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Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with machine learning. This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. This hands-on workshop will take the audience from the basic steps of machine learning that is linear regression and logistic regression to more complex models such a decision trees and neural networks.

During the workshop, we will learn how to make supervised machine learning (classification and regression), when to use classification and when to use regression models and when to use classification models, all with practical and real datasets. Followed by unsupervised techniques such as hierarchical clustering and advanced AI neural network models.

Who Should Attend

  • Willing to learn machine learning algorithm with Python.
  • Wishes to move beyond the basics and develop an understanding of the whole range of machine learning                   algorithms.
  • With intermediate to advanced EXCEL knowledge who is unable to work with large datasets.
  • Who has a deep interest in the practical application of machine learning to real world problems.
  • Interested to present their findings in a professional and convincing manner.
  • Who wishes to start or transit into a career as a data scientist.
  • Who wants to apply machine learning to their domain.

Course Outline

Module 1

  • Understanding data types and structures.
  • What is the role of statistics and probabilities in machine learning
  • Preparing your data for machine learning.
  • Splitting your data in python for training and testing.

In the first part as probably, it could be our first time with python, we will work with simple objects in python and learn how to import data frames, how to clean the data and remove empty values or impute missing values. Or maybe transformation of the data and removing outliers. After that, we will understand why we need to split our data so we can have more confidence on the validity of our machine learning models. We will also know when to use seed randomizations.

Module 2

  • Working on a classification problem using several classification learning models.
  • Working on a regression problem using several regression models and also non-linear models.
  • Evaluation the results of the model.
  • Using cross validation to verify the effectiveness of the model.

This part will focus on the different categories of machine learning and when do we use classification, regression or clustering. We will start machine learning problems using classification techniques such as logistic regression and K-nearest neighbors, these techniques we will use on real time problems so audience can relation to problems from a business environment context. After that , we will start using regression models and decision tree models to predict a continuous problem. Finally, we will test the accuracy of the models and the concepts of over-fitting and why the models need to be flexible and not highly over-fitted. We will also use cross validation out of sample techniques to be more certain about the effectiveness of the machine learning model being formulated.

Module 3

  • Introduction to unsupervised learning.
  • What are the applications of unsupervised learning?
  • Unsupervised hierarchical clustering.
  • Introduction to neural networks.

Part three we will focus on unsupervised learning techniques and segmentation of the data based on their similarities. when do we apply unsupervised learning and use cases from business.

Finally, we will end the workshop with a simple example on neural networks , how propagation is being made ? what is gradient descent? and finally why its so powerful and provides better results than normal models.

Finally, we will test the accuracy of the models and the concepts of over-fitting and why the models need to be flexible and not highly over-fitted. We will also use cross validation out of sample techniques to be more certain about the effectiveness of the machine learning model being formulated.

Workshop Requirements

  • A good processing speed laptop.
  • Anaconda python download and install before the workshop for python version 3.7. (Link: https://www.anaconda.com/distribution/)


Course Materials

It is very important to analyze the need for Training along with the level of knowledge you require. This session give you a liberty to choose the modules you need, discuss it with the trainer how you want to be delivered, decide the place at your convenience and allocate number of hours to solve your problem. This does not include any development as a consulting but will include solution if you are facing a particular problem.