cropped image_6_ removebg preview
BECOME AN INSTRUCTOR
shape
shape

Foundations of AI and Machine Learning for Beginners

  • Home
  • Foundations of AI and Machine Learning for Beginners

Foundations of AI and Machine Learning for Beginners

Total Duration: 20 hours

Duration per Day: 4 hours

Days: 5

Prerequisites

  • Basic programming knowledge (preferably in Python).
  • Good to have Familiarity with basic mathematical concepts (algebra, calculus, and statistics).
  • Basic understanding of computer science fundamentals

Course Objectives

  • Understand the fundamentals of Data Science, Machine Learning (ML), Deep Learning, and Artificial Intelligence (AI).
  • Gain proficiency in Python programming for ML and AI applications.
  • Develop skills in data preprocessing, model evaluation, and deployment.

Day 1: Introduction to Data Science, AI, ML, and Deep Learning

Introduction to Data Science

  • Definition and Scope
  • Data Science Lifecycle
  • Applications and Case Studies

Introduction to Artificial Intelligence (AI)

  • Definition and Evolution of AI
  • Types of AI: Narrow, General, and Super AI
  • AI Applications

Introduction to Machine Learning (ML)

  • Definition and Types of ML: Supervised, Unsupervised, and Reinforcement Learning
  • Applications and Use Cases

Introduction to Deep Learning

  • Definition and Importance
  • Difference between ML and Deep Learning
  • Real-world Applications

Day 2 & 3: Python Programming for Beginners

Introduction to Python

  • Why Python for Machine Learning?
  • Installing Python & Jupyter Notebook
  • Introduction to Anaconda
  • Writing your first Python program
  • Basic I/O operations

Python Data Structures

  • Lists, Tuples, Sets, and Dictionaries
  • When to use what?
  • Mutable vs. Immutable
  • Creating, indexing, and slicing lists
  • Dictionary key-value operations
  • List comprehensions for efficient coding

Control Structures & Functions

  • Conditional Statements (if-else)
  • Loops (for, while)
  • Defining functions
  • Arguments & return values
  • Lambda functions

Day 4: Data Preprocessing and Introduction to ML Algorithms

Data Preprocessing using Pandas

  • Data Cleaning: Handling Missing Values, Outliers
  • Feature Scaling: Normalization, Standardization
  • Data Transformation: Encoding Categorical Variables
  • Introduction to Pandas
  • Series and DataFrames
  • Data Cleaning & Preprocessing
  • Handling missing values
  • Filtering, sorting, and grouping
  • Loading and analyzing datasets

Day 5: Fundamental Concepts and Statistics for ML

Basic EDA (Exploratory Data Analysis)

  • Learn the fundamental statistics required for ML.
  • Understand the mathematical foundations of ML.

Fundamentals of Statistics and ML

  • Descriptive Statistics: Mean, Median, Mode, Standard Deviation
  • Probability Theory and Distributions
  • Inferential Statistics: Hypothesis Testing, p-values, Confidence Intervals

T. Sanjay

Tech Enthusiast | Seasoned Corporate EnterT(r)ainer

Apply Now