Machine Learning with Python Training

Machine Learning with Python Training Syllabus

Introduction To Machine Learning

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

Software Installation and Introduction to Python

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?

Python Basics

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

Data Types in Python

In this topic, you will learn the data types in Python

• Numbers
  • Int
  • Float
  • Examples
• String
  • How we represent a string in Python
  • Creating a string
  • Print a string
  • String indexing
  • String slicing
  • Properties of a string
  • Methods in string
  • Print formatting
• List
  • How we represent a list in Python
  • Creating a list
  • List indexing
  • List slicing
  • Properties of a list
  • Methods in list
  • Nested list
• Dictionary
  • How we represent a dictionary in Python
  • Creating a dictionary
  • Methods in dictionary
  • Nested dictionary
• Tuple
  • How we represent a tuple in Python
  • Creating a tuple
  • Tuple indexing
  • Tuple slicing
  • Properties of a tuple
  • Methods in tuple

• Set
  • How we represent a tuple in Python
  • Creating a set
  • Use case of set
• Boolean
  • What are Boolean
  • Comparison operators

Conditional 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

Looping

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

Function

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

Statistics And Probability

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

Machine Learning Packages

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

Machine Learning Algorithms

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
• Copy Scape:

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