Data Science Professional Certificate: This program includes substantial content on Artificial Intelligence and machine learning while focused on data science.Comprising nine courses, this program can take approximately 8-10 months to finish with a commitment of 3-5 hours per week.
- edX: IBM also works together with edX to provide courses on Artificial Intelligence and related technologies. E.g include:
- Artificial Intelligence Professional Certificate: This sequence of courses covers fundamentals in artificial intelligence, machine learning, and deep learning.This series of courses often takes around 3-6 months to complete, with an estimated workload of 2-4 hours per week per course.
- Cognitive Class: Previously known as Big Data University, Cognitive Class is an IBM initiative offering free courses on data science, Artificial Intelligence, and big data. Courses include:
- Deep Learning (This is a free course can be completed in about 8-10 hours).
- Machine Learning with Python (takes around 20 hours to complete).
- Data Science Hands-On with Open Source Tools
- IBM Developer: IBM Developer offers various learning paths and tutorials on AI, machine learning, and data science. This platform includes hands-on labs, code patterns, and articles to help learners build practical skills.The learning paths and tutorials on IBM Developer vary greatly in length. Some tutorials might take just a few hours, while comprehensive learning paths can span several weeks.
Please note
The duration to complete IBM’s Artificial Intelligence courses can vary widely depending on the course, the learner’s prior knowledge, and the depth of the material covered.In addition, these courses range from beginner to advanced levels, providing opportunities for learners at all stages to develop their AI expertise.
Students undertaking IBM’s AI courses may experience several specific challenges, which can differ based on their background, experience, and the course content. Here are some common challenges faced in IBM’s Artificial Intelligence courses:
- Technical Complexity:
- Mathematical Rigor: AI concepts require a strong understanding of mathematics, particularly linear algebra, calculus, and statistics. Learners without a solid math background may struggle with these topics. My calculus days in year one wasn’t funny all, its important you brace up for this
- Programming Skills: IBM Artificial Intelligence courses often require proficiency in programming languages such as Python. Those new to programming might find it challenging to keep up.If this really what you want then giving up is not an option
- The challenge of Time
- Balancing with Other Responsibilities: My days in cyber girls fellowship was quite difficult. This is so for many learners are working professionals or students with other commitments. Finding the time to consistently work through course material can be challenging.
- Self-Paced Learning: While flexibility is an advantage, self-paced courses require a high degree of self-discipline and time management skills, which some learners may find challenging. Having an itinerary helps yo keep you on track and in check
- Access to Resources:
- Computational Resources: Some advanced AI courses, especially those involving deep learning, require significant computational power. Learners may need access to high-performance hardware, which can be costly or inaccessible.Not having access to this can distort learning in a great way
- Data Availability: Practical exercises often require substantial datasets. Accessing, managing, and processing large datasets can be a hurdle.
- Practical Application (Theoritcal Learning wont Fly)
- Hands-On Projects: Courses like those on IBM Skills Network and Coursera often include hands-on projects. Applying theoretical knowledge to practical problems can be challenging, especially without prior experience.
- Real-World Integration: Understanding how to incorporate AI models into real-world applications and workflows can be complex and requires additional domain knowledge.
- Staying Motivated (It is easy to lose Focus)
- Consistency: It is easy to start. However, maintaining consistent progress can be tough, especially for self-learners. Lack of immediate feedback or peer interaction can lead to decreased motivation.
- Overcoming Difficult Topics: Encountering particularly challenging topics or assignments can be discouraging, and finding the right support or resources to overcome these hurdles is crucial. Build a community of like minded individual
- Interpreting and Debugging Models
Model Understanding:Learners often struggle with interpreting and debugging AI models, particularly complex ones like deep learning networks. Understanding why a model behaves a certain way and how to improve it requires deep insight.
- Navigating Course Structure
- Course Navigation: This can be exhausting, With numerous modules and supplementary materials, learners may find it overwhelming to navigate through extensive course content.
- Prerequisites: Ensuring that they meet the prerequisites for advanced courses can be a barrier. Some learners might need to take additional foundational courses to build the necessary background.
- Ethical and Bias Concerns
- Understanding Ethics: Grasping the ethical implications and ensuring fairness and transparency in AI models can be a complex and often nuanced aspect of learning AI.
To combat these challenges effectively and efficiently, learners can take several steps:
- Leverage Community Support: As mentioned earlier “build a community of like minded individual”. Joining forums, study groups, or online communities related to the course can provide valuable support and motivation.
- Supplementary Resources: Utilizing additional resources such as textbooks, online tutorials, and coding practice platforms can help strengthen learning.
- Time Management: Setting a regular study schedule and breaking down the material into manageable chunks can aid in maintaining consistent progress. This is crucial to the success of your learning. Remember, its not just about starting a course but Finishing strong
- Seek Feedback: Learn to ask for feedback. Engaging with instructors or peers for feedback on projects and assignments can help improve understanding and performance.
IBM and its partner platforms typically offer various resources, including forums, peer networks, and instructor support, to help learners overcome these challenges and succeed in their AI learning journey.
