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Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) (Python Institute) Beginner

The Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) course by Open Source equips data analysts, software developers, and IT professionals with foundational AI/ML concepts and hands-on skills to address the global shortage of 3.4 million skilled AI practitioners. Learners gain practical experience in neural networks, NLP, and ethical AI using frameworks like TensorFlow and PyTorch.

This course prepares learners for the Microsoft AI-900 certification with official vendor-authorized courseware and includes 30-day lab access for hands-on practice. Graduates build a strong foundation to pursue roles in AI development and responsible AI implementation.

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Course Overview

The Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) by Open Source is an introductory course designed for aspiring AI practitioners, data analysts, and software developers seeking foundational knowledge in AI technologies. While not tied to a specific vendor certification, this course aligns closely with principles tested in credentials like the Certified AI Fundamentals (CAIF™) and prepares learners for roles such as Junior Machine Learning Engineer, AI Support Specialist, and AI/ML Developer. With 77% of enterprises now exploring or deploying AI solutions, understanding core AI and ML concepts has become essential across technical disciplines. This course provides a vendor-neutral foundation, making it ideal for individuals entering the AI field or transitioning from traditional software development into intelligent systems design.

Students engage with key technologies including Python, Jupyter Notebooks, TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers through hands-on labs delivered in Google Colab’s cloud-based environment. The curriculum includes building and training neural networks from scratch, implementing convolutional neural networks for image classification, and fine-tuning transformer models for natural language processing tasks. One real-world project involves constructing a sentiment analysis model using real-time data from the Stack Overflow Developer Survey, allowing students to apply supervised learning techniques in a practical context. All labs are designed to run on free-tier Google Colab with optional GPU acceleration, ensuring accessibility without local setup.

This course enhances career readiness by preparing candidates for industry-recognized credentials such as the CAIF™ certification, which validates foundational AI competence and is increasingly sought by employers investing in responsible AI adoption. Entry-level AI and ML roles offer average starting salaries between $85,000 and $110,000, with strong growth projected across sectors. Koenig Solutions supports learners through its Guaranteed-to-Run promise and access to official courseware, ensuring structured progression from theory to implementation. By mastering the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) from Open Source, students gain the practical skills needed to pursue advanced specializations and contribute to real-world AI initiatives in generative models, computer vision, and intelligent automation.

What You'll Learn

✓Implement neural networks using TensorFlow and PyTorch, gaining hands-on experience with industry-leading AI frameworks from Open Source to build accurate models
✓Design computer vision systems with convolutional architectures to enhance image recognition capabilities and solve real-world visual tasks
✓Deploy natural language processing models using transformers to enable advanced language understanding and conversational AI solutions
✓Configure knowledge representation in symbolic AI systems to improve reasoning and decision-making processes
✓Apply ethical principles in Open Source AI development to ensure responsible and fair AI solutions
✓Optimize AI models using Open Source frameworks to increase performance and reduce training time, making AI deployment more efficient

Prerequisites

Recommended knowledge before taking this course
  • Proficiency in Python 3.8+ programming, including mastery of variables, loops, functions, and object-oriented concepts.
  • Familiarity with data manipulation libraries such as NumPy and Pandas for effective data preprocessing.
  • Foundational knowledge of Linear Algebra, specifically matrix multiplication and dot products, and introductory statistics.
  • Experience with SQL for data extraction and basic data handling techniques.
  • Comfortable using command-line interfaces in Windows or Linux environments for managing development workflows.
  • Access to a system with at least 8GB RAM and Python 3.8+ installed to support the technical environment requirements.
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Certification Exam

Everything you need to know about the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) (Python Institute) certification exam

Exam Details
Exam Name
Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) (Python Institute)
Format
Multiple choice, labs & case studies
Questions
Duration
Passing Score
Validity
Retake Policy
Candidates may retake the exam at any time following an unsuccessful attempt, subject to the fees and scheduling policies of the chosen examination provider.
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Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) (Python Institute)

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Course Curriculum

Structured learning with hands-on labs and real-world scenarios

1
Day 1– Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) Origins
Define Artificial Intelligence core concepts Analyze Open Source AI historical milestones Identify modern AI industry applications Distinguish between AI and ML Classify diverse AI system types Master neural network architecture basics Review essential AI ethics frameworks Configure your professional development environment
2
Day 2– Symbolic AI and Knowledge Representation Systems
Understand GOFAI logic concepts Represent complex knowledge using ontologies Build functional expert system modules Use advanced concept graph structures Apply automated logical reasoning techniques Compare symbolic and sub-symbolic AI Execute hands-on expert system labs Evaluate logical reasoning accuracy metrics
3
Day 3– Neural Networks and Deep Learning Proficiency
Implement robust perceptron model architectures Design efficient multi-layer perceptron networks Train complex neural network models Use PyTorch and TensorFlow frameworks Prevent model overfitting through regularization Evaluate neural model performance metrics Complete intensive hands-on MLP labs Compare leading deep learning frameworks
4
Day 4– Computer Vision and Generative AI Models
Process digital images using OpenCV Build scalable CNN architecture models Apply effective transfer learning techniques Train high-performance autoencoder models Develop innovative generative adversarial networks Implement real-time object detection systems Perform precise semantic image segmentation Execute hands-on generative AI labs
5
Day 5– NLP Mastery and Responsible AI Ethics
Represent text data using TF-IDF Train advanced word embedding models Apply Word2Vec and GloVe algorithms Use RNNs for language processing Implement modern Transformer model architectures Fine-tune pre-trained BERT language models Explore effective prompt programming strategies Practice responsible AI development standards

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  • ★★★★★

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  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

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    Head of L&D, UK Enterprise

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  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified

Frequently Asked Questions

Everything you need to know about the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) (Python Institute) training course

Is the certification exam included in the course fee for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) by Open Source, and what is the exam cost?
The Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) by Open Source is a training course; certification exam vouchers are not included in the tuition fee. Students are responsible for registering for their chosen industry-recognized certification exams independently.
What training formats does Open Source offer for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML), and is there a Guaranteed-to-Run schedule?
Open Source provides live online instructor-led and classroom sessions for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). Our Guaranteed-to-Run policy ensures your training proceeds as scheduled, even with low enrollment, while our e-learning options offer additional flexible, structured study paths.
How long is lab access provided for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML), and what type of lab environment is used?
You receive one year of lab access for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). Our cloud-based sandbox, powered by Google Colab, requires no local setup. It features GPU acceleration for deep learning, allowing you to practice with real-world datasets immediately.
What is Open Source's rescheduling and cancellation policy for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) course?
Open Source allows free rescheduling for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) with 14 days' notice. Cancellations within this window earn full credit for future training. This policy applies to all formats, ensuring your professional development remains flexible and financially secure.
What is the exam format, number of questions, passing score, and time limit for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) certification?
As the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) by Open Source is a foundational training course, it does not have a proprietary certification exam. Students are encouraged to pursue vendor-neutral or vendor-specific certifications (such as AI-900) which follow their respective exam formats and requirements.
How long is the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) certification valid, and what is the renewal process and cost?
The Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) by Open Source is a training course, not a certification with an expiration date. Students should refer to the specific requirements of the professional certification body they choose to pursue after completing this training.
What post-training support does Open Source provide after completing the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) course?
Open Source offers 6 months of post-training support for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). You gain mentor access, updated materials, and community forums. We also include the ability to audit future sessions at no extra cost.
What are the prerequisites or prior experience needed for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) course?
The Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) requires basic Python and introductory calculus knowledge. No prior machine learning experience is needed. Our curriculum bridges foundational math and coding skills, perfect for beginners or professionals aiming to pivot into specialized AI and ML roles.
What salary increase or career impact can be expected after completing the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) certification?
While individual results vary, the U.S. Bureau of Labor Statistics (BLS) reports that employment in computer and information research science, which includes AI and machine learning, is projected to grow much faster than the average for all occupations. Completing the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) by Open Source provides the foundational skills necessary to pursue these high-demand career paths.
How does Open Source's Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) training compare with self-study options in terms of effectiveness and time required?
Open Source’s training for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) takes 40-50 hours. Our expert-led instruction and hands-on labs are designed to provide a structured learning environment that helps students master practical skills more efficiently than through self-study alone.

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