Best Machine Learning Course Institute in Delhi, Noida & Gurugram

What You Will Learn in Machine Learing Course Training ?

Digital Paradize Machine Learning Training Course is Designed by Certified Experienced Machine Learning industry experts, and it covers in-depth knowledge on Machine Learning. Throughout this Machine Learning Training Course, you will be working on real-life industry use cases and live projects Source code. Participants completing the Machine Learning Course have plenty of job opportunities in the industry. Digital Paradize, recognized as the Best Machine Learning Course training institute in Delhi & NCR. Whether you are a project manager, a college student or I.T professional the best Machine Learning training institute in Delhi offers best training environment, expert Machine Learning trainers, and flexible training schedules for entire modules. .

10 Modules of Machine Learing Course

Machine Learning Course Content

Introduction to Machine Learning Course.

In the first module of Machine Learning Course, We will focus on developing your knowledge of Machine Learning basics and latest components of Machine Learning.
What is Machine Learning?
What is Data Mining?
What is Deep Learning?
What is Descriptive Analysis?
what is Predictive Analysis?

Python

In this module of Machine Learning Course, you will Master the skills of python programming in depth. Only a complete Machine Learning course has improved our students to secure a job in various MNCs.
Basics of python
Types of Variables in python
Numbers
Strings
Lists
Dictionaries
Statements
Looping
function

Anaconda Distribution

Anaconda Distribution is an open source it is the simplest way to implement Python/R data science and machine learning on Windows, Linux and Mac OS. With above 11 million users worldwide, it is the Best industry standard for developing, testing, and training on an individual machine, allowing data scientists to:
Instantly download 1,500+ Python/R data science packages
Manage dependencies, libraries, and environments with Conda
Develop and practice machine learning and deep learning models Analyze data with scalability and performance with pandas and NumPy
Visualize results with Matplotlib.

Working Framework Jupyter Notebook

Project Jupyter exists to develop open-source services, software and open standards for interactive computing across dozens of programming languages.
The Jupyter Notebook is an open-source web application that enables you to build and distribute documents that comprise live code, narrative text, visualizations, and equations. Uses include data transformation and cleaning, statistical modeling, numerical simulation, machine learning, data visualization, and much more.

NumPy

NumPy is the library for the Python programming language and a basic package for scientific computing with Python. It includes:
a powerful N-dimensional array object
sophisticated (broadcasting) functions
tools for integrating C/C++ and Fortran code
useful linear algebra, Fourier transform, and random number abilities
Besides its obvious scientific uses, NumPy can also be used as an effective multi-dimensional container of generic data. Arbitrary data-types can be defined. This provides NumPy to seamlessly and quickly integrate with a broad variety of databases.
NumPy is authorized under the BSD license, allowing reuse with few restrictions.
NdArray
ndim, shape, size, dtype, itemsize, data
Array Creation
Printing Arrays
Basic Operations
Universal Functions
Indexing, Slicing and Iterating
Shape Manipulation
Changing the shape of an array
Stacking together different arrays
Splitting one array into several smaller ones
Copies and Views
No Copy at All
View or Shallow Copy
Deep Copy
Fancy indexing and index tricks
Indexing with Arrays of Indices
Indexing with Boolean Arrays
The ix_() function
Linear Algebra
Simple Array Operations
Tricks and Tips
Automatic Reshaping
Vector Stacking
I/O in NumPy

Pandas

pandas is a popular and widely used software library written for the Python programming language used for data analysis and Manipulation. It offers time series and data structures and operations for manipulating numerical tables. It is free software published under the three-clause BSD license.
Pandas Basics
Object Creation
Viewing Data
Selection
Missing Data
Operations
Merge
Grouping
Reshaping
Time Series
Categoricals
Plotting
Getting Data In/Out
Gotchas
Essential Basic Functionality
Head and Tail
Attributes and Underlying Data
Accelerated operations
Flexible binary operations
Descriptive statistics
Function application
Reindexing and altering labels
Iteration
dt accessor
Vectorized string methods
Sorting
Copying
dtypes
Selecting columns based on dtype
Intro to Data Structures
Series
DataFrame
Panel
Deprecate Panel

Matplotlib

Matplotlib is a plotting library for the Python programming language which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. its numerical mathematics extension NumPy. Matplotlib can be used in the Python, Python scripts and IPython shells, web application servers and the Jupyter notebook.
Introductory
Intermediate
Advanced
Colors
Text
Toolkits
Scipy
Introduction
Basic functions
Special functions (scipy.special)
Integration (scipy.integrate)
Optimization (scipy.optimize)
Interpolation (scipy.interpolate)
Fourier Transforms (scipy.fftpack)
Signal Processing (scipy.signal)
Linear Algebra (scipy.linalg)
Sparse Eigenvalue Problems with ARPACK
Compressed Sparse Graph Routines (scipy.sparse.csgraph)
Spatial data structures and algorithms (scipy.spatial)
Statistics (scipy.stats)
Multidimensional image processing (scipy.ndimage)
File IO (scipy.io)

Seaborn

Seaborn is built on top of matplotlib and closely integrated with pandas data structures. It is used for Making Statistical graphics in python. seaborn offers:
Functional support for using certain variables to show observations or aggregate statistics.
A dataset-oriented Application Program Interface(API) for examining associations between multiple variables.
Options for visualizing bivariate or univariate distributions and for comparing them between subsets of data.
Automatic plotting and estimation of linear regression models for various kinds of dependent variables.
High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations.
Convenient views onto the overall structure of complex datasets.
Tight control over matplotlib figure styling with some built-in themes.
Tools for choosing color palettes that reveal patterns in your data
Seaborn points to make visualization a central part of examining and interpreting data. Its dataset-oriented plotting functions operate on data frames and arrays containing whole datasets and internally perform the important semantic mapping and statistical aggregation to create informative plots.
Plotting functions
Visualizing statistical relationships
Relating variables with scatter plots
Emphasizing continuity with line plots
Showing multiple relationships with facets
Plotting with categorical data
Categorical scatterplots
Distributions of observations within categories
Statistical estimation within categories
Plotting “wide-form” data
Showing multiple relationships with facets
Visualizing the distribution of a dataset
Plotting univariate distributions
Plotting bivariate distributions
Visualizing pairwise relationships in a dataset
Visualizing linear relationships
Functions to draw linear regression models
Fitting different kinds of models
Conditioning on other variables
Controlling the size and shape of the plot
Plotting a regression in other contexts
Multi-plot grids
Building structured multi-plot grids
Conditional small multiples
Using custom functions
Plotting pairwise data relationships
Plot aesthetics
Controlling figure aesthetics
Seaborn figure styles
Removing axes spines
Temporarily setting figure style
Overriding elements of the seaborn styles
Scaling plot elements
Choosing color palettes
Building color palettes
Qualitative color palettes
Sequential color palettes
Diverging color palettes
Setting the default color palette

NLTK(Natural Language Tool kit)

The Natural Language Toolkit(NLTK), is a set of libraries and programs for symbolic and statistical natural language processing for Python programs to work with human language data.It provides easy-to-use interfaces.

Language Processing and Python
Accessing Text Corpora and Lexical Resources
Processing Raw Text
Writing Structured Programs
Categorizing and Tagging Words
Learning to Classify Text
Extracting Information from Text
Analyzing Sentence Structure
Building Feature Based Grammars
Analyzing the Meaning of Sentences
Managing Linguistic Data
Afterword: Facing the Language Challenge

Data Preprocessing and Data Analysis

Data Cleaning or Data cleansing.
Data Integration.
Data Transformation.
Data Reduction.
Data Discretisation.
Data Visualisation.
Sql Queries.
Statistics.

Descriptive Statistics

Mean
Median
Mode
Variance.

inferential statistics

Linear Regression.
Binomial Distribution.
Normal Distribution.
Chi-Squared Test.
Probability.
Permutation and Combination.
Least Square .

Data Structures

Algorithms.
Aysymptotic Notations.
Greedy Algorithm.
Divide And Conquer.
Pointer.
Complexity.
Array.

Linked Lists

Linked Lists.
Doubly Linked Lists.
Circular Linked Lists.
Circular Doubly Linked Lists.

Stack and Queue

Stack Implementation
Queue Implementation

Searching Techniques

Linear Search
Binary Search
Interpolation Search
Hash Table

Sorting Techniques

Insertion Sort
Selection Sort
Bubble Sort
Quick Sort
Merge Sort
Heap Sort
Bucket Sort
Comb Sort
Counting Sort
Radix Sort
Shell Sort
Bitonic Sort
Cocktail Sort
Cycle Sort
Tim Sort

Graph

Graph Traversal Algorithm
Depth First Search ( DFS )
Breadth First Search ( BFS )

Tree

Tree Traversal
Binary Search Tree
AVL Tree
Spanning Tree
B Tree
B+ Tree

Recursion

Tower Of Hanoi
Fibonacci Seres

Machine Learning

Supervised Learning.
Classification Techniques.
Regression Techniques.
Dependent variable.
Independent variable.
Population.
Random sampling
Gradient Descent.
Cost Function.
Hypothesis function.
Least squares.
Regularization.
Correlation.
Training Data and Test Data.
Cross Validation.
Type I and type II error.
Interpolation and Extrapolation.
False Positive and False Negative.
Bias and Variance.
File Format.

Regression

Linear Regression With One Variable.
Linear Regression With Multiple Variable.
Polynomial Regression.
Logistic Regression.
Decision Tree Regression.
Random Forest Regression.

Support Vector Machine (SVM)

Hyper Planes
Support Vectors
Small margin
Large margin

Time Series Forecasting

Trends
Linear Trend and Non Linear Trend
Seasonal Trend
Cyclical Trend
Irregular Trend
Autoregressive ( AR )
Moving Average ( MA )
Autoregressive Moving Average ( ARMA )
Autoregressive Integrated Moving Average ( ARIMA )
Naive Bayes.
K Nearest Neighbours.

Unsupervised Learning

Clustering.
K means Clustering.
Hierarchical Clustering.
Mean Shift Clustering.

Reinforcement Learning ( AI )

Neural Network.
Convolution Neural Network.
Image recognition.
Video recognition.
Artificial Neural Network.
Single Layer Network.
Two Layer Network.
Feed Forward Layer Network.
Fully Connected Layer Network.
Natural Language Processing. ( NLTK )
Text Mining
Speech recognition
Autocomplete
Predictive typing
Spell checker
Grammer checker
Auto-correct
Duplicate detection
Spam detection
Recommendation System.
Collaborative Filtering.
Content Based Filtering.

Source Code

GitHub Source Code.
Kaggle Source Code.

Learn From Certified 9+ Years ExperiencedTrainner

Best Machine Learning Course in Delhi

Work on Live Projects
Free Backup classes
Certificate
100% Job Placement Assistance

Why CHoose Machine Learing Course?

Machine Learning Course has become a big game changer in most of the modern industries over the last few years .More than 75% of companies are investing or are planning to invest in Machine Learning in the next two years. Machine Learning jobs have been created in almost all industries but there is still a large number of unfilled jobs across the globe due to shortage of required skill.

Who is Eligible for Machine Learning Course training?

Any graduate who is passionate about the Machine Learning technology can learn, get trained and make a very successful career in Machine Learning Course.Some technical knowledge is needed that you can easily gain.
Our Machine Learing Course is Designed to train you from the scratch, provide hands on real projects and provide job grooming as well as job support.

What kind of jobs You will get after completing Machine Learing Course ?

Why Digital Paradize?

We Digital Paradize is a Team of 9+ yrs Experienced IT professionals. the reason is, we don’t give up. When a student commits to a course, we commit to the student. We believe you are joining Digital Paradize for learning knowledge that is useful and complete and which will help to you get a better job and better carrier We are passionately committed to deliver industry validated courses our every module is designed by industry experts after doing deep research on current trending requirements in the industry .

Guaranteed Job Assistance

Interview Preperation

Work on Live Projects

Certification Exam preperation

24x7 support

Flexible payment methods

Machine Learning Course in Delhi, Noida & gurugram

FAQ

Why choose Machine Learning Course?

Machine Learning Course has become a big game changer in most of the modern industries over the last few years. More than 75% of companies are investing or are planning to invest in Machine Learning in the next two years. Machine Learning jobs have been created in almost all industries but there is still a large number of unfilled jobs across the globe due to the shortage of required skill.
Machine learning is the gleaming star of the moment. With each industry seeing to apply AI in their field, considering machine learning opens a plethora of opportunities to develop machine learning applications in various verticals – such as image recognition, cybersecurity, face recognition, fraud detection or medicine.
many machine learning companies on the brink of hiring proficient Machine Learning engineers, Today Machine Learning is the brain behind business intelligence. Netflix announced the prize worth $1 million to the first person who will improve the accuracy of its recommendation Machine Learning algorithm by 10%.
This is a clear indication of how important even a slight improvement is in the accuracy of recommendation machine learning algorithms to increase the profitability of Netflix. Every customer-centric organization is looking to use machine learning technology and is the next big thing paving opportunities for IT professionals. Machine learning algorithms have become the treasures of business and consumers so if you want to put yourselves somewhere in the better position with salary growth of software engineers then this is the best time to learn Machine Learning.

Why should I Join DigitalParadize?

Digital Paradize Machine Learing Trainers are certified and have more than 9 years’ hand on experience on Machine Learning Course training. Our trainers are industry recognised experts With Experience on several global projects who have trained thousands of students and working professionals.Our Placement team is always active to inform students about ongoing Machine Learing job placements.

What is Machine Learing?

Machine Learning is an application of Artificial Intelligence. It allows software applications to become correct in predicting results. Machine Learning concentrates on the development of computer programs, and the primary goal is to enable computers to discover automatically without human interference. Google announces "Machine Learning is the future," and the future of Machine Learning is going to be extremely brilliant. As humans become more addicted to machines, we’re eyewitnesses to a new change that’s winning over the world, and that is going to be the future of Machine Learning.

Do you Offer Money Back Guarantee?

Yes,If you are not satisfied after taking 3 classes,you can ask Head of the department for refunding your fees.Digital Paradize Machine Learing Course is developed by Experts and The course Module are industry validated.So Overall you will get a updated course with 9+ years of Experienced Certified Trainers.

Do you provide any certificate and free software of Machine Learing?

Yes we will give you Digital Paradize Pvt Ltd. Machine Learing certificate with Cloudera certification study material and from the very first week you will get all the required software for your course. and on regular interval you will have your Assesment test which will help to improve your skills.

How much time it will take to master the concept of Machine Learing?

"When you start something new, you learn very fast. It takes a few days to reach the beginner level. From beginner to intermediate level, it takes weeks, and from intermediate to expert level, it takes months. So an average person need 3 months to master the skills of Machine Learing and crack the cloud era certification examination with dedicated study. our Machine Learing course modules are practical oriented what you need to do is just complete your Assignments and Projects on time and maintain punctuality in your classes of Machine Learing .try to avoid skipping a single class of Machine Learing although we have a back up class for students who unfortunately miss their classes with serious reasons.So you dont need to worry about your missed Machine Learing classes."

Why digital paradize known as the best institute for ML?

Digital Paradize is best Machine Learning Course institute in Delhi & NCR "There are so many reasons: 1. Our Machine Learing trainer have more than 9+ years of Experience. 2.Course Modules are designed by doing research on ongoing need of the industry. 3.We provide free backup classes. 4.We take assesment test after each module. 5.We provide job assistanc. Our placement team will keep updating you about the ongoing placement interviews until you get a job. We are work on each student to give them best training"

Do you provide live Project during the Course?

Yes Digital Paradize provides Projects with Source code at the end of the course

What kind of classes I will get in Digital Paradize?

Digital paradize Machine Learning course is based on 80% practical and 20% theory. Digital Paradize Machine Learning Course in Delhi & NCR provides a comprehensive introduction to Deep Learning, Data mining, Machine learning, and Statistical pattern identification. Topics cover: (i) Supervised learning (support vector machines, neural networks, parametric/non-parametric algorithms, kernels ). (ii) Unsupervised learning (dimensionality reduction, clustering, deep learning, recommender systems). (iii) Best practices in machine learning Course (bias/variance theory; innovation process in machine learning and Artificial Intelligence). The ML course will also contain various case studies and applications so that you will practically learn how to apply learning algorithms in various technologies like text understanding ( anti-spam, web search), database mining, medical informatics, computer vision, etc.

what are the prerequisites for Machine Learing Course?

It will be added advantage if you have following skills. 1.Any one programming language like c,c++,core java or paython 2.if you are familiar with linux operating system and its commands 3.Sql knowlege. 4.know the concepts of RDBMS. 5.puzzle solving skills. The developer part of Machine Learning Course requires in depth knowledge of coding and IT skills. Though Java is the primary language used in most of the Machine Learning frameworks. you can enroll yourself for prerequisite courses like core java and python in Digital Paradize before starting your Machine Learing course.

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core and Latest Components of Machine Learing Course

Machine Learing Course contains 10 Modules (70hrs class room training)