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Fundamentals of AI, Machine Learning, and Python Programming

Курс от Packt

Начальный≈ 16 чАнглийский

О курсе

Updated in May 2025. This course now features Coursera Coach! 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. Embark on a transformative learning experience designed to equip you with a robust understanding of AI, machine learning, and Python programming. This course begins with a thorough introduction to artificial intelligence and machine learning, demystifying the core concepts and exploring how algorithms and data-driven techniques empower computers to learn and adapt. As you progress, you'll delve into the architecture of deep learning and neural networks, grasping how these advanced structures mimic human cognition to process complex data and make accurate predictions. Transitioning from theory to practical application, the course guides you through setting up your development environment with Anaconda, laying the groundwork for efficient coding and package management. You'll then immerse yourself in Python programming, mastering flow control mechanisms, data structures, and functions. The journey continues with an exploration of essential Python libraries such as NumPy, Matplotlib, and Pandas, providing you with the tools to handle data manipulation and visualization effectively. The latter part of the course focuses on advanced AI topics, including the installation and application of deep learning libraries like TensorFlow and PyTorch. You'll learn about the fundamental structures of artificial neurons and neural networks, and the crucial roles of activation functions, loss functions, and optimizers in training models. Through hands-on projects, such as building regression models for house price prediction and binary classification models for heart disease prediction, you'll apply your knowledge to real-world scenarios, reinforcing your learning and enhancing your practical skills. This course is designed for aspiring data scientists, machine learning enthusiasts, and Python programmers. It is ideal for beginners seeking a comprehensive introduction to AI and machine learning, as well as professionals looking to deepen their understanding of these technologies. Prerequisites include basic programming knowledge and a keen interest in artificial intelligence and data science.

Программа курса

30 модулей · 83 учебных материалов

01Introduction3 материалов

Introduction

Introduction to the SpecializationВидео Introduction to the Course 'Fundamentals of AI, Machine Learning, and Python Programming'Чтение Full Specialization ResourcesЧтение

02Introduction to AI and Machine Learning2 материалов

Introduction to AI and Machine Learning

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Packt - Course Instructors

Преподаватель курса

Introduction to AI and Machine LearningВидео

03Introduction to Deep learning and Neural Networks3 материалов

Introduction to Deep learning and Neural Networks

Introduction to Deep learning and Neural NetworksВидео Assessment 1Задание Understanding Artificial NeuronsDIALOGUE

04Setting Up Computer - Installing Anaconda2 материалов

Setting Up Computer - Installing Anaconda

Setting Up Computer - Installing AnacondaВидео Setting Up Your Python Development EnvironmentDIALOGUE

06Python Basics - Lists and Tuples3 материалов

Python Basics - Lists and Tuples

Python Basics - Lists and TuplesВидео Assessment 2Задание Understanding Tuples and Lists in PythonDIALOGUE

07Python Basics - Dictionaries and Functions3 материалов

Python Basics - Dictionaries and Functions

Python Basics - Dictionaries and Functions - part 1Видео Python Basics - Dictionary and Functions - part 2Видео Working with Python DictionariesDIALOGUE

11Installing Deep Learning Libraries2 материалов

Installing Deep Learning Libraries

Installing Deep Learning LibrariesВидео Installing and Managing Python Libraries for Deep LearningDIALOGUE

12Basic Structure of Artificial Neuron and Neural Network3 материалов

Basic Structure of Artificial Neuron and Neural Network

Basic Structure of Artificial Neuron and Neural NetworkВидео Assessment 4Задание Understanding Artificial Neural NetworksDIALOGUE

13Activation Functions Introduction2 материалов

Activation Functions Introduction

Activation Functions IntroductionВидео Understanding Activation Functions in Deep LearningDIALOGUE

14Popular Types of Activation Functions2 материалов

Popular Types of Activation Functions

15Popular Types of Loss Functions3 материалов

Popular Types of Loss Functions

16Popular Optimizers2 материалов

Popular Optimizers

17Popular Neural Network Types2 материалов

Popular Neural Network Types

18King County House Sales Regression Model - Step 1 Fetch and Load Dataset3 материалов

King County House Sales Regression Model - Step 1 Fetch and Load Dataset

King County House Sales Regression Model - Step 1 Fetch and Load DatasetВидео Assessment 6Задание Loading and Inspecting Data with PandasDIALOGUE

21Steps 5 and 6 - Compile and Fit Model3 материалов

Steps 5 and 6 - Compile and Fit Model

Steps 5 and 6 Compile and Fit ModelВидео Assessment 7Задание Compiling and Fitting a Regression ModelDIALOGUE

22Step 7 Visualize Training and Metrics2 материалов

Step 7 Visualize Training and Metrics

Step 7 Visualize Training and MetricsВидео Visualizing Regression Model Training with MatplotlibDIALOGUE

23Step 8 Prediction Using the Model2 материалов

Step 8 Prediction Using the Model

Step 8 Prediction Using the ModelВидео Predicting House Prices with a Pre-trained ModelDIALOGUE

24Heart Disease Binary Classification Model - Introduction3 материалов

Heart Disease Binary Classification Model - Introduction

Heart Disease Binary Classification Model - IntroductionВидео Assessment 8Задание Binary Classification with Neural NetworksDIALOGUE

25Step 1 - Fetch and Load Data2 материалов

Step 1 - Fetch and Load Data

Step 1 - Fetch and Load DataВидео Loading and Analyzing CSV Data with PandasDIALOGUE

27Step 4 - Defining the Model3 материалов

Step 4 - Defining the Model

Step 4 - Defining the ModelВидео Assessment 9Задание Building a Sequential Model for ClassificationDIALOGUE

28Step 5 - Compile, Fit, and Plot the Model2 материалов

Step 5 - Compile, Fit, and Plot the Model

Step 5 - Compile, Fit, and Plot the ModelВидео Compiling and Training a Keras ModelDIALOGUE

29Step 5 - Predicting Heart Disease Using Model2 материалов

Step 5 - Predicting Heart Disease Using Model

Step 5 - Predicting Heart Disease Using ModelВидео Interpreting Machine Learning Model PredictionsDIALOGUE