IOAC-ML Training Overview


Learn more about machine learning and deep learning in association with TIH IIT-Patna and NIELIT, a comprehensive online live training programme as a Industry Oriented Advance Certification in Machine Learning. This course will learn you the fundamentals of programming using machine learning techniques. The procedure that was previously performed by workers will need to be automated, however this cannot be done without first understanding of machine learning and deep learning ideas.

Data science and analytics is included which is designed by TIH IIT-Patna and NIELIT for IOAC-ML curriculum since it is crucial for drawing conclusions from data. KVCH in association with TIH IIT-Patna and NIELIT IOAC-ML program prepares students to become leaders in a wide range of machine learning applications. Deep Learning, graphical modelling, and reinforcement learning are just a few of the cutting-edge AI and machine learning techniques covered in our course.

Benefit from a tailored mentoring programme, placement assistance, and more as part of your 80 hours of online training. Working professionals are welcome to enrol in KVCH in association with TIH IIT-Patna and NIELIT Machine Learning and Deep Learning course. You will learn Machine Learning ideas from the initial concept due to the combination of a top-notch curriculum and one-on-one guidance from professionals in the field.

After finishing IOAC-ML course, you will get a programme certificate from TIH IIT-Patna recognizing your mastery of state-of-the-art machine learning techniques and establishing your credibility as a machine learning expert in your organisation.

Machine Learning admin training

When you enrol in our Machine Learning Training Program , you can master the following:

  • Gain practical experience via the course's emphasis on problem-solving activities.
  • Expertise in both unsupervised and supervised machine learning (ML) models.
  • In-depth research on the field of Machine Learning algorithms.
  • Developing Deployable AIML Models Using Cutting-Edge Image Processing.
  • Implemented technologies include state-of-the-art computer vision, sequential natural language processing, and generative adversarial networks.
  • Establishing a dashboard for monitoring AIML projects
  • Python for Data Science and Probability Analysis

The industry oriented advanced certification- machine learning course opens up various opportunities for your career. Those with competence in machine learning have a vast array of work opportunities accessible to them. If you have expertise in the sector, a machine learning expert role pays well. You can pursue the following professions:

  • Machine Learning Engineer
  • Data Scientist
  • NLP Scientist
  • Computational Linguist
  • Software Developer

The field of machine learning is one area that has seen tremendous progress in today's technology-driven society. Let's discuss the global salary range for machine learning experts. Experts in machine learning can earn anywhere from an attractive salary to a comfortable living, depending on their marketability and demand.

  • The salary for machine learning in India might vary from Rs. 7.5 lakhs to Rs. 23 lakhs per year.
  • The salary range is determined by experience and additional abilities. By enrolling in industrial oriented certification, chances of an attractive salary are high.
  • As per LinkedIn, there are currently over 64,000 open positions for Machine Learning experts in the United States.
  • The annual salary for a machine learning scientist is $671,548.
  • Top machine learning engineers with less than a year of experience working in India earn around $500,000 per year.
  • Depending on their skill set, region, and demand, entry-level machine learning engineers generally charge $672,106 annually.
  • A mid-level engineer earns $1,173,074 a year.

Industry oriented machine learning courses are popular because they provide real-time live training experience to the students. It lets you gain hands-on knowledge through the hands-on problem solving based approach of the course. Students who learn machine learning through industrial training are more in demand and have better career options.


The top hiring companies after industry oriented advance certification machine learning are-

  • Tata Consultancy Services
  • Accenture
  • Amazon
  • Quantiphi
  • Infosys

This training course will provide you with hands-on experience in various topics related to machine learning. The market value of an industry oriented machine learning certification is high. Expertise in machine learning is in great demand across all business sectors, from senior positions to entry-level positions.

  • Become a certified ML professional from one of the leading institutions with essential skills in the in-demand fields of Machine Learning and Deep Learning.
  • The certification will open doors for various job opportunities.
  • You will work on projects which can be showcased in your CV.
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IOAC-ML Training Curriculum


Topics:
  • What is Data Science and what does a data scientist do. 
  • Various examples of Data Science in the industries and how Python
  • is deployed for  
  • Data Science applications 
  • Various steps in Data Science process like data wrangling, data exploration and   selecting the model
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Topics:
  • Introduction to a basic construct in Python 
  • Understanding indentation like tabs and spaces 
  • Code comments like Pound # character, names and variables 
  • Python built-in data types like containers (list, set, tuple and dict), numeric (float, complex, int), text sequence (string), constants (true, false, ellipsis) and others classes  
  • instances, modules, exceptions and more) 
  • Basic operators in Python like logical, bitwise, assignment,
  • comparison and more, slicing and the slice operator 
  • Loop and control statements like break, if, for, continue, else, range() and more.
  • List List comprehension and Errors and Exception Handling
  • Map and Filter
  • File Handling

Topics:
  • Introduction to mathematical computing in Python 
  • What are arrays and matrices, array indexing, array math, ND-array object 
  • Array, different ways of creating array, multidimensional arrays
  • Reshaping arrays, mathematical operations with arrays
  • Accessing component, transpose,insert, change values of arrays
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Topics:
  • Fundamentals of dataframes and Data types, Reading in data, size,
  • shape, getting index & columns of data,Slicing data, replacing
  • columns, filtering, joining, pivoting and sorting data.
Data Wrangling:
  • Missing value handling, data formatting, data normalization, binning, Turning categorical variables to quantitative, Joins
EDA:
  • Descriptive Statistic, Summarizing data, remove duplicates, group by on data, Correlation and methods on dataframe

Topics:
  • Probability Refresher (Bayes Theorem)
  • Descriptive Statistics - Mean,Mode,Median
  • Variance and Standard Deviation
  • Percentiles, Quartiles and Interquartile Range (IQR)
  • Confidence Interval
  • Central Limit Theorem
  • Correlation and Covariance
  • Probability Distribution (Normal, binomial)
  • Hypothesis Testing and Statistical Significance
  • Null and Alternative Hypothesis
  • Z test, T test, Chi Square test
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Topics:
  • What is exploratory data analysis and building of hypothesis, plotting and other techniques.
  • Getting Started with Matplotlib
  • Pyplot
  • Figure class
  • Axes Class
  • Setting Limits and Tick labels
  • Multiple Plots
  • What is a Legend?
  • Creating Different Types of Plots
  • Line Graph
  • Bar chart
  • Histograms
  • Scatter Plot
  • Pie Chart
  • PairPlot
  • Working with Images
  • Customizing Plots in Matplotlib
  • More articles on Matplotlib
  • Exercises, Applications and Projects
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Topics:
  • Need of Machine Learning
  • Introduction to Machine Learning
  • Types of Machine Learning, such as supervised, unsupervised and
  • reinforcement learning
  • Why Machine Learning with Python and applications of Machine
  • Learning.
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Topics:
  • What is supervised learning, classification 
  • Decision Tree, algorithm for Decision Tree induction 
  • Confusion Matrix 
  • Random Forest 
  • Naïve Bayes, working of Naïve Bayes, how to implement Naïve Bayes Classifier 
  • Support Vector Machine, working process of Support Vector Mechanism 
  • What is Hyper Parameter Optimization 
  • Comparing Random Search with Grid Search
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Topics:
  • Introduction to supervised learning
  • Types of supervised learning - regression and classification
  • Introduction to regression
  • Simple linear regression
  • Multiple linear regression, 
  • Assumptions in linear regression, and math behind linear regression.
  • Hands-on Exercise – Linear Regression and Train-Test Implementation
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Topics:
  • Introduction to classification
  • Linear regression vs logistic regression
  • Math behind logistic regression with detailed formulas
  • log it function and odds
  • Confusion matrix and accuracy
  • True positive rate v/s false positive rate
  • Threshold evaluation with ROCR.
  • Hands-on Exercise – Logistic regression, Confusion matrix Implementation
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Topics:
  • Introduction to tree-based classification
  • Understanding a decision tree
  • Impurity function and entropy to understand the concept of
  • information gain for the right split of node
  • Gini index 
  • Overfitting
  • Pruning, pre-pruning, post-pruning, cost-complexity pruning
  • Introduction to ensemble techniques
  • Understanding bagging
  • Introduction to random forests
  • Finding the right number of trees in a random forest.
  • Hands-on Exercise – Decision tree Implementation and hyper
  • parameters in the random forest.
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Topics:
  • How a save a model using Pickle
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Topics:
  • Introduction to unsupervised learning, use cases of unsupervised learning 
  • What is K-means clustering, understanding the K-means clustering algorithm 
  • Hierarchical clustering and K-means clustering and how does hierarchical clustering work 
  • Types of unsupervised learning
  • Clustering and dimensionality reduction
  • Types of clustering
  • Introduction to k-means clustering
  • Math behind k-means.
  • Hands-on Exercise – K-Means
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Topics:
  • Importance of Dimensions
  • Why Dimensionality Reduction
  • PCA and its implementation
  • Hands On: - PCA
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Topics:
  • Role of Machine Learning in field of Artificial Intelligence
  • Deep Learning v/s Machine Learning 
  • Brief History of AI Recap: SL, UL and RL 
  • Classification and regression in supervised learning, 
  • Clustering and association in unsupervised learning
  • Algorithms that are used in these categories
  • Introduction to AI and neural networks
  • Deep Learning: Successes Last Decade 
  • What is AI and Deep Learning 
  • Discussion: Self-Driving Car Object Detection 
  • Applications of Deep Learning 
  • Challenges of Deep Learning
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Topics:
  • Multi-layer network introduction Regularization
  • Deep neural networks
  • Multi-layer perceptron
  • Overfitting and capacity
  • Neural network hyperparameters
  • Different activation functions used in neural networks: - ReLu, Softmax, Sigmoid, and hyperbolic functions
  • Back propagation, forward propagation, convergence, hyper parameters
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Topics:
  • Various methods that are used to train artificial neural networks
  • Gradient descent rule
  • Tuning the learning rate
  • Regularization techniques
  • Optimization techniques
  • Stochastic process
  • Vanishing gradients
  • Dropout layer 
  • Regression techniques: - including Lasso L1 and Ridge L2
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Topics:
  • Understanding how Deep Learning works
  • Activation functions
  • Illustrating perceptron
  • Perceptron training
  • Multi-layer perceptron
  • Key parameters of perceptron
  • Pytorch
  • Pytorch and Its Ecosystem
  • Use-case implementation
  • Batch normalization
  • Neural network training process customization.
  • Hands On: Build a Deep Learning Model Using Pytorch
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Topics:
  • Introduction to deep neural networks (DNNs)
  • Several building blocks of artificial neural networks (ANNs), 
  • The architecture of DNN and its building blocks
  • various parameters, layers, and optimization algorithms in DNN
  • activation functions.
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Topics:
  • What is a convolutional neural network?
  • Understanding the architecture and use-cases of CNN
  • Pooling layers 
  • Visualize CNN
  • How to fine-tune a convolutional neural network
  • Transfer learning
  • Kernel filter
  • Feature maps, and pooling
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Topics:
  • Introduction to Natural Language Processing (NLP)
  • Introduction to text mining
  • Introduction to Sentiment  
  • Natural Language Toolkit (NLTK) environment and text mining: its cleaning, pre-processing and text classification.
  • Hands-on Exercise – NLTK implementation
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IOAC-ML Training Features

Instructor-led Live SessionsInstructor-led Live Sessions

KVCH experts with in-depth knowledge create a focused learning environment by presenting learners with real-world industry problems and focusing on solutions.

Live Training SessionsLive Training Sessions

During our certified training, seasoned instructors and industry experts conduct remote sessions to share their extensive knowledge with the learners.

Flexible Curriculum Flexible Curriculum

Professionals can obtain in-depth knowledge of cutting-edge machine learning training by taking advantage of the availability of specialised certificates.

 Expert Support Expert Support

Through a ticketing system that operates around the clock, our technical support staff is available to answer any questions you may have.

Certification Certification

Upon finishing the course and the assigned tasks, you will be awarded a certificate from TIH IIT-Patna , recognising your accomplishment as a machine learning expert.

Assignments Assignments

There is a quiz at the end of each lesson that must be completed before the next lesson begins to test your understanding.

What are the benefits of our training program?

  • Live, interactive training by experts.
  • Curriculum that focuses on the learner.
  • Challenge-based, hands-on project work.
  • Testing of Expertise in a Variety of Areas.
  • Opportunities for team building.
  • Cost- saving training.
  • Convenient for your employees.
  • Completely tailor-made curriculum.
  • Post training support and query management.
  • Regular feedbacks to monitor training effectiveness.

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IOAC-ML Training FAQs

What is KVCH’s with TIH IIT Patna IOAC-ML course and how is it different ?

The IOAC-ML course is a mapped-out study plan designed to get you up to speed in Machine Learning Engineering and comes highly recommended by authorities in the field. This course provides industrial training to the students which is highly beneficial in today’s competitive era. Start your path to success as a Machine Learning Engineer with the aid of this training program.

Is this course suitable for beginners ?

Yes, this course is designed keeping every important aspect of machine learning in mind, so this course is appropriate for beginners as it will provide them with industrial training experience before actually working in a company.

What is the duration of this program ?

The total duration of this course is 80 HRS.

What is the mode of training ?

This course will be taught through online live training sessions.

Does the placement assistance guarantee me a job ?

Apparently not. If you need help finding employment, our program exists to make that a reality for you. It's a chance to see what kind of lucrative opportunities the business world has to offer and locate a career that fits your skills and experience. Your chances of being hired will always come down to how well you do in the interview and how well you fit the needs of the recruiter.

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What are the benefits of our training program?

Features/Benefits.

  • Live, interactive training by experts.
  • Curriculum that focuses on the learner.
  • Challenge-based, hands-on project work.
  • Testing of Expertise in a Variety of Areas.
  • Opportunities for team building.
  • Cost- saving training.
  • Convenient for your employees.
  • Completely tailor-made curriculum.
  • Post training support and query management.
  • Regular feedbacks to monitor training effectiveness.
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