Mastering Artificial Intelligence (AI) with Python and R

Mastering Artificial Intelligence (AI) with Python and R, available at $54.99, has an average rating of 4.79, with 357 lectures, based on 7 reviews, and has 2023 subscribers.

You will learn about Foundational Skills: Master Python and R programming for AI and ML applications. Data Handling: Efficiently manage and manipulate data using libraries like NumPy and pandas. Visualization: Create insightful visualizations with Matplotlib and Seaborn. Machine Learning: Implement algorithms for classification, regression, clustering, and more. Advanced Techniques: Dive into neural networks, natural language processing, and predictive analytics. Real-world Applications: Apply skills to solve practical problems like predictive analysis and market basket analysis. Tools Mastery: Gain proficiency in tools like Anaconda, Jupyter Notebook, and RStudio for seamless development. This course is ideal for individuals who are Beginners in Programming: Those who want to learn artificial intelligence and machine learning starting from the basics. or Students and Professionals: Individuals pursuing careers or studies in data science, artificial intelligence, or related fields. or Enthusiasts: Anyone curious about the applications and concepts of AI and ML, looking to build foundational knowledge. or Programmers Switching Careers: Developers transitioning into AI and ML roles who need to solidify their understanding and skills. or Anyone Interested: Individuals keen on understanding the fundamentals and practical applications of artificial intelligence and machine learning using Python and R. It is particularly useful for Beginners in Programming: Those who want to learn artificial intelligence and machine learning starting from the basics. or Students and Professionals: Individuals pursuing careers or studies in data science, artificial intelligence, or related fields. or Enthusiasts: Anyone curious about the applications and concepts of AI and ML, looking to build foundational knowledge. or Programmers Switching Careers: Developers transitioning into AI and ML roles who need to solidify their understanding and skills. or Anyone Interested: Individuals keen on understanding the fundamentals and practical applications of artificial intelligence and machine learning using Python and R.

Enroll now: Mastering Artificial Intelligence (AI) with Python and R

Summary

Title: Mastering Artificial Intelligence (AI) with Python and R

Price: $54.99

Average Rating: 4.79

Number of Lectures: 357

Number of Published Lectures: 357

Number of Curriculum Items: 357

Number of Published Curriculum Objects: 357

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

Foundational Skills: Master Python and R programming for AI and ML applications.

Data Handling: Efficiently manage and manipulate data using libraries like NumPy and pandas.

Visualization: Create insightful visualizations with Matplotlib and Seaborn.

Machine Learning: Implement algorithms for classification, regression, clustering, and more.

Advanced Techniques: Dive into neural networks, natural language processing, and predictive analytics.

Real-world Applications: Apply skills to solve practical problems like predictive analysis and market basket analysis.

Tools Mastery: Gain proficiency in tools like Anaconda, Jupyter Notebook, and RStudio for seamless development.

Who Should Attend

Beginners in Programming: Those who want to learn artificial intelligence and machine learning starting from the basics.

Students and Professionals: Individuals pursuing careers or studies in data science, artificial intelligence, or related fields.

Enthusiasts: Anyone curious about the applications and concepts of AI and ML, looking to build foundational knowledge.

Programmers Switching Careers: Developers transitioning into AI and ML roles who need to solidify their understanding and skills.

Anyone Interested: Individuals keen on understanding the fundamentals and practical applications of artificial intelligence and machine learning using Python and R.

Target Audiences

Beginners in Programming: Those who want to learn artificial intelligence and machine learning starting from the basics.

Students and Professionals: Individuals pursuing careers or studies in data science, artificial intelligence, or related fields.

Enthusiasts: Anyone curious about the applications and concepts of AI and ML, looking to build foundational knowledge.

Programmers Switching Careers: Developers transitioning into AI and ML roles who need to solidify their understanding and skills.

Anyone Interested: Individuals keen on understanding the fundamentals and practical applications of artificial intelligence and machine learning using Python and R.

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.

Course Curriculum

Chapter 1: Artificial Intelligence with Python – Beginner Level

Lecture 1: Artificial Intelligence Overview

Lecture 2: Download Anaconda Navigator

Lecture 3: Set up and Installation

Lecture 4: Numpy in Jupyter Notebook

Lecture 5: Array Function

Lecture 6: Numpy indexing and Selection

Lecture 7: Filter Function

Lecture 8: Python Libraries for Visualization

Lecture 9: Python Libraries for Visualization Continued

Lecture 10: Matpotlib Library and its Users

Lecture 11: Matpotlib Library and its Users Continued

Lecture 12: Plotting of Data

Lecture 13: Seaborn Package for Visualization

Lecture 14: Seaborn Package for Visualization Continued

Lecture 15: Scatter Plots

Lecture 16: Scatter Plots Continued

Lecture 17: Seaborn Libraries and its Implication

Chapter 2: Artificial Intelligence with Python – Intermediate Level

Lecture 1: Introduction to Course

Lecture 2: Python for AI

Lecture 3: What is Machin Learning

Lecture 4: Data Processing Effort

Lecture 5: What is Meaning of Bias

Lecture 6: Bias vs Variance Tradeoff

Lecture 7: Model Evolution

Lecture 8: Scikit Learn

Lecture 9: Loading the Data

Lecture 10: Checking the Visualization

Lecture 11: Predict

Lecture 12: Data Values

Lecture 13: Applying Dimensionality Reduction

Lecture 14: Model Selection

Lecture 15: Kneibhbors Classifier

Lecture 16: Accuracy of Classifier

Lecture 17: ML Classification Handson

Lecture 18: Statistical Analysis of the Dataset

Lecture 19: Import Label Encoder

Lecture 20: Accuracy Score

Lecture 21: Multilayer Perceptron

Lecture 22: Multilayer Perceptron Continued

Lecture 23: Number of Clusters

Lecture 24: Multiple Method

Lecture 25: Keras-Pytorch and Tensorflow

Lecture 26: Working on Jupyter Notebook

Lecture 27: Binary Classification

Lecture 28: Checking the Visualization

Lecture 29: Pyplot

Chapter 3: AI Artificial Intelligence – Predictive Analysis with Python

Lecture 1: Introduction to Predictive Analysis

Lecture 2: Random Forest and Extremely Random Forest

Lecture 3: Dealing with Class Imbalance

Lecture 4: Grid Search

Lecture 5: Adaboost Regressor

Lecture 6: Predicting Traffic Using Extremely Random Forest Regressor

Lecture 7: Traffic Prediction

Lecture 8: Detecting patterns with Unsupervised Learning

Lecture 9: Clustering

Lecture 10: Clustering Meanshift

Lecture 11: Clustering Meanshift Continues

Lecture 12: Affinity Propagation Model

Lecture 13: Affinity Propagation Model Continues

Lecture 14: Clustering Quality

Lecture 15: Program of Clustering Quality

Lecture 16: Gaussian Mixture Model

Lecture 17: Program of Gaussian Mixture Model

Lecture 18: Classification in Artificial Intelligence

Lecture 19: Processing Data

Lecture 20: Logistic Regression Classifier

Lecture 21: Logistic Regression Classifier Example Using Python

Lecture 22: Naive Bayes Classifier and its Examples

Lecture 23: Confusion Matrix

Lecture 24: Example os Confusion Matrix

Lecture 25: Support Vector Machines Classifier(SVM)

Lecture 26: SVM Classifier Examples

Lecture 27: Concept of Logic Programming

Lecture 28: Matching the Mathematical Expression

Lecture 29: Parsing Family Tree and its Example

Lecture 30: Analyzing Geography Logic Programming

Lecture 31: Puzzle Solver and its Example

Lecture 32: What is Heuristic Search

Lecture 33: Local Search Technique

Lecture 34: Constraint Satisfaction Problem

Lecture 35: Region Coloring Problem

Lecture 36: Building Maze

Lecture 37: Puzzle Solver

Lecture 38: Natural Language Processing

Lecture 39: Examine Text Using NLTK

Lecture 40: Raw Text Accessing (Tokenization)

Lecture 41: NLP Pipeline and Its Example

Lecture 42: Regular Expression with NLTK

Lecture 43: Stemming

Lecture 44: Lemmatization

Lecture 45: Segmentation

Lecture 46: Segmentation Example

Lecture 47: Segmentation Example Continues

Lecture 48: Information Extraction

Lecture 49: Tag Patterns

Lecture 50: Chunking

Lecture 51: Representation of Chunks

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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