AI Engineer Explorer Course
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AI Engineer Explorer Course
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What you'll learn
Master Python programming essentials for AI applications, including data structures and file handling.
Gain proficiency in data manipulation with NumPy and Pandas, preparing datasets for machine learning.
Understand core mathematical concepts like linear algebra and calculus as they apply to AI.
Implement machine learning algorithms and apply them to real-world problems using supervised learning techniques.
Details to know
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There are 6 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course will guide you through the essential skills and concepts you need to become proficient in artificial intelligence engineering. You'll start with a strong foundation in Python programming, diving into core data science tools and techniques before advancing to key mathematical principles that power AI algorithms. As you progress, you'll master machine learning techniques and apply them in real-world projects, building confidence and practical knowledge. The course begins with Python programming basics, including control flow, functions, and working with data structures. You'll then move into data science, where you'll learn to handle data using libraries like NumPy and Pandas, followed by data visualization using Matplotlib and Seaborn. This section will prepare you to clean, manipulate, and analyze large datasets efficiently—key skills for any AI engineer. Next, you'll dive into the mathematics behind machine learning, including linear algebra, calculus, and statistics. These concepts are crucial for understanding the inner workings of AI algorithms and building more sophisticated models. You'll also explore machine learning itself, from basic supervised learning models to more advanced techniques like regression, classification, and k-Nearest Neighbors (k-NN). This course is perfect for anyone looking to launch or enhance their career in AI engineering. It is designed for individuals with basic programming knowledge who want to deepen their understanding of Python, data science, and machine learning. The course is suitable for learners with intermediate experience in Python and programming basics. It is a comprehensive introduction to AI engineering with a hands-on, project-based approach. By the end of the course, you will be able to write Python code for AI tasks, clean and manipulate data with Pandas and NumPy, apply mathematical principles to machine learning models, and implement basic machine learning algorithms like regression, classification, and k-NN.
In this module, we will introduce you to the course’s goals, the key concepts you’ll learn, and how this will prepare you for a successful AI engineering career. You’ll also meet your instructor and get an overview of their approach to teaching.
What's included
1 video1 reading
1 video•Total 5 minutes
- What You'll Learn in the AI Engineer Explorer Course•5 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
In this module, we will focus on Python fundamentals, providing the tools and techniques required to write effective Python code for AI applications. From setting up your development environment to solving AI problems, this module sets the groundwork for the rest of your AI journey.
What's included
7 videos1 assignment
7 videos•Total 193 minutes
- Day 1: Introduction to Python and Development Setup•21 minutes
- Day 2: Control Flow in Python•33 minutes
- Day 3: Functions and Modules•23 minutes
- Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)•31 minutes
- Day 5: Working with Strings•24 minutes
- Day 6: File Handling•23 minutes
- Day 7: Pythonic Code and Project Work•39 minutes
1 assignment•Total 15 minutes
- Python Programming Basics for Artificial Intelligence - Assessment•15 minutes
In this module, we will dive into the world of data science, covering tools like NumPy and Pandas to manipulate and clean datasets. You will also learn to visualize data effectively, which is essential for extracting actionable insights in AI.
What's included
7 videos1 assignment
7 videos•Total 154 minutes
- Day 1: Introduction to NumPy for Numerical Computing•23 minutes
- Day 2: Advanced NumPy Operations•22 minutes
- Day 3: Introduction to Pandas for Data Manipulation•20 minutes
- Day 4: Data Cleaning and Preparation with Pandas•24 minutes
- Day 5: Data Aggregation and Grouping in Pandas•15 minutes
- Day 6: Data Visualization with Matplotlib and Seaborn•27 minutes
- Day 7: Exploratory Data Analysis (EDA) Project•23 minutes
1 assignment•Total 15 minutes
- Data Science Essentials for Artificial Intelligence - Assessment•15 minutes
In this module, we will cover the essential mathematical concepts that form the foundation of machine learning and AI. From linear algebra to calculus and probability, this section equips you with the tools to understand and build powerful AI algorithms.
What's included
7 videos1 assignment
7 videos•Total 135 minutes
- Day 1: Linear Algebra Fundamentals•21 minutes
- Day 2: Advanced Linear Algebra Concepts•20 minutes
- Day 3: Calculus for Machine Learning (Derivatives)•18 minutes
- Day 4: Calculus for Machine Learning (Integrals and Optimization)•16 minutes
- Day 5: Probability Theory and Distributions•25 minutes
- Day 6: Statistics Fundamentals•19 minutes
- Day 7: Math-Driven Mini Project – Linear Regression from Scratch•15 minutes
1 assignment•Total 15 minutes
- Mathematics for Machine Learning and Artificial Intelligence - Assessment•15 minutes
In this module, we will explore the critical concepts of probability and statistics, which are essential for understanding uncertainty, making predictions, and analyzing data in machine learning and AI applications.
What's included
7 videos1 assignment
7 videos•Total 124 minutes
- Day 1: Probability Theory and Random Variables•19 minutes
- Day 2: Probability Distributions in Machine Learning•17 minutes
- Day 3: Statistical Inference – Estimation and Confidence Intervals•16 minutes
- Day 4: Hypothesis Testing and P-Values•12 minutes
- Day 5: Types of Hypothesis Tests•19 minutes
- Day 6: Correlation and Regression Analysis•17 minutes
- Day 7: Statistical Analysis Project – Analyzing Real-World Data•25 minutes
1 assignment•Total 15 minutes
- Probability and Statistics for Machine Learning and Artificial Intelligence - Assessment•15 minutes
In this module, we will introduce you to machine learning, from foundational concepts to specific techniques like supervised learning. You will also gain hands-on experience with model evaluation, applying these skills to real-world AI challenges.
What's included
7 videos3 assignments
7 videos•Total 150 minutes
- Day 1: Machine Learning Basics and Terminology•16 minutes
- Day 2: Introduction to Supervised Learning and Regression Models•16 minutes
- Day 3: Advanced Regression Models – Polynomial Regression and Regularization•35 minutes
- Day 4: Introduction to Classification and Logistic Regression•24 minutes
- Day 5: Model Evaluation and Cross-Validation•16 minutes
- Day 6: k-Nearest Neighbors (k-NN) Algorithm•17 minutes
- Day 7: Supervised Learning Mini Project•25 minutes
3 assignments•Total 90 minutes
- Full Course Practice Assessment•15 minutes
- Introduction to Machine Learning - Assessment•15 minutes
- Full Course Assessment•60 minutes
Instructor
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Frequently asked questions
The AI Engineer Explorer Course is designed to teach foundational skills required for a career in AI engineering. The course covers Python programming, data science, mathematics, machine learning, and statistical analysis. With artificial intelligence becoming increasingly important across industries, this course provides essential skills to understand and apply AI techniques in real-world scenarios, making it highly relevant for those looking to enter or advance in the field of AI engineering.
This course provides a comprehensive introduction to key concepts in artificial intelligence, focusing on Python programming, data science essentials, and machine learning. Throughout the course, you will learn how to program in Python, work with data using tools like Pandas and NumPy, understand the mathematics behind machine learning algorithms, and apply machine learning models to real-world problems. It’s structured to ensure you gain both theoretical knowledge and hands-on experience to kickstart your career in AI.
After completing the AI Engineer Explorer Course, you will be equipped with the skills to program in Python, manipulate and analyze data, understand mathematical foundations of machine learning, and implement machine learning algorithms. You will also be able to handle real-world data, perform exploratory data analysis, and build predictive models using various machine learning techniques. These skills will enable you to pursue AI engineering roles or continue to specialize further in the field.
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