Machine Learning

Machine Learning

Overview

The world is changing their way to optimize their efforts and maximize the productivity. We AdHoc Networks offering Online / Classroom training in the domain of Machine Learning. In this course you will what exactly machine learning is and how you can use this in your real life scenario, in Addition to this you will machine learning Raspberry pi and AWS cloud also.

Audience : 

Candidates having Good Experience in data handling / Bigdata Hadoop / Data Science, also fresher candidate with Python or R knowledge can attend this.

Prerequisite :

Candidate must have knowledge of Python Programming language or R programming with basic knowledge of Linux platform.

Note:  It will be much beneficial to have knowledge of Big data Hadoop.

Training Certification Duration: 2 Weeks | 4 Weeks |

Internship Certification Duration: 6 Weeks | 4 Months | 6 Months

Course Content : 

    1. What is  Machine Learning

  • Introduction to machine learning
  • Understanding the need
  • Understanding Big data and machine learning
  1.   Working with Python for ML
  • Understanding why python is needed
  • Running the installer and use case
  • Linear regression with one variable
  • Understanding ML based Python libraries
  • Numpy, Matplotlib, Sci-py, Sci-kit Learn, Tensorflow
  • Keras, NLP through NLTK
  1.    Machine learning techniques
  • Types of learning
  • Advice of applying machine learning
  • Machine learning System Design
  1.  Supervised learning
  • Regression
  • Classification
  • Case study  learning in regression
  • Case study learning in classification
  • Understanding and Implementation of SVM
  • Understanding and Implementation of KNN
  • Understanding and Implementation of Random Forest
  • Understanding and Implementation of Decision Tree
  1.  Unsupervised Learning
  • K-Mean Clustering
  • Recommender Systems/Engines
  • Deep learning
  • Sentiment Analysis
  • Artificial Neural Networks
  1.  Deep learning
  • Searching for image
  • Playing games with dockers and containers
  • Drawing  graphs with R
  1.  Machine with Apache Spark
  • Spark core
  • Spark architecture
  • Working RDD’s
  • ML with spark and Mlib
  1.  Neural Networks analysis
  • Understanding neural networks
  • Data learning and machine predictions
  1. Computer Vision
  • Understanding image processing
  • Image processing with OpenCV
  • Object and Face detection with OpenCV, Karios, CNN
  • Realtime object detection with Darknet using Coco dataset
  • Object detection using YOLO (You Only Look Once)
  • Tensorflow usage for Image processing
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