AI, ML, DS – How To Get Started- A Beginner's Guide | Week-by-Week Study Plan

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AI, ML, DS – How To Get Started- A Beginner's Guide | Week-by-Week Study Plan

AI, ML, DS – How To Get Started? A Beginner's Guide

Overview of the Fields

Artificial Intelligence (AI) is the broad field of building intelligent systems that simulate human behavior such as reasoning, learning, and decision-making.

Machine Learning (ML) is a subfield of AI that enables systems to learn patterns from data with minimal human intervention. ML powers many modern applications like recommendation engines, speech recognition, and fraud detection.

Data Science (DS) involves extracting actionable insights from structured and unstructured data through statistical, analytical, and computational techniques. It bridges the gap between data and decision-making.

Data Science Essentials

Prerequisites

  • Solid understanding of statistics, probability, and linear algebra
  • Familiarity with calculus, especially derivatives and optimization
  • Basic programming skills in Python

Key Tools & Libraries

  • Pandas: Data manipulation and analysis
  • NumPy: Numerical computations and arrays

Analysis Techniques

  • Data Cleaning & Missing Value Handling
  • Outlier Detection & Removal
  • Exploratory Data Analysis (EDA)
  • Time Series Analysis

Visualization Tools

  • Python Libraries: Matplotlib, Seaborn, Plotly, Bokeh
  • BI Tools: Power BI, Tableau

Machine Learning Breakdown

Types of Learning

  • Supervised Learning: Learn from labeled data (e.g., Linear/Logistic Regression, Random Forest, SVM)
  • Unsupervised Learning: Discover patterns in unlabeled data (e.g., K-Means, PCA, GMM)
  • Semi-Supervised Learning: Combines a small amount of labeled data with a large amount of unlabeled data
  • Reinforcement Learning: Agents learn via rewards (e.g., Q-Learning, PPO)

Deep Learning Concepts

Deep Learning is a specialized area of ML that uses neural networks with multiple layers to model complex patterns in data.

Core Models

  • Convolutional Neural Networks (CNNs) – for image data
  • Recurrent Neural Networks (RNNs) – for sequential data
  • Long Short-Term Memory Networks (LSTMs) – for long-term dependencies
  • Generative Adversarial Networks (GANs) – for data generation

AI Techniques and Applications

  • Algorithms: Search & Optimization, Adversarial Search
  • Problem Solving: Planning, Reasoning, Constraint Satisfaction
  • Applications: Robotics, Knowledge Representation under Uncertainty

Week-by-Week Study Plan

Week 1–2: Foundation Building

  • Math: Mean, median, standard deviation, matrix operations, eigenvalues, derivatives
  • Python Programming: Variables, functions, and basic libraries like Pandas and NumPy

Week 3–5: Data Science Basics

  • Data Handling: Cleaning, missing values, outlier treatment
  • EDA: Identify trends and distributions
  • Visualization: Practice with Matplotlib, Seaborn, Plotly

Week 6–8: Machine Learning Essentials

  • Supervised: Linear/Logistic Regression, Decision Trees, Random Forest
  • Unsupervised: K-Means, PCA, Hierarchical Clustering
  • Projects: Work with small real-world datasets (e.g., Titanic, Iris)

Week 9–11: Deep Learning Focus

  • Topics: Perceptrons, CNNs, RNNs, LSTMs
  • Frameworks: TensorFlow, PyTorch
  • Mini Projects: Image classifier, sentiment analyzer

Week 12+: Dive into AI Concepts

  • AI Topics: Search algorithms, optimization, planning, reasoning
  • Advanced Fields: NLP, Computer Vision, Generative AI (Transformers, GANs)

Final Thoughts

Getting started with AI, ML, and DS may seem overwhelming, but with the right approach and consistent effort, anyone can master the foundational concepts and start building real-world applications. Whether you're interested in academic research or industry projects, this roadmap provides a comprehensive launchpad for your journey into intelligent systems.

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