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Machine Learning

Expert in Machine Learning.More than 4 year


  • Lessons : 12
  • Length : 4 Months
  • Level : Basic
  • Category : Software Training
  • Started : 01-04-2019
  • Shift : 02
  • Class : 120

Machine Learning Training and Course Description

We will walk you through all the aspects of machine learning from simple linear regressions to the latest neural networks, and you will learn not only how to use them but also how to build them from scratch. Big part of this path is oriented on Computer Vision(CV), because it’s the fastest way to get general knowledge, and the experience from CV can be simply transferred to any ML area. We will use TensorFlow as a ML framework, as it is the most promising and production ready. Learning will be better if you work on theoretical and practical materials at the same time to get practical experience on the learned material.

Machine Learning Training and Course Syllabus

Module 1: Data Science Overview

  • Get introduced to Data Science with Python
  • Sectors of Python
  • Components of Python
  • Know about Exploratory Data Analysis(EDA) techniques
  • Data types for plotting
  • Learn about statistical and non-statistical analysis
  • Categories and processes of statistics
  • Data distribution and dispersion
  • Histogram
  • Testing
  • How to install Anaconda Python Distribution?
  • How to work with Python Data Types
  • Comprehend mathematical functions of Numpy
  • Acquire knowledge on SciPy sub package
  • Understand Pandas SQL operation
  • Get a knowhow on Scikit
  • Supervised Learning Model Considerations
  • Supervised Learning Models
  • Unsupervised Learning Models
  • Pipeline
  • Gain overview
  • Applications
  • Libraries of NLP
  • Introduction to Data Visualization
  • Line Properties
  • Decision Boundary
  • Types of Plots
  • Web Scraping and Parsing
  • Navigating options
  • Modifying the Tree
  • Understand Why Big Data Solutions Are Provided for Python
  • Hadoop Core Components
  • Python Integration with Spark using PySpark
  • Get introduced to Machine Learning concepts
  • logarithms
  • its applications
  • supervised
  • unsupervised
  • semi-supervised
  • reinforced machine learning techniques
  • Comprehend the meaning
  • process
  • importance of data preparation
  • feature engineering and scaling
  • datasets
  • dimensionality reduction
  • Overview of Linear Algebra
  • Eigenvalues
  • Eigenvectors
  • Eigen-decomposition
  • Calculus
  • Probability and Statistics
  • Know Linear Regression
  • Equations and Algorithms in this lesson
  • Gain knowledge on classification types such as SVM
  • KNN
  • Naive Bayes
  • decision tree
  • random forest
  • logistic regression
  • k-nearest neighbours
  • support vector machines
  • Clustering definition
  • Clustering algorithms
  • prototype-based clustering
  • K-means clustering example
  • Understand the meaning
  • importance of deep learning
  • Artificial Neural networks
  • TensorFlow