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Description
Welcome to the comprehensive course on Artificial Intelligence (AI) with Python. This course is designed to equip you with the essential skills and knowledge needed to dive into the exciting world of AI, machine learning, and data science using Python programming language.
Overview: Artificial Intelligence is revolutionizing industries worldwide, from healthcare to finance, transportation to entertainment. Python, with its robust libraries and intuitive syntax, has emerged as a powerhouse for AI applications, making it the go-to choice for developers and data scientists alike.
What You’ll Learn: Throughout this course, you will embark on a journey that covers everything from foundational concepts to advanced techniques in AI and machine learning. Starting from the basics of Python programming, we’ll gradually delve into NumPy for numerical computing, Matplotlib and Seaborn for data visualization, and Scikit-learn for implementing machine learning algorithms.
Section 1: Artificial Intelligence with Python – Beginner Level
This section provides a foundational understanding of Artificial Intelligence (AI) using Python, aimed at beginners. It starts with an introduction to the course objectives, emphasizing practical applications in data science and machine learning. Students are guided through setting up their development environment with Anaconda Navigator and essential Python libraries. The focus then shifts to NumPy, a fundamental library for numerical computing, covering array functions, indexing, and selection. Additionally, students learn about Python libraries like Matplotlib and Seaborn for data visualization, essential for interpreting and presenting data effectively.
Section 2: Artificial Intelligence with Python – Intermediate Level
Building upon the basics, this intermediate-level section delves deeper into Python for AI applications. It begins with an overview of Python’s role in machine learning, followed by discussions on data processing, bias vs. variance tradeoff, and model evaluation techniques. Students explore Scikit-learn for machine learning tasks, including data loading, visualization, and applying dimensionality reduction methods like Principal Component Analysis (PCA). The section also covers popular classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), enhancing students’ ability to build and evaluate machine learning models.
Section 3: AI Artificial Intelligence – Predictive Analysis with Python
Focused on predictive analysis, this section introduces advanced AI techniques using Python. Topics include ensemble methods like Random Forest and AdaBoost, handling class imbalance, and grid search for hyperparameter tuning. Students apply these techniques to real-world scenarios, such as traffic prediction using regression models. Unsupervised learning methods like clustering (e.g., K-Means, Affinity Propagation) are also explored for detecting patterns in data without labeled outcomes. The section concludes with examples of classification tasks using algorithms like Logistic Regression, Naive Bayes, and Support Vector Machines (SVM).
Section 4: Artificial Intelligence and Machine Learning Training Course
This comprehensive section covers foundational AI concepts and algorithms essential for understanding intelligent agents, state space search, and heuristic search techniques. Topics include various search algorithms like BFS, DFS, and iterative deepening, along with heuristic approaches such as A* and hill climbing. Machine learning principles are introduced, including the Perceptron algorithm, backpropagation for neural networks, and classification using decision trees and rule-based systems like Prolog and CLIPS. The section prepares students for practical implementation through examples and hands-on exercises.
Section 5: Machine Learning with R
Dedicated to machine learning using R, this section begins with an introduction to R’s capabilities for data manipulation and analysis. Topics include regression and classification problems, data visualization techniques, and implementing machine learning models like K-Nearest Neighbors (KNN) and Decision Trees. Students learn about model evaluation metrics, cross-validation techniques, and ensemble learning methods such as Random Forest and AdaBoost. The section emphasizes practical applications through examples and case studies, preparing students to leverage R for predictive analytics tasks.
Section 6: Logistic Regression & Supervised Machine Learning in Python
Focused specifically on logistic regression and supervised learning techniques in Python, this section covers the machine learning lifecycle from data preprocessing to model evaluation. Topics include exploratory data analysis (EDA), feature selection, and model training using algorithms like Decision Trees and logistic regression. Students gain hands-on experience in building and optimizing predictive models, understanding key metrics like accuracy, precision, and recall. Cross-validation techniques are also explored to ensure robust model performance.
Section 7: Project on R – Card Purchase Prediction
The final section offers a practical project using R for predictive analytics. Students work on predicting card purchases based on customer data, starting with dataset exploration and variable analysis. They build logistic regression and decision tree models, evaluating performance metrics like ROC curves and lift charts. The project emphasizes model interpretation and optimization, culminating in the deployment of a predictive model for real-world applications.
These sections collectively provide a comprehensive journey through artificial intelligence and machine learning concepts, supported by practical examples and hands-on projects to reinforce learning outcomes.
| Total Students | 1276 |
|---|---|
| Original Price($) | |
| Sale Price | Free |
| Number of lectures | 357 |
| Number of quizzes | 0 |
| Total Reviews | 1 |
| Global Rating | 5 |
| Instructor Name | EDUCBA Bridging the Gap |
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