AI & Machine Learning
Unlock Data-Driven Insights with AI and Machine Learning
As Artificial Intelligence (AI) and Machine Learning (ML) rapidly reshape industries, the need for foundational knowledge in these transformative technologies has never been more pressing.
The AI and Machine Learning course equips you with the core concepts, practical tools, and critical thinking needed to develop and deploy intelligent solutions that drive tangible business results. Through hands-on exercises and real-world case studies, you’ll learn how to design end-to-end ML pipelines—from data collection and preprocessing to model deployment and maintenance.
This course not only demystifies AI and ML for business contexts—it empowers you to confidently transform raw data into actionable insights, driving innovation and a competitive edge for you and your organization.
If you’re eager to learn the foundations of AI and ML to use the latest tools to get insights from your data, or if you are beginning to embark on a Data Scientist career, this is the right course for you to get started.
Prerequisite: Data Wrangling with Python course, or strong Python and data coding skills:
- Basic Python syntax
- Flow control structures (loops and conditionals)
- Function definition and calling
- Basic Python data structures (lists and dictionaries)
- NumPy arrays
- Pandas DataFrames
What You’ll Learn from the AI & Machine Learning Course
Foundations of AI and Machine Learning
- Understand the basic definitions of AI and machine learning
- Recognize common ML tasks (regression, classification, unsupervised learning) and their applications
- Learn fundamental ML algorithms (linear regression, logistic regression, decision trees, random forests, principal component analysis, k-means clustering)
Model Selection and Training
- Choose an appropriate algorithm for your use case
- Measure errors in your model with appropriate metrics
- Tune the model performance by adjusting hyperparameters
- Automatically optimize models with cross-validation
Data Preparation and Feature Engineering
- Transform raw features into useful input for ML models
- Impute missing values
- Properly encode categorical features
- Engineer sophisticated features to improve model performance
Using scikit-learn for Machine Learning
- Learn the scikit-learn API, a leading machine learning library
- Use existing scikit-learn estimators and transformers
- Build custom scikit-learn components to solve your specific problems
- Coordinate many feature engineering steps with ColumnTransformers
- Simplify the training and prediction cycles with Pipelines
Machine Learning Applications
- Predict housing prices with regression models
- Understand customer churn with classification models
- Simplify complex data sets with dimensionality reduction
- Categorize customer types with clustering
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Self-Paced, Hybrid Course
- 19—28 hours course (depends on experience with Python and data)
- 7 hours of self-paced content
- 4—6 hours exercises
- 8—15 hours mini-project
- Hands-on, project oriented experiential learning
- Learn at your own pace
- Instructor office hours for supplemental support
- No travel
Common Roles
- Junior business analysts, data analysts, finance analysts, BI analysts, MI analysts, product operations, design operations, project managers
- Individuals looking to get a foundational understanding of AI/data science techniques and learn how to leverage AI tools to solve simple to intermediate real-world business problems
Key Takeaways
By the end of this course, you will be able to:
Accelerate AI-Driven Solutions
- Develop and deploy predictive models that address real business challenges and unlock new revenue streams.
- Automate repetitive tasks and decision-making processes, maximizing efficiency and innovation.
Optimize Model Lifecycle Management
- Adapt quickly to evolving data and business requirements, ensuring long-term model relevance.
Drive Strategic Business Impact
- Integrate AI initiatives with broader enterprise strategies, accelerating data-driven transformation.
- Translate technical findings into actionable insights that bolster competitive advantage and ROI.
Ready to start?
AI & Machine Learning
After completing AI and Machine Learning, you’ll gain the practical skills and strategic mindset necessary to turn cutting-edge AI technologies into real-world business outcomes—empowering you to lead in an increasingly data-centric world.
Your Path To Certification
- Enroll in All Three Courses: Complete Data Wrangling with Python, AI & Machine Learning, and Generative AI for NLP.
- Engage & Apply: Learn at your own pace, apply concepts with hands-on exercises, and attend office hours for personalized support.
- Demonstrate Mastery: Validate your new abilities through real-world mini-projects.
- Earn the Credential: Upon successful completion of all three courses, you’ll be awarded the Data Science Analyst Certification.
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