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Ai Engineer Explorer Certificate Course - AD-TEAM - 07-31-2025
![[Image: 70e82b106ade9b9bd61d8e07badb73cd.jpg]](https://i125.fastpic.org/big/2025/0731/cd/70e82b106ade9b9bd61d8e07badb73cd.jpg)
Ai Engineer Explorer Certificate Course
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.37 GB | Duration: 12h 42m
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
![[Image: kDeoTaUJ_o.jpg]](https://images2.imgbox.com/ea/3d/kDeoTaUJ_o.jpg)
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