Description
Introduction of AI & ML with Python
Artificial Intelligence (AI) and Machine Learning (ML) have transformed industries by enabling data-driven predictions and insightful visualizations. This course focuses on leveraging Python’s libraries and frameworks to build predictive models, visualize results, and extract actionable insights from data. You’ll learn how to apply AI and ML techniques to real-world scenarios, from data preprocessing to model evaluation and visualization of predictions.
Prerequisites
- Basic understanding of Python programming
- Familiarity with statistics and data analysis concepts
- Basic knowledge of machine learning principles
Table of Contents
- Introduction to AI & ML with Python
1.1 What is AI and Machine Learning?
1.2 Key Concepts in AI & ML
1.3 Python Libraries for AI & ML (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras)
1.4 Overview of the AI/ML Workflow - Data Preprocessing for Machine Learning
2.1 Collecting and Cleaning Data
2.2 Handling Missing Data and Outliers
2.3 Feature Engineering and Selection
2.4 Scaling and Normalizing Data
2.5 Splitting Data into Training and Testing Sets - Supervised Learning: Predicting with Labels
3.1 Introduction to Supervised Learning
3.2 Linear Regression for Predictive Modeling
3.3 Classification Algorithms: Logistic Regression, SVM, KNN
3.4 Decision Trees and Random Forests
3.5 Model Evaluation: Accuracy, Precision, Recall, F1-Score
3.6 Hyperparameter Tuning and Cross-Validation - Unsupervised Learning: Discovering Patterns in Data
4.1 Introduction to Unsupervised Learning
4.2 Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN
4.3 Dimensionality Reduction with PCA (Principal Component Analysis)
4.4 Anomaly Detection Techniques(Ref: Terraform for DevOps: Implementing Infrastructure-as-Code Best Practices)
4.5 Visualizing Clusters and Patterns - Advanced Machine Learning Techniques
5.1 Ensemble Learning: Bagging, Boosting, and Stacking
5.2 Support Vector Machines (SVM) for Classification
5.3 Neural Networks for Predictive Modeling
5.4 Introduction to Deep Learning with Keras and TensorFlow
5.5 Transfer Learning and Fine-Tuning Pre-Trained Models - Model Evaluation and Improvement
6.1 Evaluating Model Performance with Confusion Matrix and ROC Curve
6.2 Feature Importance and Interpretability
6.3 Model Comparison and Selection
6.4 Dealing with Overfitting and Underfitting
6.5 Continuous Model Improvement with Feedback Loops - Data Visualization for AI & ML
7.1 Introduction to Data Visualization Tools
7.2 Visualizing Data with Matplotlib and Seaborn
7.3 Plotting Machine Learning Models and Predictions
7.4 Visualizing Model Performance: ROC, Precision-Recall Curves
7.5 Creating Interactive Visualizations with Plotly and Dash
7.6 Heatmaps, Correlation Matrices, and Feature Importance Plots - AI & ML for Time Series Predictions
8.1 Introduction to Time Series Analysis
8.2 Data Preprocessing for Time Series
8.3 Forecasting with ARIMA and SARIMA Models
8.4 Machine Learning Models for Time Series (LSTM Networks)
8.5 Visualizing Time Series Predictions and Trends - Natural Language Processing (NLP) for Text Predictions
9.1 Introduction to NLP and Text Mining
9.2 Preprocessing Text Data (Tokenization, Lemmatization)
9.3 Sentiment Analysis with Machine Learning
9.4 Text Classification and Clustering
9.5 Visualizing Word Frequencies and Topics - Deploying Machine Learning Models for Predictions
10.1 Overview of Model Deployment
10.2 Exporting Models with Pickle and Joblib
10.3 Deploying Models with Flask or Django for Web Applications
10.4 Introduction to Cloud-based ML Deployment (AWS, Azure, GCP)
10.5 Real-Time Predictions with ML APIs - Capstone Project: Building and Visualizing a Predictive Model
11.1 Defining the Problem and Dataset
11.2 Data Collection, Cleaning, and Preprocessing
11.3 Model Building and Evaluation
11.4 Visualization of Results and Insights
11.5 Deploying the Final Model for Predictions - Conclusion
12.1 Recap of Key AI & ML Concepts
12.2 Practical Applications of AI & ML in Real-World Scenarios
12.3 Next Steps: Advanced Topics and Continuous Learning
12.4 Resources for Further Exploration
Conclusion
By mastering AI and ML with Python, you will be equipped to build robust predictive models and extract meaningful insights through data visualization. From preprocessing data to evaluating model performance and deploying predictions, this course provides a comprehensive approach to leveraging Python’s rich ecosystem for AI/ML tasks. By integrating these skills, you can address complex business challenges and make data-driven decisions that enhance operational efficiency and drive growth.
