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Data Science and Machine Learning Fundamentals

Data Science and Machine Learning Fundamentals

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Last Update 12/30/2023
Completion Time 6 hours 55 minutes
Members 4
  • Section-1
    • 1-1 introduction 1080p
  • Section-2
    • 2-1 What is Data Science
    • 2-2 Types of Data Analysis
    • 2-3 data science skills 1080p
    • 2-4 Two types of Computer Science
    • 2-5 data science process
    • 2-6 Machine Learning Process
    • 2-7 Types of Machine Learning
    • 2-8 Types of Data Science Models
    • 2-9 Priorities for Business Leaders
    • 2-10 Model Objectives
    • 2-11 Model Limitations
    • 2-12 Model Metrics
    • 2-13 Data Science Tools
    • 2-14 Data Science Roles
    • 2-15 What is Artificial Intelligence
  • Section-3
    • 3-1 Regression Theory and Business Objectives
    • 3-2 Simple and Multiple Linear Regression
    • 3-3 Ordinary Least Squares
    • 3-4 Interpreting Coefficients
    • 3-5 Regression Metrics
    • 3-6 Training and Testing
    • 3-7 Underfitting and Overfitting
    • 3-8 Plotting a line of best fit in excel
    • 3-9 Calculating the optimal line
    • 3-10 Regression in Python
    • 3-11 Other Regression Techniques
  • Section-4
    • 4-1 Classification Theory and Business Objectives
    • 4-2 Classification Algorithms
    • 4-3 Visualizing Logistic Regression
    • 4-4 Decision Tree
    • 4-5 K Nearest Neighbours
    • 4-6 Support Vector Machines
    • 4-7 Naive Bayes
    • 4-8 Gaussian Naive Bayes
    • 4-9 Confusion Matrix
    • 4-10 Evaluation Metrics
    • 4-11 Overfitting and Underfitting
    • 4-12 How to install RegressIt Excel Addin
  • Section-5
    • 5-1 Data Preparation & Terminology
    • 5-2 Data Cleansing Part 1
    • 5-3 Data Cleansing Part 2
    • 5-4 Data Cleansing Solutions
    • 5-5 EDA Introduction
    • 5-6 EDA Continuous Data Type
    • 5-7 EDA Categorical Data Type
    • 5-8 EDA Data Type Exceptions
    • 5-9 EDA Binary Descriptive Stats
    • 5-10 EDA unordered categories
    • 5-11 EDA Ordered Variables
    • 5-12 EDA Continuous Variables
    • 5-13 EDA Correlation
    • 5-14 EDA Scatter Plot Matrix
    • 5-15 EDA Feature Selection
    • 5-16 Feature Engineering
    • 5-17 Feature Engineering Outliers
    • 5-18 Featuring Engineering Normalization
    • 5-19 Feature Engineering Grouping,Binning
    • 5-20 Feature Engineering One Hot Encoding
    • 5-21 Feature Engineering Calculation
    • 5-22 Training and Testing Model Part 1
    • 5-23 Training and Testing Model Part 2