DBMS – Structured Query Language

• Introduction & Models of DBMS
• SQL & Sub Language of SQL
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Query/Retrieval Language (DQL/DRL)
• Transaction Control Language (TCL)
• Data Control Language (DCL)
• Installation of MySQL & Database Normalization
• Sub Queries & Key Constraints
• Aggregative Functions, Clauses & Views

Importing & Exporting Data

• Data Extraction from CSV (pd.read_csv)
• Data Extraction from TEXT File (pd.read_table)
• Data Extraction from CLIPBOARD (pd.read_clipboard)
• Data Extraction from EXCEL (pd.read_excel)
• Data Extraction from URL (pd.read_html)
• Writing into CSV (df.to_csv)
• Writing into EXCEL (df.to_excel)
• Data Extraction from DATABASES
  • Python MySQL Database Connection
    • Import mysql.connector Module
    • Create the Connection Object
    • Create the Cursor Object
    • Execute the Query

Data Visualization with Python Matplotlib

• Data Visualization Introduction
• Tasks of Data Visualization
• Benefit of Data Visualization
• Plots for Data Visualization
• Matplotlib Architecture
• General Concept of Matplotlib
• MatPlotLib Environment Setup
• Verify the MatPlotLib Installation
• Working with PyPlot
• Formatting the Style of the Plot
• Plotting with Categorical Variables
• Multi-Plots with Subplot Function
• Line Graph
• Bar Graph
• Histogram
• Scatter Plot
• Pie Plot
• 3Dimensional – 3D Graph Plot
• mpl_toolkits
• Functions of MatPlotLib
• Contour Plot, Quiver Plot, Violin Plot
• 3D Contour Plot
• 3D Wireframe Plot
• 3D Surface Plot
• Box Plot
  • What is a Boxplot?
  • Mean, Median, Quartiles, Outliers
  • Inter Quartile Range (IQR), Whiskers
  • Data Distribution Analysis
  • Boxplot on a Normal Distribution
  • Probability Density Function
  • 68–95–99.7 Rule (Empirical rule)

Data Analysis Project using Python Programming

Machine Learning

• What is Machine Learning
• Importance of Machine Learning
• Need for Machine Learning
• Statistics Vs Machine Learning
• Traditional Programming Vs Machine Learning
• How Machine Learning like Human Learning
• How does Machine Learning Work?
• Machine Learning Engineer Responsibilities
• Life Cycle of Machine Learning
  • Gathering Data
  • Data preparation
  • Data Wrangling
  • Analyze Data
  • Train the model
  • Test the model
  • Deployment
• Features of Machine Learning
• History of Machine Learning
• Applications of Machine Learning
• Types of Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement Learning

Supervised Machine Learning

• How Supervised Learning Works?
• Steps Involved in Supervised Learning
• Types of supervised Machine Learning Algorithms
  • Classification
  • Regression
• Advantages of Supervised Learning
• Disadvantages of Supervised Learning

Unsupervised Machine Learning

• How Unsupervised Learning Works?
• Why use Unsupervised Learning?
• Types of Unsupervised Learning Algorithm
  • Clustering
  • Association
• Advantages of Unsupervised Learning
• Disadvantages of Unsupervised Learning
• Supervised Vs Unsupervised Learning
• Reinforcement Machine Learning
• How to get Datasets for Machine Learning?
  • What is a Dataset?
  • Types of Data in Datasets
  • Popular Sources for Machine Learning Datasets

Data Preprocessing in Machine Learning

• Why do we need Data Preprocessing?
  • Getting the Dataset
  • Importing Libraries
  • Importing Datasets
  • Finding Missing Data
    • By Deleting the Particular Row
    • By Calculating the Mean
  • Encoding Categorical Data
    • LableEncoder
    • OneHotEncoder
  • Splitting Dataset into Training and Test Set
  • Feature Scaling
    • Standardization
    • Normalization

Classification Algorithms in Machine Learning

• What is the Classification Algorithm?
• Types of Classifications
  • Binary Classifier
  • Multi-class Classifier
• Learners in Classification Problems
  • Lazy Learners
  • Eager Learners
• Types of ML Classification Algorithms
  • Linear Models
    • Logistic Regression
    • Support Vector Machines
  • Non-linear Models
    • K-Nearest Neighbors
    • Naïve Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • Kernel SVM
  • Evaluating a Classification Model
    • Confusion Matrix
      • What is a Confusion Matrix?
        • True Positive
        • True Negative
        • False Positive – Type 1 Error
        • False Negative – Type 2 Error
      • Why need a Confusion matrix?
      • Precision
      • Recall
      • Precision vs Recall
      • F1-score
      • Confusion Matrix in Scikit-Learn
      • Confusion Matrix for Multi-Class Classification
    • Log Loss or Cross-Entropy Loss
    • AUC-ROC curve
• Use cases of Classification Algorithms

K-Nearest Neighbor (KNN) Algorithm in Machine Learning

• Why do we need a K-NN Algorithm?
• How does K-NN work?
  • What is Euclidean Distance
  • How it Calculates the Distance
• How to Select the Value of K in the K-NN Algorithm?
• Advantages of KNN Algorithm
• Disadvantages of KNN Algorithm
• Python Implementation of the KNN Algorithm
• Analysis on Social Network Ads Dataset
• Steps to Implement the K-NN Algorithm
  • Data Pre-processing Step
  • Fitting the K-NN algorithm to the Training Set
  • Predicting the Test Result
  • Test Accuracy of the Result (Creation of Confusion Matrix)
  • Visualizing the Test Set Result.
  • Improve the Performance of the K-NN Model

Naïve Bayes Classifier Algorithm in Machine Learning

• Why is it Called Naïve Bayes?
  • Naïve Means?
  • Bayes Means?
• Bayes’ Theorem
  • Posterior Probability
  • Likelihood Probability
  • Prior Probability
  • Marginal Probability
• Working of Naïve Bayes’ Classifier
• Advantages of Naïve Bayes Classifier
• Disadvantages of Naïve Bayes Classifier
• Applications of Naïve Bayes Classifier
• Types of Naïve Bayes Model
  • Gaussian Naïve Bayes Classifier
  • Multinomial Naïve Bayes Classifier
  • Bernoulli Naïve Bayes Classifier
• Python Implementation of the Naïve Bayes Algorithm
• Steps to Implement the Naïve Bayes Algorithm
  • Data Pre-processing Step
  • Fitting Naive Bayes to the Training set
  • Predicting the Test Result
  • Test Accuracy of the Result (Creation of Confusion matrix)
  • Visualizing the Test Set Result
  • Improve the Performance of the Naïve Bayes Model

Decision Tree Classification Algorithm in Machine Learning

• Why use Decision Trees?
• Types of Decision Trees
  • Categorical Variable Decision Tree
  • Continuous Variable Decision Tree
• Decision Tree Terminologies
• How does the Decision Tree Algorithm Work?
• Attribute Selection Measures
  • Entropy
  • Information Gain
  • Gini index
  • Gain Ratio
• Algorithms used in Decision Trees
  • ID3 Algorithm → (Extension of D3)
  • 5 Algorithm→ (Successor of ID3)
  • CART Algorithm → (Classification & Regression Tree)
• How to Avoid/Counter Overfitting in Decision Trees?
  • Pruning Decision Trees
  • Random Forest
• Pruning: Getting an Optimal Decision tree
• Advantages of the Decision Tree
• Disadvantages of the Decision Tree
• Python Implementation of Decision Tree
• Steps to Implement the Decision Tree Algorithm
  • Data Pre-processing Step
  • Fitting a Decision-Tree Algorithm to the Training Set
  • Predicting the Test Result
  • Test Accuracy of the Result (Creation of Confusion matrix)
  • Visualizing the Test Set Result
  • Improve the Performance of the Decision Tree Model

Random Forest Classifier Algorithm in Machine Learning

• Working of the Random Forest Algorithm
• Assumptions for Random Forest
• Why use Random Forest?
• How does Random Forest Algorithm Work?
  • Ensemble Techniques
  • Bagging (Bootstrap Aggregation)
• Applications of Random Forest
• Disadvantages of Random Forest
• Python Implementation of Random Forest Algorithm
• Steps to Implement the Random Forest Algorithm:
  • Data Pre-processing Step
  • Fitting the Random Forest Algorithm to the Training Set
  • Predicting the Test Result
  • Test Accuracy of the Result (Creation of Confusion Matrix)
  • Visualizing the Test Set Result
  • Improving the Performance of the Random Forest Model

Logistic Regression Algorithm in Machine Learning

• Logistic Function (Sigmoid Function)
• Assumptions for Logistic Regression
• Logistic Regression Equation
• Type of Logistic Regression
  • Binomial Logistic Regression
  • Multinomial Logistic Regression
  • Ordinal Logistic Regression
• Python Implementation of Logistic Regression (Binomial)
• Steps to Implement the Logistic Regression:
  • Data Pre-processing Step
  • Fitting Logistic Regression to the Training Set
  • Predicting the Test Result
  • Test Accuracy of the Result(Creation of Confusion Matrix)
  • Visualizing the Test Set Result
  • Improve the Performance of the Logistic Regression Model

Support Vector Machine Algorithm

• Types of Support Vector Machines
  • Linear Support Vector Machine
  • Non-Linear Support Vector Machine
• Hyperplane in the SVM Algorithm
• Support Vectors in the SVM Algorithm
• How does SVM Works?
  • How does Linear SVM Works?
  • How does Non-Linear SVM Works?
• Python Implementation of Support Vector Machine
• Steps to Implement the Support Vector Machine:
  • Data Pre-processing Step
  • Fitting Support Vector Machine to the Training Set
  • Predicting the Test Result
  • Test Accuracy of the Result(Creation of Confusion Matrix)
  • Visualizing the Test Set Result
  • Improve the Performance of the Support Vector Machine Model

Regression Algorithms in Machine Learning

• Terminologies Related to the Regression Analysis
• Dependent Variable
• Independent Variable
• Outliers
• Multi-collinearity
• Under fitting and Overfitting
• Why do we use Regression Analysis?
• Types of Regression
  • Linear Regression
  • Logistic Regression
  • Polynomial Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression
  • Ridge Regression
  • Lasso Regression

Linear Regression in Machine Learning

• Types of Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
• Linear Regression Line
  • Positive Linear Relationship
  • Negative Linear Relationship
• Finding the Best Fit Line
• Assumptions of Linear Regression