Intermediate
Introduction to AI and ML
AI course for beginners
Curriculum
- 11 Sections
- 57 Lessons
- 0m Duration
Introduction to AI and ML
4 Lessons
- Introduction to AI
- Introduction to Machine Learning
- Overview of AI and Machine Learning
- Overview of Machine Learning Workflow
Math ( Statics ) Foundation
2 Lessons
- Statistics for Machine Learning Engineers: A Comprehensive Tutorial
- Statistics and Math for AI/ML and Deep Neural Networks (DNNs)
Data Analysis
8 Lessons
- Introd Jupter
- Writing and Using Markdown in Jupyter Notebook
- VSCode and Jupternotebook
- Analyzing Numerical Data with NumPy
- Analyzing Data Using Pandas
- Visualization with Matplotlib
- Visualization Using Seaborn
- Exploratory Data Analysis
Machine Learning Fundamentals
14 Lessons
- what is Statistical Learning
- What is difference between Statistical Learning and Machine Learning
- Dataset Splitting: Training, Test, and Validation
- Regression vs. Classification Problems
- Supervised Learning: Linear Regression
- Advanced topic: Why Use Mean Squared Error (MSE) in Regression?
- Supervised Learning – Classification
- Logistic Regression
- What is the Likelihood Function for Logistic Regression (Logit)?
- Difference Between Logistic Regression and Linear Regression
- What are Non-Parametric Methods in Machine Learning?
- Naive Bayes Classifier: Detailed Explanation with Example
- What is Linear Discriminant Analysis (LDA)?
- K-Nearest Neighbors (KNN) Algorithm
Model evaluation
7 Lessons
- Model Evaluation: Performance Metrics and Error
- Understanding Bias and Variance
- Advanced topic: Understanding Variance More Deeply
- Overfitting, Underfitting
- False Positives, False Negatives, and AUC-ROC Curve
- Hypothesis Testing
- Advanced topic: Relationship Between Hypothesis Testing and Machine Learning
Resampling methods
8 Lessons
- How to Sample from a Probability Distribution
- Resampling Methods
- Why Do We Need Cross-Validation?
- Bootstrap
- Advanced topic: why sample 100 points even if we only have 100 points in the original data.
- How Bootstrap Helps in Machine Learning Model Evaluation
- Advanced topic: Bootstrap vs. Cross-Validation
- Dimension Reduction Methods
Advanced ML
3 Lessons
- Supervised Learning – Decision Trees and Random Forests
- Advanced topic: Tree-Based Methods
- Support Vector Machine (SVM)
unsupervisied learning
2 Lessons
- Unsupervised Learning: Clustering
- Discussion: can unsupervised method handle regression and classification problem?
Neural Networks and DNN
5 Lessons
- Neural Networks and Deep Learning
- Introduction to Neural Networks
- Backward Propagation (Backpropagation)
- Advanced topic: BP without closed math formula
- Deep Learning and Convolutional Neural Networks (CNNs)
Reenforcement Learning
1 Lesson
- Reinforcement Learning
AI in real world
3 Lessons
- Advanced AI Techniques
- AI in the Real World
- Practical AI Applications
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