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Machine Learning

Machine Learning Course Overview

There is an increasing demand for skilled machine learning engineers across all industries. We recommend this Machine Learning training course for the following professionals.

Machine Learning Course Key Features
  • Developers aspiring to be data scientists or machine learning engineers
  • Analytics managers who are leading a team of analysts
  • Business analysts who want to understand data science techniques
  • Graduates looking to build a career in data science and machine learning.

Skills Covered

  • Supervised and unsupervised learning
  • Linear and logistic regression
  • Support Vector Machine
  • Decision tree
  • K-Means clustering
  • Deep Learning fundamentals
  • TensorFlow with Keras (API)
  • Image Classification
  • Text Classification

Machine Learning Course Curriculum

Eligibility

The Machine Learning certification course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

Pre-requisites

This Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.

Course Content
  • Python Programming Overview
    1. Python Basics
    2. Python Operators
    3. Python Flow-Control
    4. Python Functions
    5. Python OOPs
  • Python Libraries for Machine Learning
    1. Python pandas
    2. Python numpy
    3. Python matplotlib
    4. Python seaborn
    5. Python sklearn
  • Machine Learning
    1. Machine Learning Introduction
    2. Basics of Machine Learning
    3. Supervised Learning
      1. Simple Linear Regression
      2. Multiple Linear Regression
      3. Classification
      4. Logistic Regression
      5. Support Vector Machine
      6. Decision Tree
      7. Random Forest
      8. Naïve Bayes Classifier
      9. KNN(K-Nearest Neighbors)
    4. Un-Supervised Learning
      1. Clustering
      2. K-means Clustering
  • Deep Learning
    1. TensorFlow
    2. Keras
    3. Neural Network
      1. Artificial Neural Network
      2. Convolution Neural Network
      3. Recurrent Neural Network
  • Text Analytics

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Machine Learning Projects

  • Credit card fraud detection Machine Learning Project
  • New car sales prediction Machine Learning Project
  • Loan and Insurance Prediction Machine Learning Project
  • Iris Flowers Classification Machine Learning Project
  • BigMart Sales Prediction Machine Learning Project
  • Sales Forecasting using Walmart Dataset Machine Learning Project
  • Stock Prices Predictor Machine Learning Project
  • MNIST Handwritten Digit Classification Machine Learning Project
  • Stock Prices Predictor Machine Learning Project
  • Taxi Fair Prediction Machine Learning Project
  • Robot Processing Automation

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