Become a Machine Learning Engineer

BY
Board Infinity

Master the fundamentals of machine learning, such as natural language processing, deep learning, and computer vision.

Lavel

Beginner

Mode

Online

Duration

134 Hours

Fees

₹ 7500 60000

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based

Course overview

Become a Machine Learning Engineer courses are created by Board Infinity, an e-learning platform designed for learners to assist them in accomplishing goals to achieve their professional goals. Become a Machine Learning Engineer online classes by Board Infinity are designed for individuals who want guidance directly from industry experts and to enhance their practical understanding to become certified AI and ML specialists.

This course aims to give students a thorough understanding of both basic and advanced artificial intelligence and machine learning topics and approaches, such as deep learning, computer vision, natural language processing, generative algorithms, and convolutional neural networks. Applicants will have established experience in a variety of machine learning and artificial intelligence tools, including Power BI, Seaborn, Numpy, Pandas, Keras, Python, Maplplotlib, and Tensorflow, by the end of this course.

The highlights

  • Certificate of completion
  • Live online course
  • Case studies
  • Assignments
  • Projects

Program offerings

  • Online course
  • 1:1 mentoring
  • Learning resources. placement support
  • Accessible on mobile devices

Course and certificate fees

Fees information
₹ 7,500  ₹60,000

The fees for the course Become a Machine Learning Engineer is :

Fees componentsAmount
Original feesRs. 60,000
Discounted feesRs. 7,500
certificate availability

Yes

certificate providing authority

Board Infinity

What you will learn

Knowledge of artificial intelligence Machine learning Knowledge of deep learning Natural language processing Knowledge of python Knowledge of numpy

After completing the Become a Machine Learning Engineer online training, applicants will develop an in-depth knowledge of both basic and advanced artificial intelligence and machine learning principles. The principles of deep learning, computer vision, and natural language processing will be explored by applicants. Applicants will gain knowledge of generative algorithms and neural networks, such as convolutional neural networks. Applicants will also gain a thorough understanding of the abilities required to work with artificial intelligence and machine learning tools such as Python, Pandas, Numpy, Tensorflow, Matplotlib, Keras, and Power BI.

The syllabus

Week 1: Introduction to Machine Learning and Artificial Intelligence

  • Introduction to Machine Learning
  • Overview of Deep Learning and Artificial Intelligence
  • ML Applications and Real-world problem examples (How is it happening in the companies, Why so much hype, Why companies want it, How is it everywhere)
  • Future Scope and Jobs in Artificial Intelligence
  • Industry Views over Jobs and its Future Scope

Week 1: Foundations on Python

  • Getting started with Jupyter Notebook & Google Colab
  • Python Basics
  • Lists, Tuples, and Dictionaries
  • OOPs Terminology

Week 2: Deep Learning and Introduction to Tensorflow

  • Introduction to Deep learning
  • Artificial neural network
  • Gradient Descent and variants
  • Backpropagation
  • Introduction to Tensorflow and Keras
  • A first artificial neural network with Sequential API

Week 3-4: CNN Foundations

  • Introduction to CNN, convolutions, and pooling
  • CNN parameters discussion
  • Optimizers
  • Functional API
  • Writing first CNN on MNIST
  • Advanced CNN concepts
  • Types of Convolutions
  • Discussion about Receptive Field
  • Learning Rate Scheduler
  • Regularization techniques
  • Discussion about VGG, Inception, Resnet, Densenet architectures
  • CNN training techniques
  • Network Visualization Techniques discussion
  • LR finder, Cyclic Learning rate, Progressive Resizing, Test Time Augmentation
  • Intro to Transfer Learning and applications
  • Differential Learning rate
  • How to create a dataset from online

Week 5-6: Advanced Computer Vision

  • Object Detection
  • Introduction to Object detection
  • Two-stage detectors and one-stage detector
  • Faster-RCNN, YOLO, SSD
  • Bounding box Annotation tool
  • Training object detector
  • Focal loss discussion
  • Introduction to Image Segmentation
  • FCN, UNet, Deeplab-V3

Week 6-7: Generative Algorithms

  • Introduction to Generative Algorithms
  • Autoencoder, Variational Autoencoder, and GAN
  • DCGAN discussion
  • Introduction to different GAN architectures
  • Different Loss Functions for GAN’s

Week 7-8: Deployment on Cloud and Edge

  • Introduction to tflite/tfjs
  • Quantization and Model Compression
  • Mobilenet and squeeze net

Week 8-9: Mathematics for Machine Learning, EDA

  • Introduction to calculus
  • Introduction to Linear Algebra
  • Numpy
  • Pandas - Cleaning and Munging Data - Visualizing Data

Week 9-11: Machine Learning(Supervised)

  • Linear Models: Linear and Logistic Regression with Stochastic Gradient Descent
  • Lasso, Ridge regression
  • Probability-Based Models: Naive Bayes
  • Tree-Based Model: Decision Tree and Random Forest - Ensemble Methods: Bagging, Boosting - Proximity Based Models: K Nearest neighbor - Model selection

Week 11-12: Machine Learning(Unsupervised)

  • Clustering
  • K-Means
  • Hierarchical
  • PCA
  • Hyper Parameter Tuning

Week 12-13: NLP with Machine Learning

  • Introduction to NLP
  • Bag of words, tf-idf
  • Sentiment analysis
  • POS Tagging
  • Named Entity Recognition

Week 13-14: NLP with Deep Learning

  • Deep Learning in NLP
  • Introduction to RNN, LSTM
  • Word2vec
  • Language models

Week 15: Advanced NLP

  • Transformer Architecture
  • Transfer Learning in NLP
  • Introduction to BERT
  • Variants of BERT
  • Introduction to Hugging Face Library

Week 16: Career Services and Placement Preparation

  • Self Awareness
  • Industry Guidelines
  • LinkedIn Profile Building
  • How to become Kaggle Grand Master
  • Build your GitHub profile
  • Resume Building
  • Role Clarity
  • Life Skill Development
  • How to search for a Job?
  • Interview Cracking Techniques
  • Salary Negotiation

Instructors

Mr Ruble Joseph

Mr Ruble Joseph
Vice President
eClerx

B.E /B.Tech

Mr Mirza Rahim Baig

Mr Mirza Rahim Baig
Lead of Business Analytics
Flipkart Pvt. Ltd.

Mr Kunaal Naik

Mr Kunaal Naik
Marketing Operation Advisor
Freelancer

B.E /B.Tech

Mr Punit Shah

Mr Punit Shah
Data Science Lead
Freelancer

Other Bachelors, Other Masters

Mr Anil Chandra Naidu Matcha

Mr Anil Chandra Naidu Matcha
Co Founder
Freelancer

B.E /B.Tech

Ms Geetanjali Prasad

Ms Geetanjali Prasad
Head Data science
Freelancer

B.E /B.Tech

Mr Rishi Raghuvanshi

Mr Rishi Raghuvanshi
Senior Data Scientist
Freelancer

B.E /B.Tech, M.E /M.Tech.

Mr Dhiman Mandal

Mr Dhiman Mandal
Data Scientist
Ericsson

B.E /B.Tech, M.E /M.Tech.

Mr Pulkit Aneja

Mr Pulkit Aneja
Data Scientist
Amazon

Mr Vedant Dwivedi

Mr Vedant Dwivedi
Deputy Manager
Reliance Jio Infocom...

Mr Kishan Kanhaiya

Mr Kishan Kanhaiya
Data Scientist
Freelancer

B.E /B.Tech

Mr Anurag Mukherjee

Mr Anurag Mukherjee
Senior Analyst
Accenture

B.E /B.Tech

Mr Ravi Teja

Mr Ravi Teja
Co-Founder
Freelancer

B.E /B.Tech

Mr Rajanikant Ghate

Mr Rajanikant Ghate
Data Scientist
Freelancer

B.E /B.Tech, M.E /M.Tech.

Mr Abhishek Dubey

Mr Abhishek Dubey
Data Scientist
Freelancer

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