Prerequisites

Statistics

Probability

Linear Algebra

Calculus

Programming Skills

Data Handling

Notebook

Applications of
Machine Learning

Computer Vision (image classification, object detection, segmentation)

Natural Language Processing (text classification, sentiment analysis, machine translation)

Recommendation Systems (collaborative filtering, content-based filtering)

Anomaly Detection (fraud detection, network intrusion detection)

Predictive Analytics (sales forecasting, customer churn prediction)

Speech Recognition and Generation

Robotics and Control Systems

Bioinformatics and Computational Biology

Unsupervised
Learning

Concepts:

  1. Clustering

  2. Dimensionality Reduction

  3. Anomaly Detection

  4. Unlabeled Data

  5. Feature Engineering

Functions:

  1. Isolation Forest

  2. One-Class SVM

Data Preprocessin-
g and Feature
Engineering

Data Preprocessing

  1. Feature Engineering

Model Training, Validation, and Testing

  1. Model Training

  2. Model Validation

  3. Model Testing

Hyperparameter Tuning

  1. Hyperparameter Tuning

Model Evaluation Metrics

  1. Model Evaluation Metrics

Basics of Machine
Learning

What is Artificial Intelligence (AI)?

  1. What is Machine Learning?

  2. Types of Problems in Machine Learning

Semi-Supervised
Learning

Concepts:



Functions:


Computer Vision

Image Classification

Object Detection

Semantic Segmentation

Instance Segmentation

Pose Estimation

Image Segmentation

Image Recognition

Image Generation

Image Super-Resolution

Image Denoising

Image Registration

Optical Character Recognition (OCR)

Video Analysis

3D Computer Vision

Machine Learning
Theory and
Concepts

Bias-Variance Tradeoff

  1. Bias

  2. Underfitting

  3. High Bias

  4. Simplistic Models

  5. Variance

  6. Overfitting

  7. High Variance

  8. Complex Models

  9. Bias-Variance Tradeoff

  10. Balancing Bias and Variance

  11. Model Complexity and Performance

Optimization Algorithms

  1. Gradient Descent

  2. Batch Gradient Descent

  3. Stochastic Gradient Descent

  4. Mini-Batch Gradient Descent

  5. Momentum

  6. Accelerating Gradient Descent

  7. Damping Oscillations

  8. Adaptive Optimization Algorithms

  9. AdaGrad

  10. RMSProp

  11. Adam

  12. Constrained Optimization

  13. Lagrange Multipliers

  14. Quadratic Programming

Machine Learning
Algorithms

Instance-based Algorithms (e.g. K-Nearest Neighbors)

Regression Algorithms (e.g. Linear Regression, Logistic Regression)

Tree-based Algorithms (e.g. Decision Trees, Random Forest)

Ensemble Methods (e.g. Bagging, Boosting)

Neural Networks and Deep Learning Algorithms

Clustering Algorithms (e.g. K-Means, DBSCAN)

Dimensionality Reduction Algorithms (e.g. PCA, t-SNE)

Supervised
Learning

Concepts:

  1. Classification

  2. Regression

  3. Labeled Data

  4. Training, Validation, and Test Sets

  5. Overfitting and Underfitting

  6. Bias-Variance Tradeoff

Functions:

  1. Logistic Regression

  2. Linear Regression

  3. Decision Trees

  4. Random Forests

  5. Support Vector Machines (SVMs)

  6. K-Nearest Neighbors (KNN)

  7. Naive Bayes Classifier

  8. Neural Networks

Natural Language
Processing (NLP)

Language Models

  1. N-gram Models

  2. Text Preprocessing

  3. Tokenization

  4. Stopword Removal

  5. Stemming and Lemmatization

  6. Text Normalization

  7. Text Classification

  8. Topic Classification

  9. Spam Detection

  10. Intent Classification

  11. Named Entity Recognition (NER)

  12. Sequence Labeling

  13. Conditional Random Fields (CRFs)

  14. Machine Translation

  15. Statistical Machine Translation

  16. Neural Machine Translation

  17. Encoder-Decoder Models

  18. Sequence-to-Sequence Learning

  19. Language Generation

  20. Text Summarization

  21. Dialogue Systems

  22. Chatbots

  23. Word Embeddings

  24. GloVe

  25. FastText

  26. Contextualized Word Embeddings (ELMo, BERT)

  27. Information Retrieval

  28. Machine Comprehension

  29. Speech Recognition

  30. Hidden Markov Models

  31. Gaussian Mixture Models

  32. Deep Neural Networks for Speech

  33. Automatic Speech Recognition (ASR) Systems

  34. Statistical NLG

  35. Neural NLG

  36. Data-to-Text Generation

  37. Data Augmentation Techniques

  38. Bias and Fairness

  39. Explainability and Interpretability

  40. Responsible AI Practices

Machine Learning
Libraries and
Frameworks

Python

  1. scikit-learn

  2. TensorFlow

  3. PyTorch

Java

Apache Spark MLlib