Data Science and Machine Learning with Python

Name: Data Science and Machine Learning with Python
Price: 499.00 ZAR

Want to harness the power of big data using data science and machine learning? This five-day instructor-led course is the perfect start.

5 day course

Supporting material

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Data science and machine learning are two of the most important disciplines to emerge in technology in recent years, and at their heart is the understanding and management of data.

This five-day course will guide you through the world of data science (DS) and machine learning (ML) using the Python programming language and data management principles. At the end of the course you will have a thorough understanding of analytics and automation, and will have developed practical skills in Python targeted towards data analysis so you can create sophisticated models.

Through a combination of lectures, demonstrations and exercises, you will gain practical experience applying DS and ML techniques to real data. This course is essential for individuals who want to leverage the power of data science and machine learning to drive organisation development.

Course overview

Who should attend:

Python developers
Coders looking to upskill and focus on big data

Walk away with the ability to:

Set up a powerful environment for data science and machine learning tasks
Load, visualise and clean big data
Output and describe descriptive statistics
Create a range of graphs and data visualizations
Gain actionable insights from big data
Confidently assess structure and quality of the data
Carry out thorough exploratory data analyses
Define and apply a range of machine learning algorithms
Understand the end-to-end process of machine learning, from data to prediction
Use correct machine learning algorithms based on data criteria
Optimise machine learning models and assess efficacy
Describe the process, underlying maths, architecture and applications of neural networks
Create, optimise and apply your own deep learning models for image recognition

Prerequisites

To get the most of out of this course, you should have:

A working knowledge of python. You should be confident with basic variable types, functions, if/else, for loops and importing modules
Basic maths skills - understanding of mean, standard deviation, x/y graphs, logistic and linear regression are a plus.

Course agenda

Module 1: Introduction to Data Science

Basics of data science
Getting set up for data science
Introduction to jupyter notebook and gaggle
Numpy and pandas for data science
Loading and exploration of data
Visualisation techniques for data science
Practical - creating your first data science report

Module 2: Data Science -Preaparation and Actionable Insights

Variable types
Correcting data
Completing data
Creating data (feature engineering)
Converting data
Practical - real-life data set

Module 3: Machine Learning - Theory and Model Selection

Introduction to ML
Foundations - linear and logistic regression
Cost, loss, bias and variance
Usuing scikit learn
Practical - linear regression in practice
Commonly used ML models

Module 4: Applying Machine Learning Models

Real-world use cases
Practical - choosing and applying an ML model
Assessing results
Improving model output
Practical - your end-to-end ML project

Module 5: Deep Learning - Applying Neural Networks

A conceptual introduction to deep learning and neural networks
Underlying maths
Neural network architecture
Practical - applying NNs
Optimising NNs
Image classification using neural networks
Taking these skills forward

Book this course

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+27 (10) 5008676