Machine Learning with Python Training

In this topic, you will understand the basic introduction to Machine
Learning

• What is Machine Learning?
• Where can we use Machine Learning?
• Scope of Machine Learning?
• Difference between Machine Learning Vs Deep Learning Vs Artificial Intelligence

In this topic, you will get to know about the software that we will be
using and a basic understanding of the python

• Installing Anaconda
• What is an IDE and different IDEs
• Overview of Jupyter Notebook
• What is Python?
• Advantages of using Python in Machine Learning?

In this topic, you will learn the basics of Python

• Arithmetic Operations
• What is a variable?
• Rules for variable assignment
• Examples of variable assignment
• Variable re-assignment
• Different between print and non-print statements

In this topic, you will understand the end-to-end use of conditional statements

• What are conditional statements
• Syntax
• If statement

• Examples

• Combination of if and else statements

• • Examples

• Combination of if, elif and else statements

• • Examples

In this topic, you will understand the concept of looping

• What is looping?
• Where we use looping?
• Types of looping
• For loop

• • Where we use for loop?
  • Syntax
  • Example of for loop using string, list, dictionary, tuple
  • Logic based looping examples

• While loop

• • Where we use while loop?
  • Syntax
  • Exaples

In this topic, you understand about functions

• What is a function
• Where we use function
• Syntax
• What is a doc string
• Examples using functions
• Local variable and global variable
• Args and kwargs

In the topic, you will learn the basics of statistics and probability concepts, which will be useful when we proceed with Machine Learning

• What is statistics
• Why statistics in Machine Learning
• Difference between data and information
• Types of Statistics
• Central Tendency Theory

• • Mean
  • Median
  • Mode

• Measures of Dispersion

• • Range
  • Variance
  • Standard Deviation
  • Inter Quartile Range

• Outlier treatment

• • What is an Outlier?
  • How to identify the presence of an outlier?

• • • Box and whisker plot to detect outliers
      • What is a quartile
      • Representation of box and whisker plot
        • Min
        • 25 % or lower quartile
        • 50 % or Median
        • 75 % or upper quartile
        • Max
      • Scatter plot to detect outliers
    • How to deal with outliers?
      • Capping and flooring
      • Replacing with mean or median values
    • Missing Value treatment
      • What is a missing value?
      • How to detect missing values?
      • How to deal with missing values?
        • Deleting missing values
        • Replacing missing values with mean or median or mode
        • Replacing missing values with unique value or category
        • Replacing missing values with ffill or bfill
        • KNN Imputation
      • String categorical data treatment
        • Get dummies method to deal with string categorical data
      • Normal Distribution
      • Skewness
        • Positive skewness
        • Negative skewness
      • Kurtosis
      • Correlation
        • What is correlation?
        • Positive correlation
        • Negative correlation
        • No correlation
        • Examples
        • Pearson correlation
      • What is Probability?
      • Types of Probability?
      • Odds and Odds ration
      • Distance Metrics
        • Euclidean distance
        • Manhattan distance
      • Bias Variance trade off
        • What is bias
        • What is variance
        • Underfitting
        • Overfitting

In this topic, you will learn what the machine learning packages are and where we use it

• Pandas

• • Where we use pandas
  • Installing pandas
  • Importing pandas
  • Reading files using pandas
  • What is a dataframe
  • What is a series
  • Basic operations using pandas

• • • To check the top five rows in dataframe
    • To check the bottom five rows in dataframe
    • info()
    • describe()
    • How to check what are the column names

• • Creating a dataframe
  • Understanding columns
  • Understanding index and indexing
  • Value counts
  • Filtering
  • Groupby

• Numpy

• Where we use numpy
• Installing numpy
• Importing numpy
• Creating an array
• Basic array operations
• Array sorting
• Reshape
• Array stacking
• Array splitting

• Matplotlib

• • Where we use matplotlib
  • Installing matplotlib
  • Importing matplotlib
  • Line plot
  • Bar plot
  • Histogram
  • Scatter plot
  • Pie chart

In this topic, you will learn the machine algorithms, use cases etc.,

• What is machine learning?
• Where we use machine learning?
• Types of machine learning

• • Supervised machine learning
  • Unsupervised machine learning

• Steps involved in supervised machine learning
• Types of supervised machine learning

• • Regression
  • Classification

• Linear Regression

• • What is linear regression?
  • Where we use linear regression?
  • Assumptions in linear regression
  • Types of linear regression

• • • Simple linear regression
    • Multiple linear regression

• • Ordinary Least Squared
  • Example of linear regression using dataset

• Logistic Regression

• • What is logistic regression?
  • Where we use logistic regression?
  • Concept of logistic regression
  • Difference between linear regression and logistic regression
  • Example of logistic regression using dataset

• • K-Nearest Neighbour

• • • What is K-Nearest Neighbour?
    • Where we use K-Nearest Neighbour?
    • Concept of K-Nearest Neighbour
    • Example of K-Nearest Neighbour using dataset

• • Decision Tree

• • • What is decision tree?
    • Where we use decision tree?
    • Types of decision tree
      • Continuous variable decision tree
      • Categorical variable decision tree
    • Concept of decision tree
      • Gini index
      • Information gain
    • Example of decision tree using dataset

• • Random Forest

• • • What is random forest?
    • Where we use random forest?
    • Concept of random forest
    • Types of random forest
      • Continuous variable
      • Categorical variable
    • Example of random forest using dataset

• • Naïve Bayes

• • • What is Naïve Bayes algorithm?
    • Where is Naïve Bayes algorithm used?
    • Example of Naïve Bayes algorithm using dataset

• • K-Means Clustering

• • • What is K-Means clustering?
    • Where is K-Means clustering used?
    • Concept of K-Means clustering
    • Example of K-Means clustering using dataset

• • Hierarchical Clustering

• • • What is hierarchical clustering?
    • Where we use hierarchical clustering?
    • Concept of hierarchical clustering
    • Example of hierarchical clustering using dataset