Machine learning using Python Training in Noida | Machine learning with Python Institute

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Are you Looking for the Best Institute for Machine learning with Python training in Noida? DUCAT offers Machine learning with Python training classes with live project by expert trainer in Noida. Our Machine learning with Python training program in Noida is specially designed for Under-Graduates (UG), Graduates, working professional and also for Freelancers. We provide end to end learning on Machine learning with Python Domain with deeper dives for creating a winning career for every profile.

Why To Enroll In Our Machine learning with Python Training Course in Noida / Greater Noida?

We Focus on Innovative ideas, High-quality Training, Smart Classes, 100% job assistance, Opening the doors of opportunities. Our Machine learning with Python Trainees are working across the nation. We at Ducat India, No#1 Mean Stack Course in Noida with 100% Placement. Certified Trainers with Over 10,000 Students Trained in Machine learning with Python Course in Noida, Greater Noida,Ghaziabad,Gurgaon,Faridabad

What Our Students Will Get During Machine learning with Python Training Course?

Get dedicated student support, career services, industry expert mentors and real world projects. Career Counselling. Timely Doubt Resolution. 50% Salary Hike, Career Counselling Case Studies + Tools + Certificate.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Python is mainly stated as high-level, general-purpose programming language, which emphasizes code readability. The syntax helps the programmers to express their concepts in few general "lines of code" when compared with other promising languages, like Java or C++. Through our courses, you can easily construct clear programs, on both large and small scales.As the importance of Python programming language is gaining huge popularity, therefore; the need to understand and know the language is increasing among people. When you think about Python training, you must look for an Ducat expert help.

Introduction To Python

  • Why Python
  • Application areas of python
  • Python implementations
  • Cpython
  • Jython
  • Ironpython
  • Pypy
  • Python versions
  • Installing python
  • Python interpreter architecture
  • Python byte code compiler
  • Python virtual machine(pvm)

Writing and Executing First Python Program

  • Using interactive mode
  • Using script mode
  • General text editor and command Window
  • Idle editor and idle shell
  • Understanding print() function
  • How to compile python program explicitly

Python Language Fundamentals

  • Character set
  • Keywords
  • Comments
  • Variables
  • Literals
  • Operators
  • Reading input from console
  • Parsing string to int, float

Python Conditional Statements

  • If statement
  • If else statement
  • If elif statement
  • If elif else statement
  • Nested if statement

Looping Statements

  • While loop
  • For loop
  • Nested loops
  • Pass, break and continue keywords

Standard Data Types

  • Int, float, complex, bool, nonetype
  • Str, list, tuple, range
  • Dict, set, frozenset

String Handling

  • What is string
  • String representations
  • Unicode string
  • String functions, methods
  • String indexing and slicing
  • String formatting

Python List

  • Creating and accessing lists
  • Indexing and slicing lists
  • List methods
  • Nested lists
  • List comprehension

Python Tuple

  • Creating tuple
  • Accessing tuple
  • Immutability of tuple

Python Set

  • How to create a set
  • Iteration over sets
  • Python set methods
  • Python frozenset

Python Dictionary

  • Creating a dictionary
  • Dictionary methods
  • Accessing values from dictionary
  • Updating dictionary
  • Iterating dictionary
  • Dictionary comprehension

Python Functions

  • Defining a function
  • Calling a function
  • Types of functions
  • Function arguments
  • Positional arguments, keyword arguments
  • Default arguments, non-default arguments
  • Arbitrary arguments, keyword arbitrary arguments
  • Function return statement
  • Nested function
  • Function as argument
  • Function as return statement
  • Decorator function
  • Closure
  • Map(), filter(), reduce(), any() functions
  • Anonymous or lambda function

Modules & Packages

  • Why modules
  • Script v/s module
  • Importing module
  • Standard v/s third party modules
  • Why packages
  • Understanding pip utility

File I/O

  • Introduction to file handling
  • File modes
  • Functions and methods related to filehandling
  • Understanding withblock

Object Oriented Programming

  • Procedural v/s object oriented programming
  • OOP principles
  • Defining a class & object creation
  • Object attributes
  • Inheritance
  • Encapsulation
  • Polymorphism

Exception Handling

  • Difference between syntax errors and exceptions
  • Keywords used in exception handling
  • try, except, finally, raise, assert
  • Types of except blocks

Regular Expressions(Regex)

  • Need of regular expressions
  • Re module
  • Functions /methods related to regex
  • Meta characters & special sequences

GUI Programming

  • Introduction to tkinter programming
  • Tkinter widgets
  • Tk, label, Entry, Textbox, Button
  • Frame, messagebox, filedialogetc
  • Layout managers
  • Event handling
  • Displaying image

Multi-Threading Programming

  • Multi-processing v/s Multi-threading
  • Need of threads
  • Creating child threads
  • Functions /methods related to threads
  • Thread synchronization and locking

Statistics, Probability & Analytics:

Introduction to Statistics

  • Sample or population
  • Measures of central tendency
  • Arithmetic mean
  • Harmonic mean
  • Geometric mean
  • Mode
  • Quartile
  • First quartile
  • Second quartile(median)
  • Third quartile
  • Standard deviation

Probability Distributions

  • Introduction to probability
  • Conditional probability
  • Normal distribution
  • Uniform distribution
  • Exponential distribution
  • Right & left skewed distribution
  • Random distribution
  • Central limit theorem

Hypothesis Testing

  • Normality test
  • Mean test
  • T-test
  • Z-test
  • ANOVA test
  • Chi square test
  • Correlation and covariance

Numpy Package

  • Difference between list and numpy array
  • Vector and matrix operations
  • Array indexing and slicing

Pandas Package

Introduction to pandas

  • Labeled and structured data
  • Series and dataframe objects

How to load datasets

  • From excel
  • From csv
  • From html table

Accessing data from Data Frame

  • at &iat
  • loc&iloc
  • head() & tail()

Exploratory Data Analysis (EDA)

  • describe()
  • groupby()
  • crosstab()
  • boolean slicing / query()

Data Manipulation & Cleaning

  • Map(), apply()
  • Combining data frames
  • Adding/removing rows & columns
  • Sorting data
  • Handling missing values
  • Handling duplicacy
  • Handling data error

Categorical Data Encoding

  • Label Encoding
  • One Hot Encoding
  • Handling Date and Time

Data Visualization using matplotlib and seaborn packages

  • Scatter plot, lineplot, bar plot
  • Histogram, pie chart,
  • Jointplot, pairplot, heatmap
  • Outlier detection using boxplot

Machine Learning:

Introduction To Machine Learning

  • Traditional v/s Machine Learning Programming
  • Real life examples based on ML
  • Steps of ML Programming
  • Data Preprocessing revised
  • Terminology related to ML

Supervised Learning

  • Classification
  • Regression

Unsupervised Learning

  • clustering

KNN Classification

  • Math behind KNN
  • KNN implementation
  • Understanding hyper parameters

Performance metrics

  • Math behind KNN
  • KNN implementation
  • Understanding hyper parameters


  • Math behind regression
  • Simple linear regression
  • Multiple linear regression
  • Polynomial regression
  • Boston price prediction
  • Cost or loss functions
  • Mean absolute error
  • Mean squared error
  • Root mean squared error
  • Least square error
  • Regularization

Logistic Regression for classification

  • Theory of logistic regression
  • Binary and multiclass classification
  • Implementing titanic dataset
  • Implementing iris dataset
  • Sigmoid and softmax functions

Support Vector Machines

  • Theory of SVM
  • SVM Implementation
  • kernel, gamma, alpha

Decision Tree Classification

  • Theory of decision tree
  • Node splitting
  • Implementation with iris dataset
  • Visualizing tree

Ensemble Learning

  • Random forest
  • Bagging and boosting
  • Voting classifier

Model Selection Techniques

  • Cross validation
  • Grid and random search for hyper parameter tuning

Recommendation System

  • Content based technique
  • Collaborative filtering technique
  • Evaluating similarity based on correlation
  • Classification-based recommendations


  • K-means clustering
  • Hierarchical clustering
  • Elbow technique
  • Silhouette coefficient
  • Dendogram

Text Analysis

  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words customization
  • Stemming and lemmatization
  • Feature extraction
  • Sentiment analysis
  • Count vectorizer
  • Tfidfvectorizer
  • Naive bayes algorithms

Dimensionality Reduction

  • Principal component analysis(pca)

Open CV

  • Reading images
  • Understanding gray scale image
  • Resizing image
  • Understanding haar classifiers
  • Face, eyes classification
  • How to use webcam in open cv
  • Building image data set
  • Capturing video
  • Face classification in video
  • Creating model for gender prediction


  • Two project using Python & ML
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