AI Programming with Python

About Course

Learn the basics of Python and dive into building real-world AI solutions. Master essential libraries like NumPy, Pandas, and scikit-learn, and kickstart your journey into artificial intelligence.

Write clean and efficient Python code for AI applications
Understand key mathematical concepts behind AI and machine learning
Manipulate and analyze data using libraries like NumPy and Pandas
Visualize data insights with Matplotlib and Seaborn
Build and train machine learning models with scikit-learn
Understand the basics of neural networks and deep learning
Apply AI techniques to real-world projects and datasets
Prepare for careers in AI development, data science, and machine learning engineering

Course Content

Module 1: Introduction to Python
Python installation and setup Variables, data types, and operators Control flow and loops Functions and modules Object-Oriented Programming basics

Module 2: Mathematics for AI
Linear algebra essentials Probability and statistics Calculus basics for machine learning

Module 3: Essential Python Libraries
NumPy: Numerical operations Pandas: Data manipulation and analysis Matplotlib & Seaborn: Data visualization

Module 4: Introduction to Machine Learning
What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Machine Learning workflow

Module 5: Machine Learning with scikit-learn
Data preprocessing Model selection and evaluation Regression algorithms Classification algorithms Clustering algorithms

Module 6: Neural Networks Basics
Introduction to Neural Networks Perceptron model Forward and backward propagation Building simple neural networks with Python

Module 7: Hands-on Projects
Build a movie recommendation system Predict house prices using regression Classify handwritten digits (MNIST dataset)

Module 8: Final Assessment
Capstone project Certification of completion

AI Programming with Python

About Course

Related

What Will You Learn?

Course Content

Module 1: Introduction to Python
Python installation and setup Variables, data types, and operators Control flow and loops Functions and modules Object-Oriented Programming basics

Module 2: Mathematics for AI
Linear algebra essentials Probability and statistics Calculus basics for machine learning

Module 3: Essential Python Libraries
NumPy: Numerical operations Pandas: Data manipulation and analysis Matplotlib & Seaborn: Data visualization

Module 4: Introduction to Machine Learning
What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Machine Learning workflow

Module 5: Machine Learning with scikit-learn
Data preprocessing Model selection and evaluation Regression algorithms Classification algorithms Clustering algorithms

Module 6: Neural Networks Basics
Introduction to Neural Networks Perceptron model Forward and backward propagation Building simple neural networks with Python

Module 7: Hands-on Projects
Build a movie recommendation system Predict house prices using regression Classify handwritten digits (MNIST dataset)

Module 8: Final Assessment
Capstone project Certification of completion

Trending Courses

Trending College Workshops

Company

Contact

Categories

About Course

Related

What Will You Learn?

Course Content

Module 1: Introduction to Python Python installation and setup Variables, data types, and operators Control flow and loops Functions and modules Object-Oriented Programming basics

Module 2: Mathematics for AI Linear algebra essentials Probability and statistics Calculus basics for machine learning

Module 3: Essential Python Libraries NumPy: Numerical operations Pandas: Data manipulation and analysis Matplotlib & Seaborn: Data visualization

Module 4: Introduction to Machine Learning What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Machine Learning workflow

Module 5: Machine Learning with scikit-learn Data preprocessing Model selection and evaluation Regression algorithms Classification algorithms Clustering algorithms

Module 6: Neural Networks Basics Introduction to Neural Networks Perceptron model Forward and backward propagation Building simple neural networks with Python

Module 7: Hands-on Projects Build a movie recommendation system Predict house prices using regression Classify handwritten digits (MNIST dataset)

Module 8: Final Assessment Capstone project Certification of completion

Trending Courses

Trending College Workshops

Company

Contact

Categories

Module 1: Introduction to Python
Python installation and setup Variables, data types, and operators Control flow and loops Functions and modules Object-Oriented Programming basics

Module 2: Mathematics for AI
Linear algebra essentials Probability and statistics Calculus basics for machine learning

Module 3: Essential Python Libraries
NumPy: Numerical operations Pandas: Data manipulation and analysis Matplotlib & Seaborn: Data visualization

Module 4: Introduction to Machine Learning
What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Machine Learning workflow

Module 5: Machine Learning with scikit-learn
Data preprocessing Model selection and evaluation Regression algorithms Classification algorithms Clustering algorithms

Module 6: Neural Networks Basics
Introduction to Neural Networks Perceptron model Forward and backward propagation Building simple neural networks with Python

Module 7: Hands-on Projects
Build a movie recommendation system Predict house prices using regression Classify handwritten digits (MNIST dataset)

Module 8: Final Assessment
Capstone project Certification of completion