Machine Learning

They all require AI & Machine Learning, which is exactly what Xplore IT Corp has got in store for you.

So, what’s the buzz word “machine learning” mean?

Machine learning is a sub-set of AI and is based on algorithms that improve automatically through experience.

Simply put, machine learning is about allowing machines to help you analyse the bigger data faster and make better decisions. ML algorithm helps one understand how the products are being used so that they can customize them according to the client’s choice on large scale.

The most common use case for machine learning is actually for data classification, followed by search relevance, content relevance and computer vision. Not just that, Machine learning has played a vital role in advancement of Artificial Intelligence that serves all kinds of fields and has quickly become one of the most popular technology as various industries ranging from IT, gaming, finance, robotics, healthcare, manufacturing have already set Machine learning technology in practice and will be the future with great potential.

Traditionally Machine learning enables data scientist and engineers to focus on specific data points, analyse & solve problems in turn create a better experience for clients.

Let’s take Netflix for example, In order to recommend what you may be most interested in watching next on Netflix, Team Netflix have deployed machine learning algorithms that associate your preferences with that of users with similar tastes.

Another good example is the automotive industry which is excelling in Machine Learning by making safe driving a reality. Giant players like Nissan, Tesla, Google and many other companies are using Machine learning to bring novelty to their cars. Voice recognition, IOT and high-tech camera in combination with Machine Learning are expected to make the self-driving cars a reality where you would sit in the car and tell the destination. The car will find the best route and drive you safely to the desired location. It really is remarkable what Machine learning is expected to achieve in this sector.

The scope of Machine learning in combination with other technologies is just promising and definitely one of the best career choices of this century.  For the ones who is continuously in quest for learning and research, can build a career in Machine Learning with huge pay scale as rewards. The future scope of this domain lies in automation industry and thus to bring a revolution drastic change.

With the best in industry training Machine Learning Course, we at Xplore IT Corp offer you the entire Machine Learning Toolkit used by the big players of the tech industry giving you an edge.

The Significant features of this Machine Learning Certification Course are:

We provide Diploma Program for Fresher and professionals who want to start career in Artificial Intelligence & Machine Language where the course covers python, Artificial Intelligence, Machine Learning and Data science comprehensively.

A well updated, Experienced and well-equipped faculties with industry endorsed curriculum framed by experts provide students, developers and associate engineer from IT industry an overall insight about how to craft their careers forward with ML as their weapon.

Focuses on programming, algorithms, and learning real world AI applications using machine learning models.

Facilitation of Certification from JainX – Jain University approved by UGC on course completion.

Top 3 Key Takeaways of this Machine Learning Coaching Classes

Hands on knowledge in Python Programming, data, and machine learning models.

Vast coverage of data libraries and concepts used in building real world applications.

A Valid Diploma Certification


C & C++ Programming

  • History
  • Features
  •  Setting up path
  • Working with Python scripts
  • Basic Syntax
  • Variables and Data Types in scripting
  •  Operators
  • If , If- else, Nested if-else statements
  • For, While loops
  • Nested loops
  • Control Statements
  • Accessing Strings
  • Basic Operations
  • String slices
  • Function and Methods
  • Accessing list
  • Operations
  • Working with lists
  • Function and Methods
  • Accessing tuples
  • Operations
  • Working
  • Functions and Methods

⦁ Accessing values in dictionaries
⦁ Working with dictionaries
⦁ Properties
⦁ Functions

  • Defining a function
  • Calling a function
  • Types of functions
  • Function Arguments
  • Anonymous functions
  • Global and local variables
  • Importing module
  • Math module
  • Random module
  • Packages
  • Composition
  • Printing on screen
  • Reading data from keyboard
  • Opening and closing file
  • Reading and writing files
  • Functions
  •  Exception
  •  Exception Handling
  •  Except clause
  •  Try ? finally clause
  •  User Defined Exceptions
  • Class and object
  • Attributes
  •  Inheritance
  •  Overloading
  •  Overriding
  •  Data hiding
  •  Match function
  •  Search function
  •  Matching VS Searching
  •  Modifiers
  •  Patterns
  • Architecture
  • CGI environment variable
  • GET and POST methods
  • Cookies
  • File upload
  • Connections
  • Executing queries
  • Transactions
  • Handling error
  • Socket
  • Socket Module
  • Methods
  • Client and server
  • Internet modules
  • Thread
  • Starting a thread
  • Threading module
  • Synchronizing threads
  • Multithreaded Priority Queue
  • Tkinter Programming
  • Tkinter widgets
  • Mail Communication in python scripts


  • What is Django?
  • DRY programming: Don’t Repeat Yourself
  • How to get and install Django
  • Setting up database connections
  • Managing Users & the Django admin tool
  • Django URL Patterns and Views
  • Designing a good URL scheme
  • Generic Views
  • Django Forms
  • Form classes
  • Validation & Authentication
  • Advanced Forms processing techniques
  • Unit Testing with Django
  • Using Python’s unittest2 library
  • Test
  • Test Databases
  • Scrapy – Overview
  • Scrapy – Environment
  • Scrapy – Spiders
  • Scrapy – Item Pipelines
  • Scrapy – Link Extractors
  • Scrapy – various output consoles
  • Define item
  • First spider project
  • Crawling Content
  • Extracting item
  • Scraped data
  • What is Flask?
  • Flask – Overview
  • Flask – Environment
  • Flask – Application
  • Flask – Routing
  • Flask – Variable Rules
  • Flask – URL Building
  • Flask – SQLite
  • Flask – SQLAlchemy
  • The core: Image – load, convert, and save
  • Smoothing Filters A – Average, Gaussian
  • Smoothing Filters B – Median, Bilateral
  • OpenCV 3 with Python
  • Image – OpenCV BGR: MatplotLIB
  • Basic image operations – pixel access
  • Python – Signal Processing with NumPy
  • Signal Processing with NumPy I – FFT and DFT for sine, square
  • waves, unitpulse, and random signal
  • Signal Processing with NumPy II – Image Fourier Transform: FFT & DFT
  • Inverse Fourier Transform of an Image with low pass filter: cv2.idft()
  • Image Histogram
  • Video Capture and Switching colour spaces – RGB / HSV
  • Adaptive Thresholding – Otsu’s clustering-based image thresholding
  • Edge Detection – Sobel and Laplacian Kernels
  • Canny Edge Detection
  • Hough Transform – Circles
  • Watershed Algorithm: Marker-based Segmentation I
  • Watershed Algorithm: Marker-based Segmentation II
  • Image noise reduction: Non-local Means denoising algorithm
  • Image object detection: Face detection using Haar Cascade
  • Classifiers
  • Image segmentation – Foreground extraction Grabcut algorithm
  • based on graph cuts
  • Image Reconstruction – Inpainting (Interpolation) – Fast Marching Methods
  • Video: Mean shift object tracking
  • Installation
  • Features and feature extraction – iris dataset
  • Machine Learning Quick Preview
  • Data Preprocessing I – Missing / Categorical data
  • Data Preprocessing II – Partitioning a dataset / Feature Scaling /
  • Feature Selection / Regularization
  • Data Preprocessing III – Dimensionality Reduction vs Sequential
  • Feature Selection / Assessing Feature importance via random forests
  • Data Compression via Dimensionality Reduction I – Principal
  • component analysis (PCA)
  • Data Compression via Dimensionality Reduction II – Linear
  • Discriminant Analysis (LDA)
  • Data Compression via Dimensionality Reduction III – Nonlinear
  • mappings via kernel principal component (KPCA) analysis
  • Logistic Regression, Overfitting & regularization
  • Supervised Learning & Unsupervised Learning – e.g. Unsupervised
  • PCA dimensionality reduction with iris dataset
  • Unsupervised Learning – KMeans clustering with iris dataset
  • Linearly Separable Data – Linear Model & (Gaussian) radial basis
  • function kernel (RBF kernel)
  • Decision Tree Learning I – Entropy, Gini, and Information Gain
  • Decision Tree Learning II – Constructing the Decision Tree
  • Random Decision Forests Classification
  • Support Vector Machines (SVM)


  • Serializing with pickle & DB setup
  • Basic Flask App
  • Embedding Classifier
  • Deploy
  • Updating the Classifier
  • Batch Gradient Algorithm
  • Perceptron model on the Iris Dataset using Heaviside step
  • Activation Function
  • Batch Gradient Descent Vs Stochastic Gradient Descent
  • Adaptive Linear Neuron using linear activation function with – batch
  • gradient descent method
  • Adaptive Linear Neuron using linear activation function with –
  • stochastic gradient descent (SGD)
  • Logistic Regression
  • VC (Vapnik-Chervonenkis) Dimension & Shatter
  • Bias – Variance trade off
  • Maximum Likelihood Estimation (MLE)
  • Neural Networks with backpropagation for XOR using one hidden
  • layer
  • minHash
  • if-idf weight
  • Natural Language Processing (NLP)
  • Sentiment Analysis
  • IMDb & bag-of-words
  • Tokenization, Stemming & stop words
  • Training & Cross Validation
  • Out-of-Core
  • Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)
  • Forward propagation
  • Gradient descent
  • Backpropagation of errors
  • Checking Gradient
  • Training via BFGS
  • Overfitting & Regularization
  • Deep Learning
  • Image Recognition (Image Uploading)
  • Image Recognition (Image Classification)
  • Theano, TensorFlow & Keras


  • Machine Learning using Python Programming
  • We will cover one real time project using Machine learning.


The duration of this course will be 3 MONTHS, with 2-hour sessions each day for a total of 120 hours


⦁ At the end of this course, an assessment will be organized among the participating candidates, and front-runners will be awarded a ‘Certificate of Excellence’.

Faculty Credential

Our Star faculty Mr. Gobinath A, who is a senior team member having relevant industry experience in IT industry is handling the program. His major strength is the unique combination of skill-set with many global certifications in Embedded Systems, IOT, Python, Machine Learning, Artificial Intelligence, Data Science now at your service.


90 days is the duration of the course.

Programming knowledge and networking knowledge is necessary. If you are a fresher, you may choose to learn programming and basics of data science before taking up Machine Learning.

With this Machine Learning coaching classes, We have designed an amazing hand on sessions for each theory part. Overall, 80% of the sessions will be practical and remaining sessions are theoretical explanations of the concepts, which is unavoidable.

We will be your rock overall, making your Machine Learning course fees worthwhile by providing Placement support will be provided as and when the requirements are available as per the industry demand.

Yes, but on very limited batches. You need to contact our office for batch information.

So throw your worries our way and let us enable you with the skills that today’s industry demand by our flagship programs to excel in your career. Enrol now in Best Machine Learning training course and join your dream company that’s waiting to hire you.

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