Machine Learning with Python Training in Kuwait city, Kuwait

Build Python skills with varied approaches to Machine Learning

  • 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 skills

In this four-week course, you will dive into the basics of machine learning using the well-known programming language, Python. 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

  • 48 Hours of Live 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

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Why Machine Learning?

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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 in-demand machine learning and Python skills and meet that need.  

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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 with Python Training Curriculum

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1.

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
2.

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 
3.

Learning objectives
Get introduced to Machine Learning 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  
4.

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  
5.

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 
6.

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
7.

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.  
8.

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 workshop 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 

The program is 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 immediately applicable 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 curriculum 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 algorithms, 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.

Yes, our Machine Learning with Python 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. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Machine Learning with Python course 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 course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the 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 newly acquired Machine Learning 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. 

By the end of the course, you will have the 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

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive 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 live, interactive 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 course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

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

Prerequisites for Machine Learning with Python training

  • 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 about 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.

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Skill 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

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Machine Learning with Python Course in Kuwait city

Machine Learning With Python Kuwait City

A bustling metropolis, Kuwait City is the capital of the Kingdom of Kuwait. Kuwait city is lined with striking boulevards, well-maintained gardens & parks, many high-rises and lavish hotels. It has a massive port, which is used for both, pleasure and commercial purposes. Kuwait City is the cultural, political and economic center for Kuwait. Most commercial activity happens in this region and this has led to a concentration of several top-notch organizations to set up operations in this region. Companies here are on the lookout for genuine talent that can work with data analysis and machine learning using Python. Hence it would be a worthwhile investment for individuals to consider e-learning program in data analysis using Python course in Kuwait City Obtaining a certification would make them highly employable and it will allow them a career leap Interested individuals can join the online classes run by KnowledgeHut, where the programmers undergo exhaustive training and get a detailed overview of both the theoretical and practical aspects of the subject. Python finds worldwide acceptance and is a language of choice for many developers. Easy to use and deploy, several companies use this methodology to build applications and for this, they need certified skilled professionals. Developers enrolled for the machine learning using Python course in Kuwait City will have a far higher chance of finding great jobs as this course will arm them with the ability to build applications of any size. There are plenty of opportunities for the right candidates in Kuwait City as the economy is progressing at a rapid pace. Programmers are assured of growth and higher incomes if they choose to specialize in data analysis machine learning with Python. Joining an online training program will be valuable for developers as it will expose them to the finer aspects of the subject and help them become experts in the field.

New Alternative

Python has gained a lot of interest off late and is a preferred choice for data analysis. The fact that it is open source, easy to install and learn has made it the common language for data science and development of web based analytic products. This methodology can be deployed on any scale or size and the design inherently displays syntax clarity along with simple comprehension and readability. In data science, the ability to analyze data with Python is critical and this tool can be used to create stunning visualizations. Keeping Ahead of the Curve A data analysis training using Python in Kuwait City will help programmers stay ahead of the curve because this program will ensure they get better jobs along with higher income. To aid such professionals, KnowledgeHut offers online classes, which is conducted by an experienced set of teachers. Doing this online course will ensure that professionals are well trained and adept at the use of this language.

Knowledge Hut Empowers You

The machine learning training using Python is available at a reasonable price point. The online modules, run by KnowledgeHut provide exceptional training that prepares developers to become high performing; whether at an exam or while building an application.

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