Data Mining and Machine Learning

About This Course: This course is about doing data science with Python, which immediately poses the question: what is data science? It is a term used for the cross-disciplinary set of skills, or tools, that are becoming increasingly important in many applications across industry and academia: it is fundamentally an interdisciplinary subject. Data science comprises three distinct and overlapping fields: statistics, computer science and the domain expertise— that is, a "classical" training in a subject which is necessary both to formulate the right questions in data science and to put their results in context. Applications of data science include election results reporting, stock returns forecasting, identification of microorganisms in microscope photos, and many more. The goal of this course is to give you the ability to apply tools of data science in your chosen subject area. Moreover, this course will concentrate more on the applications of the tools and less on the mathematical details.


Applications of machine learning include: adaptive websites; bioinformatics; brain-machine interfaces; classifying DNA sequences; computational anatomy; computer vision, including object recognition; information retrieval; internet fraud detection; marketing; machine learning control; machine perception; medical diagnosis; economics; online advertising; recommender systems; robotics; search engines; speech and handwriting recognition; financial market analysis, among others.


Training Cost: ₦ 155,000.00     


Who is this course for?

This course is for those who want to learn Python language with the aim of using it as a tool for data-intensive and computational science. The course is not meant to be an introduction to Python or to programming in general; it is assumed that the students have some basic exposure or familiarity with the Python language or, at list, another programming language. This course is meant to help the students learn to use Python's data science stack – libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools – to effectively store, manipulate, and gain insight from data.


This course is appropriate for professionals, graduates, master’s or research students in statistics, computer science, engineering, robotics, geographic information systems, economics, mathematical finance, computational biology, computational chemistry, physics or related quantitative fields or for individuals in other disciplines who wish to use statistical learning tools to analyse their data.


Background on Python

Python is a general-purpose and powerful programming language developed by Guido van Rossum in 1989. It is a high-level programming language and has a rich variety of native data structures such as lists, tuples, sets and dictionaries. Some of the main advantages of Python programming language include the following:

• It uses simple syntax, which makes writing Python programs fast. 

• It is free – Python is free of cost and open source, unlike commercial offerings such as Mathematica or MATLAB.

• Cross-platform support: Python is available for commonly available computer operating systems, including Windows, Unix, Linux and Mac OS X.

• Python has a large library of modules and packages that are available as part of its “standard library” that extends its functionality. Others modules and packages can be downloaded separately at no cost, including the NumPy, SciPy and Matplotlib libraries used in scientific computing.

• Python is relatively easy to learn.

• Python is flexible: It contains the best features from the procedural, object-oriented and functional programming paradigms.


Why Python for Data Mining and Machine Learning?

Over the last couple of years, Python has emerged as an important tool for scientific computing tasks, including the analysis and visualization of large datasets. This stems primarily from the large and active community of free third-party packages such as IPython for interactive execution and sharing of code,  Pandas for manipulation of heterogeneous and labelled data, NumPy for manipulation of homogeneous array-based data, Matplotlib for publication-quality visualizations, SciPy for common scientific computing tasks, Scikit-Learn for machine learning, and many more.


Course Dates: 
Wednesday 13 December – Friday 15 December 2017


Course Time: 9 am to 5 pm



Slingshot Tech Limited, 35 Moloney Street, Lagos Island, Lagos.


Prerequisites: Basic IT skills and basic programming skills in Python or any other programming language. Our Scientific Programming Using Python course would be an ideal introduction to this course. Also, a first year undergraduate-level mathematics or background in engineering, physics, mathematics, economics, finance or another applied science with some mathematical content will be helpful.



Information on how to book one or more of our courses can be found here .


Some Applications of Data Mining and Machine Learning with Python

   •     Business Analytics





   •     Spatio-temporal Data Mining for Mobility Patterns Discovery





   •     Sensor Data Mining: Are you walking or driving?





   •     Data Mining in Railway Industry





   •     Pattern Recognition and Biosynthetic Logic for Natural Product Synthesis





   •     Plate Number Recognition





   •     Face Recognition System





   •     Banks Investments into the Coal Mining Sector





   •     Credit Card Fraud and ID Theft Statistics





   •     Telecommunication Call Centre Modelling




Availability Type: 
Key Features
Learn the fundamentals of Data Science
Learn NumPy – the library that provides array facility for efficient storage and manipulation of dense data arrays in Python.
Learn Pandas – the library that provides the data storage facility for efficient storage and manipulation of labeled/columnar data in Python.
Learn Matplotlib – the library that provides capabilities for a flexible range of data visualizations in Python.
Learn Scikit-Learn – the library that provides efficient & clean Python implementations of the most important and established machine learning algorithms.
Learn the fundamental vocabulary and concepts of machine learning
Learn the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable.
Introduction to Machine Learning and Data Mining
Introduction to NumPy, Pandas, Matplotlib
Introduction to Scikit-Learn
Hyperparameters and Model Validation
Feature Engineering
Naïve Bayes
Linear Regression
Support Vector Machines
Random Forests
Principal Component Analysis
Manifold Learning
Gaussian Mixture
Kernel Density Estimation
Image Features