Ai Engineer Explorer Certificate Course

Build Your AI Foundation with Python, Data Science, Math & Machine Learning Basics

What you'll learn

Write clean Python code for AI applications using variables, loops, functions, and OOP

Analyze and manipulate data with Pandas and NumPy

Visualize datasets using Matplotlib and Seaborn

Understand core math concepts like linear algebra and calculus for AI

Apply probability theory and statistics to AI problem-solving

Explain how machine learning models work and are trained

Build and evaluate basic ML models using Scikit-learn

Develop a solid foundation to pursue advanced AI and ML topics

Requirements

No prior programming or AI experience is required — this course is beginner-friendly

A computer (Windows, macOS, or Linux) with internet access

Willingness to learn and experiment with new concepts

Basic familiarity with high school math (algebra and arithmetic is helpful but not mandatory)

Ability to install software like Python, Jupyter Notebook, and required libraries (we’ll guide you step-by-step)

Curiosity about how AI works and a passion for problem-solving

A commitment to completing lessons and hands-on exercises

Optional: A notebook or digital note-taking tool to jot down key ideas and formulas

Description

Are you ready to take your first step into Artificial Intelligence and become an AI Engineer? The AI Engineer Explorer Certificate Course is your gateway into the exciting and fast-growing world of AI, Machine Learning, and Data Science. Designed for beginners, this hands-on course equips you with the foundational skills you need to start your journey toward becoming a skilled AI developer or AI product builder.In this course, you will begin with Python Programming Basics for AI. Python is the most popular programming language in the AI world today. You will learn how to write clean Python code, understand variables, loops, functions, and object-oriented programming—laying the groundwork for building real AI applications.Next, you’ll dive into Data Science Essentials for AI, where you will explore data preprocessing, data visualization, and exploratory data analysis (EDA) using tools like Pandas, NumPy, and Matplotlib. Understanding how to work with data is critical in AI, and this section ensures you gain practical, job-ready experience.Then, you’ll master the Mathematics for Machine Learning and AI—a core pillar for any serious AI professional. We break down the essentials of linear algebra, calculus, and matrix operations in a way that is intuitive and application-oriented, helping you build strong analytical thinking.You will also cover Probability and Statistics for Machine Learning, which is crucial for understanding how AI models learn from data. Topics include Bayes’ Theorem, distributions, standard deviation, confidence intervals, and hypothesis testing—all taught with AI-centric examples that make complex concepts feel natural.Finally, you'll step into the world of Machine Learning itself. In the Introduction to Machine Learning, you’ll learn how algorithms like linear regression, classification, and clustering work under the hood. You’ll also use Scikit-learn to train and evaluate simple ML models, gaining firsthand experience in how machine learning pipelines are built.By the end of the AI Engineer Explorer Certificate Course, you’ll have a strong grasp of the core concepts of AI, and be well-prepared to move into more advanced topics like Deep Learning, Natural Language Processing, and AI product development. Whether you're a student, software developer, career changer, or tech enthusiast, this course gives you a structured, easy-to-follow path to build your AI foundation.No prior experience requiredHands-on projects includedCertificate of CompletionIdeal for AI beginners, data science aspirants, and future AI product managersTake your first step into the future—join thousands of learners and start your journey to becoming a certified AI Engineer today.

Overview

Section 1: Introduction to Course and Instructor

Lecture 1 What You’ll Learn in the AI Engineer Explorer Certificate Course

Section 2: Python Programming Basics for Artificial Intelligence

Lecture 2 Day 1: Introduction to Python and Development Setup

Lecture 3 Day 2: Control Flow in Python

Lecture 4 Day 3: Functions and Modules

Lecture 5 Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)

Lecture 6 Day 5: Working with Strings

Lecture 7 Day 6: File Handling

Lecture 8 Day 7: Pythonic Code and Project Work

Section 3: Data Science Essentials for Artificial Intelligence

Lecture 9 Day 1: Introduction to NumPy for Numerical Computing

Lecture 10 Day 2: Advanced NumPy Operations

Lecture 11 Day 3: Introduction to Pandas for Data Manipulation

Lecture 12 Day 4: Data Cleaning and Preparation with Pandas

Lecture 13 Day 5: Data Aggregation and Grouping in Pandas

Lecture 14 Day 6: Data Visualization with Matplotlib and Seaborn

Lecture 15 Day 7: Exploratory Data Analysis (EDA) Project

Section 4: Mathematics for Machine Learning and Artificial Intelligence

Lecture 16 Day 1: Linear Algebra Fundamentals

Lecture 17 Day 2: Advanced Linear Algebra Concepts

Lecture 18 Day 3: Calculus for Machine Learning (Derivatives)

Lecture 19 Day 4: Calculus for Machine Learning (Integrals and Optimization)

Lecture 20 Day 5: Probability Theory and Distributions

Lecture 21 Day 6: Statistics Fundamentals

Lecture 22 Day 7: Math-Driven Mini Project – Linear Regression from Scratch

Section 5: Probability and Statistics for Machine Learning and Artificial Intelligence

Lecture 23 Day 1: Probability Theory and Random Variables

Lecture 24 Day 2: Probability Distributions in Machine Learning

Lecture 25 Day 3: Statistical Inference - Estimation and Confidence Intervals

Lecture 26 Day 4: Hypothesis Testing and P-Values

Lecture 27 Day 5: Types of Hypothesis Tests

Lecture 28 Day 6: Correlation and Regression Analysis

Lecture 29 Day 7: Statistical Analysis Project – Analyzing Real-World Data

Section 6: Introduction to Machine Learning

Lecture 30 Day 1: Machine Learning Basics and Terminology

Lecture 31 Day 2: Introduction to Supervised Learning and Regression Models

Lecture 32 Day 3: Advanced Regression Models – Polynomial Regression and Regularization

Lecture 33 Day 4: Introduction to Classification and Logistic Regression

Lecture 34 Day 5: Model Evaluation and Cross-Validation

Lecture 35 Day 6: k-Nearest Neighbors (k-NN) Algorithm

Lecture 36 Day 7: Supervised Learning Mini Project

Section 7: Congratulations

Lecture 37 Congratulations and Best of Luck

Aspiring AI Engineers and Data Scientists starting from scratch,Product Managers and Tech Leads looking to understand AI fundamentals,Students preparing for advanced AI or ML programs,Professionals transitioning into AI-focused roles,Anyone who wants to explore the world of AI without prior experience in coding or data science