Courses / Machine Learning With Python

Home / Machine Learning With Python

Machine Learning With Python

Machine Learning With Python

Why use Python for Machine Learning

Being a coder it's very important to select the right programming language for development. Machine learning is a core part of Artificial intelligence. The upcoming era will use completely Artificial Intelligence and then we need Machine learning for calculation. But for that which programming language we can use??

So we are using Python for Machine Learning. From development to deployment, python helps developer to produce the best software.  

Course Overview

  1. What is Python
  2. Python Variable
  3. Python Data Types
  4. Python Functions
  5. Python Classes and objects
  6. Python Modules
  7. Python Packages
  8. Python Exception Handling
  1. Install IDE
  2. Install Jupyter
  3. Install Spider
  4. Install Numpy Lib
  5. Install Pandas Lib
  6. Install Matplot Lib
  1. Starting with collection of data(datasets)
  2. Learning about Numpy and Pandas(Library of Python)
  3. Reading datasets and showing then in form of arrays to tables
  4. Learning MatplotLib Space for displaying these data in form of Graph or Pie Charts
  5. Learning about Preprocessing of data.
  1. Machine Learning Languages, Types, and Examples
  2. Where is machine learning being used in todays World
  3. Advantages and Disadvantages  of Machine Learning 
  4. Machine Learning vs Statistical Modelling
  5. Supervised vs Unsupervised Learning
  6. Supervised Learning Classification
  7. Unsupervised Learning
  1. Supervised Learning
  2. unsupervised Learning
  1. Random Forest
  2. K-Nearest-Neighbour 
  3. Naiive bayes 
  4. Decision Tree
  5. Reliability of Random Forest
  6. Gradient Descent 
  7. Advantages and Disadvantages of Decision Tree
  8. Regression Algorithm 
  9. Model Evaluation 
  10. OverFitting and Under-fitting of the data
  11. Gradient Boosting Machines
  12. Model Stacking
  1. K-Means Clustering
  2. Support Vector Machines
  3. Exponential Smoothing
  4. Linear and non Linear Classification 
  5. Moving Averages 
  6. Average Models
  7. Principal Component Analysis
  1. Bagging 
  2. Bagging Classifier
  3. Bagging Regressio
  4. Boosting
  5. Boosting Classifier
  6. Boosting Regression
  1. standrad definition of time Series
  2. Time Series Analysis
  3. Time Series Forecasting
  4. Time Series Data
  5. Time Series to make your understanding concrete
  1. Why Deep Learning?
  2. what is Nural network ?
  1. Recurrent Nets
  2. Deep Belif Nets
  3. Restricted Boltzmann Machine
  1. What is Plateform of deep learning
  2. H20
  3. Data GraphLab
  4. Library in Deep Learning
  5. Caffe
  6. TensorFlow