AI ML DS - How To Get Started?
Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS) are three interrelated fields in computer science and statistics. AI focuses on creating intelligent systems, ML enables computers to learn from data and make predictions, and DS leverages data to extract insights and drive decision-making. These three fields often overlap and complement each other in solving real-world problems and advancing technology.
Data Science
Data Science combines statistical and computational tools to process and analyze large amounts of data. DS practitioners use their insights from data to inform decisions, predict trends, and improve the effectiveness of processes.
Prerequisites for Data Science
- Maths for Data Science
- Statistics for Data Science
- Linear Algebra for Data Science
- Calculus for Data Science
Important Libraries
Data Analysis
- Data Cleaning
- Handling Missing Data
- Outlier Detection
- Exploratory Data Analysis (EDA)
- Statistical Analysis
- Time Series Analysis
Data Visualization
- Data Visualization using Matplotlib
- Data Visualization using Seaborn
- Data Visualization using Plotly
- Data Visualization using Bokeh
- Power BI Tutorial
- Tableau Tutorial
Machine Learning
Machine Learning is a subset of AI focused on building systems that learn from data, identify patterns, and make decisions with minimal human intervention. ML algorithms improve their performance as the amount of data they're exposed to increases.
Supervised Machine Learning
In supervised machine learning, the model is trained on the label data to predict outcomes for new or unseen data.
- Linear Regression
- Logistic Regression
- Support Vector Machine (SVM)
- Decision Trees
- Random Forest
- Naive Bayes
- K-Nearest Neigur (KNN)
- XGBoost
Semi-Supervised Learning uses a small amount of labeled data and a large amount of unlabeled data to improve learning accuracy.
Unsupervised Machine Learning
In unsupervised machine learning, the model learns patters and structures from unlabeled data without defined output labels.
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Principal Component Analysis (PCA)
- Independent Component Analysis (ICA)
- Gaussian Mixture Models (GMM)
- t-SNE (t-Distributed Stochastic Neigr Embedding)
Reinforcement Learning
In reinforcement learning, agent learns to make decisions by interacting with an environment and receiving rewards or penalties based on its actions.
- Q Learning
- SARSA (State-Action-Reward-State-Action)
- REINFORCE Algorithm
- Actor-Critic Method
- Proximal Policy Optimization (PPO)
To learn more about machine learning, you can follow this tutorial: Machine Learning Tutorial
Deep Learning
Deep learning is a specialized area within ML. Deep learning uses neural networks with many layers (deep neural networks) to automatically learn features and representations from large datasets.
- Perceptron
- Multi-Layer Perceptron
- Artificial Neural Network
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Networks (LSTMs)
- Generative Adversarial Networks (GANs)
To learn more about deep learning, you can follow this tutorial: Deep Learning Tutorial
Artificial Intelligence (AI)
Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior. It encompasses a broad range of technologies that enable machines to perceive, comprehend, act, and learn.
- Search Algorithms
- Optimization Algorithms
- Adversarial Search Algorithms
- Constraint Satisfaction Problems
- Knowledge Representation
- Reasoning
- Planning
- Uncertain Knowledge
- Robotics
To learn more, you can follow these tutorials:
