Machine Learning using Python: A Practical Introduction

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Learn machine learning with our coding courses in Sydney.

Machine learning is the growing computer science that enables computers to act without being explicitly programmed to act. Using Python, a well-known programming language, this crash course helps you to design machine learning algorithms to help improve learning from data without human intervention. Get ahead of the rest and learn an increasingly valuable skill that will help you secure future work and adapt to an ever-expanding technological world.

Course outcomes

By the end of this course you will be able to:

  • Understand Data Structure (DS) and Conditional Statements/Loops
  • Understand the Syntax of the Conditional Statements/Loops
  • Understand what a function is
  • Understand commonly used Data Analysis library for Machine Learning
  • Understand Model Evaluation Metrics

Course Content

Day 1 – Basics of Python for Machine Learning

  • Introduction to python
  • Why Python for Machine Learning
  • Setting up Python environment & IDE (Anaconda)
  • Features/Advantages of Python
  • How to work with Jupyter IDE for Python

Getting started with Python

  • Basic Syntax
  • Writing your first “Hello World” program
  • Variable and Data Types
  • Data Type conversions
  • Operators – Arithmetic, Comparator, Logical and Membership operators
  • String Manipulation – Accessing Strings, Basic Operations on Strings, Slicing a string

Data Structure (DS) and Conditional Statements/Loops

  • Intro to different DS(Lists, Set & Tuples, Dictionaries)
  • Creating the data structures
  • Accessing elements in DS
  • Properties of DS (Mutability, ordering, indexing)
  • Working with different methods in the DS
  • Use cases on when to use which DS

Syntax of the Conditional Statements/Loops

  • Writing decision conditions(If,If-else)
  • Loops - (For loop, While Loop)
  • Control Statements using Break and Continue
  • Examples and Use Case for loops/conditional statements

Functions

  • What is a function?
  • Defining a function
  • Calling a function
  • Function Arguments
  • Inbuilt functions: abs, len, sum, type, format, min, max, input, print, sort

PANDAS (Commonly used Data Analysis library for ML)

  • Introduction to Pandas
  • Why Use Pandas?
  • What is a Pandas Series?
  • What is a Pandas Dataframe(DF)?
  • EDA (Exploratory Data Analysis) using pandas
  • Loading source files (excel, CSV, any format ..) into Pandas data frame
  • Getting Descriptive statistics from a dataset
  • How to find and replace missing values in a dataset?
  • Filtering duplicates and Dropping Null records in the dataset
  • Basic operations for analysing data

Visualisation

  • Introduction to Data Visualisation
  • Importance of data visualisation
  • Working with Python visualisation libraries
  • Creating different plots to visualise the data using Matplotlib (Scatter Plots, Line Plots, Histograms)
  • Plots (density plot – Data distribution, Box plot – outlier, Heat maps – correlation matrix)

Day 2 – Building the Machine Learning Model (Linear Regression)

  • What is Machine Learning(ML)?
  • Machine Learning Vs AI
  • How Machine Learning is impacting the world?
  • Classification of ML Models
  • Supervised Vs Unsupervised Learning

Basic Statistics for ML(Simple Interesting stats)

  • What is Statistics?
  • Why Stats is required?
  • What is an Outlier? Effect of Outlier on Model
  • Measure of Central Tendency (Mean, Mode, and Median)
  • Measure of Data Spread (IQR, Variance and Standard Deviation)
  • Covariance and Correlation

Linear Regression

  • What is Regression?
  • What is Linear Regression?
  • Dependant Vs independent variable
  • How to find the best fit line?
  • How best is the line?
  • Bias and Variance(Over Fitting Vs Underfitting
  • Multiple Linear Regression
  • Pros and Cons of Linear Regression
  • Case study to predict on Linear Regression

Building ML(Linear Regression) model using python sklearn library

  • Importing libraries
  • Load source data from files/API’s
  • Perform Exploratory Data Analysis on the dataset
  • Feature selection techniques
  • Identify the relationship between features using python plots(Scatter Plots)
  • Finding correlation matrix using Heat maps
  • Find and replace missing values
  • Find and remove duplicates/dropping Null records
  • Encode the Categorical data
  • Split the columns into dependant and independent
  • Split the dataset into train and test datasets
  • Build the linear regression model on train data
  • Predict using the model on test data

Model Evaluation Metrics

  • Why should we evaluate a model?
  • How to evaluate the performance of a ML model?
  • What is RMSE/MAE?
  • Model evaluation using R square/Adjusted R square values
  • Visualising model Error using plots

Decision Tree (Classification Model)

  • What is a Decision Tree?
  • How a human makes decisions vs. a machine making decisions?
  • How decision tree model works?
  • Interesting concept behind decision tree
  • Building the decision tree model
  • Evaluation of the classification model

ADD ON (Based on time constraints)

  • Classification Model – Showcase Random Forest Model
  • A Real-time Classification problem in Machine Learning under the Banking domain

Other Information

  • This is a bring your own device class, you will need to bring a laptop and charger to the class with the Latest version of Python.
  • This is a crash course in Machine Learning, the course facilitator will aim to fit as much in as possible but some of the above might not be covered in time. If you have a specific aspect of the program you are interested in learning then please let the tutor know.

This course has no current classes. Please join our waitlist and we will notify you when we have places available. Join Waitlist

This course has no current classes. Please join our waitlist and we will notify you when we have places available. Join Waitlist