Course Overview

Discover the power of AI with our ‘AI Marketing’ course. At TBC, we designed this course specifically for forward-thinking professionals like you.

Whether you’re a marketing professional seeking to enhance your skillset, a business owner aiming to leverage AI for better customer insights, or a tech enthusiast interested in the intersection of AI and marketing, this course is for you.

Our ‘AI Marketing’ course delves into how AI can transform your marketing efforts, from predictive analytics to personalized customer experiences. You’ll learn how to harness AI tools and techniques to drive impactful marketing strategies.

Completing this course not only boosts your marketing capabilities but also positions you as a leader in your field. Imagine leading AI-driven marketing campaigns that yield exceptional results and set new industry standards.

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Course Breakdown

  1. Define Artificial Intelligence, its history, and evolution.
  2. Analyze sector status and AI application requirements in Canada.
  3. Conduct an industry survey that encompasses data from relevant entities.
  4. Identify the differences between Machine Learning and Deep Learning.
  5. Develop a Machine Learning Model.
  • This course provides an overview of artificial intelligence (AI) and machine learning (ML), exploring their diverse applications across various fields.
  • Students will gain an understanding of fundamental concepts such as supervised learning, unsupervised learning, and reinforcement learning.
  • Additionally, the course includes hands-on exercises where students will implement simple ML algorithms, including linear regression and k-means clustering, to solidify their practical knowledge.
  • Ethical considerations in AI development and deployment are critical, focusing on fairness, transparency, and accountability in AI systems.
  • The program covers techniques for identifying and mitigating biases in datasets and models.
  • Additionally, it explores interpretability methods for understanding and explaining AI model predictions.
  • Explore AI applications across various domains, including healthcare, finance, cybersecurity, and autonomous systems.
  • Learners engage in group projects to develop and implement AI solutions for real-world problems. The outcomes of these projects are presented and discussed, highlighting the challenges faced and lessons learned.
  • Delve into data collection, cleaning, and preprocessing techniques essential for AI and ML applications.
  • The program covers exploratory data analysis (EDA) and feature selection methods.
  • Additionally, it introduces tools and libraries for data manipulation and transformation, such as pandas and scikit-learn.
  • The curriculum includes understanding neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and various deep learning architectures.
  • Participants will gain hands-on experience with deep learning frameworks such as TensorFlow and Keras.
  • Projects will focus on applying deep learning techniques to tasks like image classification, natural language processing, and sequence prediction.
  • In-depth exploration of advanced ML algorithms, including decision trees, random forests, support vector machines, and neural networks.
  • It includes practical sessions on feature engineering, model tuning, and performance evaluation.
  • Participants will engage in project work, applying advanced ML techniques to real-world datasets.