AMIRIT Techservices provides Artificial Inteligence (AI) with Python course in NOIDA sector 64, will establish your mastery of data science and analytics techniques using Python. With this AI with Python Course, you’ll learn the essential concepts of Python programming and gain deep knowledge in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.

The course curriculum of Training in Python:

  1. Python Core
  2. Python Advance
  3. Python With Machine Learning

1. Python Core

Class 1: Introduction

  • History
  • Features
  • Python installation
  • Setting up path
  • Working with python
  • Basic syntax and first program
  • Variable and Data Types
  • Operators
  • Type casting(Implicit ,Explicit, ord)

Class 2 : Conditional Statements

  •  If Statement
  • If-else Statement
  • Elif Statement
  • Nested if-else

Class 3 : Loop

  • While loop
  • For Loop
  • Nesting of loops
  • Combination Of for Loop and while loop
  • Pattern Programs

Class 4 : Control Statements

  • Break
  • Continue
  • Pass
  • Diffrence between break, continue and pass

Class 5 : String Manipulation

  • What is String?
  • Accessing of String
  • Basic operations
  • String Slicing
  • Functions and Methods (Len, str, upper, lower, min, max append, index)
  • Practice for inbuilt function making

Class 6 : Lists

  • What is Lists?
  • How it is different from Strings
  • Accessing List
  • Operations (append, extend, sort, pop, insert, remove, count, max, min, count, length, index)
  • Working with lists
  • Functions and Methods

Class 7 : Program Practice and Doubt session

  • Program Practice and Doubt session

Class 8 : Tuples

  • What is tuples?
  • Diffrence between list , tuples and strings
  • Accessing tuples
  • Working with tuples
  • Functions and Methods append, extend, sort, pop, insert, remove, count, etc…)
  • Implementation of functions and methods

Class 9 : Dictionaries

  • What is dictionary?
  • Diffrence between list ,tuples and dictionary
  • Accessing values in dictionaries
  • Working with dictionaries
  • Functions append, extend, sort, pop, insert, remove, count, etc…)

Class 10 : Functions

  • Introduction
  • Defining a function
  • Calling a function

Class 11 : Types of function

  • Function arguments
  • Anonymous functions
  • Global and local variables
  • Function recepie and docstring
  • Recursive functions
  • *args and **kwargs

Class 12 : Modules

  • What is module?
  • Importing module(os, tkinter, pymysql, math, etc…)
  • Math Module
  • Packages
  • Diffrence between packages and modules
  • Composition

Class 13 : Input-Output

  • Printing on screen
  • Reading data from keyboard
  • Opening and closing files
  • Reading and writing files
  • Functions(open, close, append, write ,read)
  • Reading writing files usig with keywords
  • Csv reading and writing file using csv text editor

Class 14 : Exception Handling

  • What is Exception and how it is generated?
  • Exception Handling
  • Opening and closing file
  • Reading and Writing Files
  • Functions(try,catch)
  • Raising exception with raise and assert keyword
  • Making your own exception classes

Class 15 : Program Practice and Doubt session

  • Program Practice and Doubt session

2. Python Advance

Class 1: OOPs Concept

  • Class and object
  • Attributes
  • Encapsulation
  • Inheritance, Types of Inheritance

Class 2 : Polymorphism

  • Overloading
  • Overriding

Class 3 : Regular expressions

  • Introduction to CFG
  • Match function
  • Search function
  • Matching VS Searching

Class 4 : Modifiers

  • Patterns
  • Examples of different Regular expression
  • Lambda function and mapping

Class 5 : Database

  • Introduction
  • SQLITE and MYSQL introduction

Class 6 : Connections

  • Creation of SQL queries
  • Executing queries

Class 7 : Cursor functions

  • Database creation and table creation
  • Insertion,Updation and Deletion
  • Handling error

Class 8 : Networking

  • Socket
  • Socket Module

Class 9 : Methods

  • Client and server
  • Internet modules
  • Complete chat server

Class 10 : Project 1

  • Project 1

Class 11 : Multithreading

  • Thread
  • Starting a thread
  • Explanation of thread generation

Class 12 : Threading module

  • Synchronizing threads
  • Multithreaded Priority Queue

Class 13 : Project 2

  • Project 2

Class 14 : Project 3

  • Project 3

Class 15 : GUI Programming

  • IntroductionAttributes of TK, TK Module
  • Tkinter programming, Tkinter widgets

2. Python With Machine Learning

Class 1: Introduction Topics

1. Machine Learning.
2. Data Mining.
3. Deep Learning.
4. Artificial Intelligence
5. Descriptive Analysis.
6. Predictive Analysis.

Class 2 : Python

1. Basics Python
2. Types of Variables
3. Numbers
4. Strings
5. Lists
6. Dictionaries
7. Tuples
8. Statements
9. Looping
10. Function
11. Anaconda Distribution
12. Working Framework Jupyter Notebook

Class 3 : NumPy.

  1. 1: NdArray
    1. 1. ndim, shape, size, dtype, itemsize, data
    2. 2. Array Creation
    3. 3. Printing Arrays
    4. 4. Basic Operations
    5. 5. Universal Functions
    6. 6. Indexing, Slicing and Iterating
  2. 2: Shape Manipulation
    1. 1. Changing the shape of an array
    2. 2. Stacking together different arrays
    3. 3. Splitting one array into several smaller ones
  3. 3: Copies and Views
    1. 1. No Copy at All
    2. 2. View or Shallow Copy
    3. 3. Deep Copy
  4. 4: Fancy indexing and index tricks
    1. 1. Indexing with Arrays of Indices
    2. 2. Indexing with Boolean Arrays
    3. 3. The ix_() function
  5. 5: Linear Algebra
    1. 1. Simple Array Operations

Class 4 : Pandas

1: Pandas Basics

  1. Object Creation
  2. Viewing Data
  3. Selection
  4. Missing Data
  5. Operations
  6. Merge
  7. Grouping
  8. Reshaping
  9. Time Series
  10. Plotting
  11. Getting Data In/Out

2: Intro to Data Structures

  1. Series
  2. DataFrame
  3. Panel
  4. Deprecate Panel

Class 5 : Matplotlib

  1. Introductory
  2. Intermediate
  3. Advanced
  4. Colors
  5. Text

Class 6 : Data Preprocessing and Data Analysis

  1. Data Cleaning or Data cleansing.
  2. Data Integration.
  3. Data Transformation.
  4. Data Reduction.
  5. Data Discretisation.
  6. Data Visualisation.
  7. Sql Queries.

Class 7 : Statistics

1: Descriptive Statistics.

  1. Mean, Median, Mode, Variance etc

2: Inferential Statistics.

  1. Linear Regression.
  2. Binomial Distribution.
  3. Normal Distribution.
  4. Chi-Squared Test
  5. Permutation and Combination.
  6. Least Square etc

Class 8 : Machine Learning

  1. 1: Supervised Learning.
    1. 1: Classification Techniques
    2. 2: Regression Techniques.
      1. 1. Dependent variable
      2. 2. Independent variable
      3. 3. Population
      4. 4. Random sampling
      5. 5. Gradient Descent.
      6. 6. Cost Function.
      7. 7. Hypothesis function
      8. 8. Least squares
      9. 9. Regularization
      10. 10. Correlation.
      11. 11. Training Data and Test Data.
      12. 12. Cross Validation.
      13. 13. Type I and type II error.
      14. 14. Interpolation and Extrapolation.
      15. 15. False Positive and False Negative.
      16. 16. Bias and Variance.
      17. 17. File Format.
      1. 2: Linear Regression With One Variable.
      2. 3: Linear Regression With Multiple Variable.
      3. 4: Polynomial Regression.
      4. 5: Logistic Regression.
      5. 6: Decision Tree Regression.
      6. 7: Random Forest Regression
      7. 8: Support Vector Machine (SVM).
        1. 1. Hyper Planes
        2. 2.Support Vectors
          1. 1. Small margin
          2. 2. Large margin
      8. 9: Time Series Forecasting.
        1. 1. Trends
        2. 2. Linear Trend and Non Linear Trend
        3. 3. Seasonal Trend
        4. 4. Cyclical Trend
        5. 5. Irregular Trend
          1. 2: Autoregressive ( AR )
          2. 3: Moving Average ( MA )
          3. 4: Autoregressive Moving Average ( ARMA )
          4. 5: Autoregressive Integrated Moving Average ( ARIMA )
      9. 10: Naive Bayes.
      10. 11: K Nearest Neighbours
  2. 2: Unsupervised Learning
    1. 1. Clustering.
    2. 2. K means Clustering
    3. 3. Hierarchical Clustering.
    4. 4. Mean Shift Clustering.
  3. 3: Reinforcement Learning ( AI )
    1. 1: Neural Network.
    2. 2: Convolution Neural Network.
      1. 1. Image recognition
      2. 2. Video recognition
      3. 3: Artificial Neural Network.
        1. 1. Single Layer Network.
        2. 2. Two Layer Network.
        3. 3. Feed Forward Layer Network.
        4. 4. Fully Connected Layer Network
  4. 4: Recommendation System.
    1. 1. Collaborative Filtering
    2. 2. Content Based Filtering.

Class 9 : Source Code

  • Source Code