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Master the basics of Machine Learning using Python language. Join now to learn under a Python and ML expert.

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Machine Learning with Python Training Program

    Machine learning is important because it gives enterprises a view of trends in ustomer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations

    In this program you will learn:

    • Python Training Curriculum
    • Data Analysis and Visualization using Pandas.
    • Data Analysis and Visualization using NumPy and MatPlotLib
    • Introduction to Data Visualization with Seaborn
    • Machine Learning
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    Introduction To Python

    • Installation and Working with Python
    • Understanding Python variables
    • Python basic Operators
    • Understanding the Python blocks.

    Python Keyword and Identiers

    • Python Keyword and Identiers
    • Python Comments, Multiline Comments.
    • Python Indentation
    • Understating the concepts of Operators
      • Arithmetic
      • Relational
      • Logical
      • Assignment
      • Membership
      • Identity

    Introduction To Variables

    • Variables, expression condition and function
    • Global and Local Variables in Python
    • Packing and Unpacking Arguments
    • Type Casting in Python
    • Byte objects vs. string in Python
    • Variable Scope

    Python Data Type

    • Declaring and using Numeric data types
    • Using string data type and string operations
    • Understanding Non-numeric data types
    • Understanding the concept of Casting and Boolean.
      • Strings
      • List
      • Tuples
      • Dictionary
      • Sets

    Control Structure & Flow

    • Statements – if, else, elif
    • How to use nested IF and Else in Python
    • Loops
    • Loops and Control Statements.
    • Jumping Statements – Break, Continue, pass
    • Looping techniques in Python
    • How to use Range function in Loop
    • Programs for printing Patterns in Python
    • How to use if and else with Loop
    • Use of Switch Function in Loop
    • Elegant way of Python Iteration
    • Generator in Python
    • How to use nested Loop in Python
    • Use If and Else in for and While Loop
    • Examples of Looping with Break and Continue Statement
    • How to use IN or NOT IN keyword in Python Loop.

    Python Function, Modules and Packages

    • Python Syntax
    • Function Call
    • Return Statement
    • Arguments in a function – Required, Default, Positional, Variable-length
    • Write an Empty Function in Python –pass statement.
    • Lamda/ Anonymous Function
    • *args and **kwargs
    • Help function in Python
    • Scope and Life Time of Variable in Python Function
    • Nested Loop in Python Function
    • Recursive Function and Its Advantage and Disadvantage
    • Organizing python codes using functions
    • Organizing python projects into modules
    • Importing own module as well as external modules
    • Understanding Packages
    • Random functions in python
    • Programming using functions, modules & external packages
    • Map, Filter and Reduce function with Lambda Function
    • More example of Python Function

    Python Date Time and Calendar

    • Day, Month, Year, Today, Weekday
    • IsoWeek day
    • Date Time
    • Time, Hour, Minute, Sec, Microsec
    • Time Delta and UTC
    • StrfTime, Now
    • Time stamp and Date Format
    • Month Calendar
    • Itermonthdates
    • Lots of Example on Python Calendar
    • Create 12-month Calendar
    • Strftime
    • Strptime
    • Format Code list of Data, Time and Cal
    • Locale’s appropriate date and time

    List

    • What is List.
    • List Creation
    • List Length
    • List Append
    • List Insert
    • List Remove
    • List Append & Extend using “+” and Keyword
    • List Delete
    • List related Keyword in Python
    • List Revers
    • List Sorting
    • List having Multiple Reference
    • String Split to create a List
    • List Indexing
    • List Slicing
    • List count and Looping
    • List Comprehension and Nested Comprehension

    Tuple

    • What is Tuple
    • Tuple Creation
    • Accessing Elements in Tuple
    • Changing a Tuple
    • Tuple Deletion
    • Tuple Count
    • Tuple Index
    • Tuple Membership
    • TupleBuilt in Function (Length, Sort)

    Dictionary

    • Dict Creation
    • Dict Access (Accessing Dict Values)
    • Dict Get Method
    • Dict Add or Modify Elements
    • Dict Copy
    • Dict From Keys.
    • Dict Items
    • Dict Keys (Updating, Removing and Iterating)
    • Dict Values
    • Dict Comprehension
    • Default Dictionaries
    • Ordered Dictionaries
    • Looping Dictionaries
    • Dict useful methods (Pop, Pop Item, Str , Update etc.)

    Sets

    • What is Set
    • Set Creation
    • Add element to a Set
    • Remove elements from a Set
    • PythonSet Operations
    • Frozen Sets

    Strings

    • What is Set
    • Set Creation
    • Add element to a Set
    • Remove elements from a Set
    • PythonSet Operations

    Python Exception Handling

    • Python Errors and Built-in-Exceptions
    • Exception handing Try, Except and Finally
    • Catching Exceptions in Python
    • Catching Specic Exception in Python
    • Raising Exception
    • Try and Finally

    Python File Handling

    • Opening a File
    • Python File Modes
    • Closing File
    • Writing to a File
    • Reading from a File
    • Renaming and Deleting Files in Python
    • Python Directory and File Management
    • List Directories and Files
    • Making New Directory
    • Changing Directory

    Python Database Interaction

    • SQL Database connection using
    • Creating and searching tables
    • Reading and Storing cong information on database
    • Programming using database connections

    Contacting user Through Emails Using Python

    • Installing SMTP Python Module
    • Sending Email
    • Reading from le and sending emails to all users

    Reading an excel

    • Working With Excel
    • Reading an excel le using Python
    • Writing to an excel sheet using Python
    • Python| Reading an excel le
    • Python | Writing an excel le
    • Adjusting Rows and Column using Python
    • ArithmeticOperation in Excel le.
    • Play with Workbook, Sheets and Cells in Excel using Python
    • Creating and Removing Sheets
    • Formatting the Excel File Data
    • More example of Python Function

    Complete Understanding of OS Module of Python

    • Check Dirs. (exist or not)
    • How to split path and extension
    • How to get user prole detail
    • Get the path of Desktop, Documents, Downloads etc.
    • Handle the File System Organization using OS
    • How to get any les and folder’s details using OS
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    Statistics

    • Categorical Data
    • Numerical Data
    • Mean
    • Median
    • Mode
    • Outliers
    • Range
    • Interquartile range
    • Correlation
    • Standard Deviation
    • Variance
    • Box plot

    Pandas

    • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
    • How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
    • Using the Excel File class to read multiple sheets More Mapping, Filling
    • Nonvalue’s
    • Exploring the Data Plotting, Correlations, and Histograms
    • Getting statistical information about the data Analysis Concepts, Handle the None Values
    • Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
    • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
    • Applying formulas on the columns Basic Grouping, Concepts of Aggre
    • gate Function
    • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
    • Under sting the Properties of Pivot Table in Pandas Advanced Reading
    • CSVs/HTML, Binning, Categorical Data
    • Exporting the results to Excel Joins:
    • Python | Pandas Data Frame Inner Join
    • Under sting the properties of Data Frame Left Join (Left Outer Join)
    • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
    • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
    • Pandas | Find Missing Data and Fill and Drop NA Appending DataFrame and Data
    • Pandas | How to Group Data How to apply Lambda / Function on Data
    • Frame
    • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
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    NumPy

    • Introduction to NumPy: Numerical Python
    • Importing NumPy and Its Properties
    • NumPy Arrays
    • Creating an Array from a CSV
    • Operations an Array from a CSV
    • Operations with NumPy Arrays
    • Two-Dimensional Array
    • Selecting Elements from 1-D Array
    • Selecting Elements from 2-D Array
    • Logical Operation with Arrays
    • Indexing NumPy elements using conditionals
    • NumPy’s Mean and Axis
    • NumPy’s Mode, Median and Sum Function
    • NumPy’s Sort Function and More

    MatPlotLib

    • Bar Chart using Python MatPlotLib
    • Column Chart using Python MatPlotLib
    • Pie Chart using Python MatPlotLib
    • Area Chart using Python MatPlotLib
    • Scatter Plot Chart using Python MatPlotLib
    • Play with Charts Properties Using MatPlotLib
    • Export the Chart as Image
    • Understanding plt. subplots () notation
    • Legend Alignment of Chart using MatPlotLib
    • Create Charts as Image
    • Other Useful Properties of Charts.
    • Complete Understanding of Histograms
    • Plotting Different Charts, Labels, and Labels Alignment etc.
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    Introduction to Seaborn

    • Introduction to Seaborn
    • Making a scatter plot with lists
    • Making a count plot with a list
    • Using Pandas with seaborn
    • Tidy vs Untidy data
    • Making a count plot with a Dataframe
    • Adding a third variable with hue
    • Hue and scattera plots
    • Hue and count plots

    Visualizing Two Quantitative Variables

    • Introduction to relational plots and subplots
    • Creating subplots with col and row
    • Customizing scatters plots
    • Changing the size of scatter plot points
    • Changing the style of scatter plot points
    • Introduction to line plots
    • Interpreting line plots
    • Visualizing standard deviation with line plots
    • Plotting subgroups in line plots

    Visualizing a Categorical and a Quantitative Variable

    • Current plots and bar plots
    • Count plots
    • Bar plot with percentages
    • Customizing bar plots
    • Box plots
    • Create and interpret a box plot
    • Omitting outliers
    • Adjusting the whisk
    • Point plots
    • Customizing points plots
    • Point plot with subgroups

    Customizing Seaborn Plots

    • Changing plot style and colour
    • Changing style and palette
    • Changing the scale
    • Using a custom palette
    • Adding titles and labels: Part 1
    • Face Grids vs. Axes Subplots
    • Adding a title to a face Grid object
    • Adding title and labels: Part 2
    • Adding a title and axis labels
    • Rotating x-tics labels
    • Putting it all together
    • Box plot with subgroups
    • Bar plot with subgroups and subplots
    • Well done! What’s next
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    Introduction to Machine Learning

    • Articial Intelligence
    • Machine Learning
    • Machine Learning Algorithms
    • Algorithmic models of Learning
    • Applications of Machine Learning
    • Large Scale Machine Learning
    • Computational Learning theory
    • Reinforcement Learning

    Techniques of Machine Learning

    • Supervised Learning
    • Unsupervised Learning
    • Semi-supervised and Reinforcement Learning
    • Bias and variance Trade-off
    • Representation Learning

    Regression

    • Regression and its Types
    • Logistic Regression
    • Linear Regression
    • Polynomial Regression

    Classication

    • Meaning and Types of Classication
    • Nearest Neighbor Classiers
    • K-nearest Neighbors
    • Probability and Bayes Theorem
    • Support Vector Machines
    • Naive Bayes
    • Decision Tree Classier
    • Random Forest Classier

    Unsupervised Learning: Clustering

    • About Clustering
    • Clustering Algorithms
    • K-means Clustering
    • Hierarchical Clustering
    • Distribution Clustering

    Model optimization and Boosting

    • Ensemble approach
    • K-fold cross validation
    • Grid search cross validation
    • Ada boost and XG Boost
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FAQ's

Machine Learning is the process of training computers to learn patterns from data and make predictions.

Python is easy to learn, has powerful libraries, and a strong ML community.

Basic knowledge of statistics, linear algebra, and probability is helpful.

Common libraries include Scikit-learn, TensorFlow, Keras, and Pandas.

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