Machine Learning

Machine Learning


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 Rasp-berry 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 Big data Hadoop knowledge.

Duration :  60 Hours

Fee :  13,500 INR + 18% GST

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