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Machine Learning with Python Training in Sydney, Australia

Become an AI expert with our immersive Machine Learning with Python Course

  • Supervised & Unsupervised Learning, Regression & Classifications and more
  • Advanced ML algorithms like KNN, Decision Trees, SVM and Clustering
  • Build and deploy deep learning and data visualization models in a real-world project
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Grow your Machine Learning with Python skills

In this four-week course, you will dive into the basics of machine learning using python; a well-known programming language. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, and classifications and more.

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Highlights

  • 34+ Hours of Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Why Machine Learning with Python?

benefits of Machine Learning with Python

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire the complete machine learning course with Python skills and meet that need.

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Not sure how to get started? Let our Learning Advisor help you.

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Who should attend this course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules

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Machine Learning Course with Python Curriculum

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Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  

Topics

  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  

Hands-on

  • Learning to implement statistical operations in Excel

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of functions and how to operate on files to read and write data in Python  
  • Understand how to use Pandas - a must have package for anyone attempting data analysis with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 

Topics

  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 

Learning objectives
Get introduced to Applied Machine Learning in Python via real-life examples and the multiple ways in which it affects our society. You will learn: 

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling relates to Machine Learning 

Topics

  • Machine Learning Modelling Flow  
  • How to treat Data in ML  
  • Types of Machine Learning  
  • Performance Measures  
  • Bias-Variance Trade-Off  
  • Overfitting and Underfitting  

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp

Topics

  • Maxima and Minima  
  • Cost Function  
  • Learning Rate  
  • Optimization Techniques  

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp

Topics

  • Linear Regression Case Study  
  • Logistic Regression Case Study  
  • KNN Classification Case Study  
  • Naive Bayesian classifiers Case Study  
  • SVM - Support Vector Machines Case Study

Hands-on

  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers 
  • Predict if a patient is likely to get any chronic kidney disease based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  

Topics

  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study

Hands-on

  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 

Topics

  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study

Hands-on

  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 

Topics 

  • Introduction to Recommendation Systems  
  • Types of Recommendation Techniques  
  • Collaborative Filtering  
  • Content-based Filtering  
  • Hybrid RS  
  • Performance measurement  
  • Case Study

Hands-on

  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on the Machine Learning with Python Course

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python certification Course is one of the best machine learning with python courses. This course is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

Learn Machine Learning with Python through a curriculum designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of practical machine learning with python skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The Machine Learning with Python syllabus is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the learners through machine learning algorithms in python, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases, all in all making it the best machine learning with python course. 

Yes, our Machine Learning with Python certification course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

The complete Machine Learning Course with Python requires daily training hours. In addition to the training hours, we recommend spending about 2 hours every day, for the duration of the course.   

Machine Learning course with Python is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems 
  2. Software or Data Engineers interested in quantitative analysis with Python 
  3. Data Analysts, Economists or Researchers

There are no prerequisites for attending this Machine Learning with Python certification course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the complete Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below - 

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your advanced Machine Learning with Python skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

Our introduction to Machine Learning with Python course will give you an opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

Learn Machine Learning with Python at KnowledgeHut which is delivered through PRISM, our immersive learning experience platform, via instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.  

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on support@knowledgehut.com and we will be happy to get back to you. 

The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

prerequisites for Machine Learning with Python

Prerequisites for Machine Learning With Python

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

What you will learn in the Machine Learning with Python course

Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data.

Fundamentals of Machine Learning

Learn Machine Learning with Python, including Supervised and Unsupervised Machine Learning.  

Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model.

Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.

Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering.

Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance .

Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis.

Skills you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

What learners are saying

S
Shifa Al Kiyumi RTO Engineer
5
A useful course, I acquired knowledge about Python, Machine Learning Modeling Flow, Treating Data, Statistical Learning and other topics. I will use this training and even the recorded videos and materials from knowledge Hut for future projects.

Attended Machine Learning with Python workshop in July 2021

D
David Thompson Managers-Software Development
4

The Agile Master's Program by KnowledgeHut exceeded my expectations. The career advancement opportunities are immense, and the increased earning potential is a major plus. The program improved my job performance and gave me greater job security. Worth every penny!

Attended Agile Masters Certification Program workshop in June 2023

A
Ava Patel Project Manager
5

I'm thrilled with KnowledgeHut's Project Management Masters Program. The growing industry and high demand for project management professionals motivated me to join. The program's focus on certifications, like the PMP, gives a significant advantage in the job market. Excited for my project management career!

Attended Project Management Masters Certification Program workshop in June 2023

J
James Leo IT Analyst
5

I've always been interested in cybersecurity, and the Ethical Hacking Mastery Course by KnowledgeHut was the perfect starting point for me. I thoroughly enjoyed the hands-on exercises, especially in IoT hacking and session hijacking. I now feel equipped to pursue a career in ethical hacking. Thank you, KnowledgeHut! 

Attended Ethical Hacking Mastery Course workshop in May 2023

M
Michelle Rodriguez DevOps Engineer
5

Game-changer! Comprehensive modules, supportive instructors, and invaluable job assistance. Landed a fantastic Cloud Architect job. Thank you, KnowledgeHut!

Attended AWS Cloud Architect Masters Program Certification workshop in May 2023

M
Matt Connely Full Stack Engineer
5

The learn by doing and work-like approach throughout the bootcamp resonated well. It was indeed a work-like experience. 

Attended Front-End Development Bootcamp workshop in May 2021

M
Mirelle Takata Network Systems Administrator
5

My special thanks to the trainer for his dedication and patience. I learned many things from him. I would also thank the support team for their help. It was well-organised, great work Knowledgehut team!

Attended Certified ScrumMaster (CSM)® workshop in July 2020

R
Rafaello Heiland Prinicipal Consultant
5

I am really happy with the trainer because the training session went beyond my expectations. Trainer has got in-depth knowledge and excellent communication skills. This training has actually prepared me for my future projects.

Attended Agile and Scrum workshop in April 2020

Machine Learning with Python Course in Sydney

Sydney  has  transformed  from  a  colonial  outpost  to  a  major  economic  and  cultural center in not only Australia but in the world. Sydney’s advanced market economy is a contribution  of  diverse  sectors,  such  as  manufacturing,  tourism,  and  finance.  The technology  sector  also  has  a  contribution  in  the  phenomenal  transformation  of  the city. To find growth opportunities in the city’s IT sector, register for the data analysis training  using  Python  in  Sydney.  This course  is  beneficial  for  software  developers, programmers, web masters, and anyone who wants to get a thorough understanding of Python and its role in data analysis and machine learning.

 Python  is a fast-growing  programming  language  that  allows  clear  programs  on both small  and  large  scales.  Top  programmers  and  developers  consider  Python a language  of  choice.  Its  ease  of  learning  and  simplicity  in  use  make  it  so  popular around the world, in turn making the data analysis using Python course in Sydneya popular training for skill development.

 A New Alternative

 Python   has   various   design   features   that   highlight   clarity   of   syntax,   easy comprehension, and easy readability, making it an integral part of data analysis and machine  learning.  The machine  learning  training  using  Python  is  an online  training offered by KnowledgeHut that is structured to help you understand the importance of Python  and  its  packages  in  data  analysis.  You  will  learn the  various  aspects  of Python  packages  essential  for  data  analysis  solutions.  Some  of  those  aspects include  different  types  of  plots  in  Matplotlib,  signal  and  image  processing  in  Spicy and  Numpy,  model  selection  in  scikit-learn,  and  vectorized string  operations  in Pandas.   We   make   machine   learning   using   Python   course   in Sydney, a comprehensive  training  course,  at  the  end  of  which  you will  receive  a  course completion certification.

Keeping Ahead of the Curve

The data  analysis  using  Python  course  in  Sydney  includes  hands-on  assignments and  project  work  on  data  analysis  scenarios  that  make  learning  easy  and  fun.  The certified instructors and industry experts delivering the training reinforce the concepts taught  in  the online  classes  through  interactive  sessions.  By  the  end  of  the  course, you  will  be  aware  of  the  techniques  of  using  Python  packages  such  as  Pandas, Spicy,  and  Numpy for  data  analysis and  modelling.  You can use  your  knowledge of the analytics libraries with Hadoop ecosystems. You will have appropriate strategies when  you  are  faced  with  scenarios  involving  reducing  data  dimensions  or  selecting the right data models.

KnowledgeHut Empowers You

When  you  register  on  KnowledgeHut,  you  can  be  sure  that  you  will  get  a  compete learning  experience.  You  will  get  access  to  e-learning  material,  a  downloadable  e-book, and a copy of the slide deck online.

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