Data Science(AI and ML) With Python

The goal of this course is to examine large amounts of data to uncover hidden patterns, correlations and other insights. This help in preparing machine learning model using AI fundamentals.

Course Structure:

• Python Data Science Overview
  • What is Data Science
  • What Is Data Science and Machine Learning?
  • Introduction of Python Data Science Tools
  • Setting up environment for this course
  • Python Data Science Packages to be used
• Fundamental of Statistics
  • Overview
  • Basic Terminology of Statistics
  • Variables
  • Data
  • Statistics
  • Dispersion
  • Scattering
  • Observation
  • Time Series Data
  • Population
  • Sample
  • Variation
  • Shape
  • Data Collections
  • Descriptive Statistics
  • Data Distribution
  • Confidence Interval
• Statistical Inferences and Relationship Between Variables
  • Hypothesis Testing
  • Correlation Theory
  • Linear regression theory
  • Polynomial Regression
  • Logistic Regression
• Introduction to NumPy (Numerical Python)
  • Numpy: Introduction
  • Create Numpy Arrays
  • Numpy Operations
  • Matrix Airthmetic and Linear Systems
  • Numpy for Basic Matrix Airthmetic
  • Broadcasting with Numpy
  • Solve Equation with Numpy
  • Statistical Operations
• Pandas
  • Introduction to Data Structures
  • Series
  • DataFrame
  • Panel
  • Reading Data
  • CSV Data
  • Excel Data
  • JSON Data
  • HTML Data
  • Data pre-Processing/Wrangling/Cleaning
  • Removing NAs /No Values from data
  • Basic Data Handling: Starting with conditional data section
  • Drop Column/Rows
  • Subset and Index Data
  • Basic Data Grouping Based on Qualitative Attributes
  • Cross tabulation
  • Reshaping
  • Pivoting
  • Rank and sort Data
  • Concatenate
  • Merging and Joining frames
• Data Visualization (plotLib)
  • Introduction
  • Histograms
  • Box Plots
  • Scatter Plots
  • Bar Plot
  • Pie Chart
  • Line Chart