Machine Learning with Python Course in Dubai

Introduction to Machine Learning:

Machine Learning is nothing but making machines learn and think and act like humans. It is the process of enabling machines to learn from past experiences and to improve the
accuracy of outputs over time. It is hard for anyone to program each and every task, right? But with Machine Learning, you do not need to program each task; instead, the computer itself develops an algorithm according to the task to be performed using the available data.

Machine Learning is a branch of Artificial Intelligence that makes machines learn
automatically from experience without being explicitly programmed. Machine Learning
focuses on programming computers in such a way that they can access data and use it to train themselves. Python is the most fitting programming language that is extensively used in the field of Machine Learning..

Machine Learning with Python

What is Machine Learning?

Machine Learning is basically the process to collect real-world data, extract useful
information from it, and then take actions to perform certain tasks without manual
programming. It helps systems improve over time on their own by exploring various types
of real-world data. It also allows organizations to improve their business strategies by
knowing the insights that are extracted from the given business data.

Machine Learning with Python

Why Machine Learning?

To answer this question, first, we need to know how it was without it. What was the scenario before humans could come up with Machine Learning? To make any decision, humans had to work hard and struggle. How far can humans think? How far can a human remember all the data and make a perfect decision? Even after working hard, the outcomes were not satisfactory.

But, luckily, humans always come up with the latest technologies each day, and Machine
Learning is one of them. Here, humans could make a machine learn so that it can make its own decisions based on the data and previous experiences with high efficiency and

Why Python for Machine Learning?

Python is the most commonly used programming language for Machine Learning. But why?

Why is Machine Learning using Python? Letโ€™s try and answer this question.

Python has lately become the backbone of Machine Learning. It is an easy-to-use programming language compared to any other object-oriented language. It is often used for data mining and data analysis and supports the implementation of a wide range of Machine Learning models and algorithms. It is famous for its readability, and it also offers platform independence, which means that you can use the same code for any machine without changes. All of these make it the perfect language of choice for Machine Learning.

Machine Learning with Python

Who should attend the Machine Learning with Python 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

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.
Machine Learning with Python

Machine Learning with Python Training Curriculum:

1.Statistical Learning

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 m graphics analyses
  • Basics of probability via daily life examples
  • Marginal probability and its importance with respect to Machine Learning
  • Bayesโ€™ theorem and conditional probability including alternate and null
  • hypotheses


  • Statistical Analysis Concepts
  • Descriptive Statistics
  • Introduction to Probability
  • Bayesโ€™ Theorem
  • Probability Distributions
  • Hypothesis Testing and Scores
2.Python for Machine Learning

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 gplot


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

3.Introduction to Machine Learning

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


  • 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


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

5.Supervised Learning

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


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

6.Unsupervised Learning

Learning objectives

Learn about unsupervised learning techniques:

  • K-means Clustering
  • Hierarchical Clustering


  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering

7.Ensemble Techniques

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


  • 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

8.Recommendation Systems

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


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

Benefits of Machine Learning with Python Course in Dubai :

  • Machine Learning with Python Course in Dubai by our trained experts.
  • You will get both Institutional certificate and the KHDA certificate.
  • Spoton provides you the facility to schedule the classes according to your availability.
  • You will get the Machine Learning with Python study materials to study further.
Enquire About The Course

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    Frequently Asked Questions?

    • Machine Learning Course in Dubai helps communicate effectively
    • To face for the interviewsย 
    • To find a better job opportunity
    • We engage the students in more practical sessions with our certified trainers.
    • We provide Online and offline mode for Machine Learning Course
    • We have Cambridge certified trainers with corporate level experienced for Machine Learning Course.
    • You will get more practical knowledge than theory.
    • You will be trained by Machine Learning experts.
    • You will get both institutional certificate and the KHDA certificate.
    • Spoton provides you the facility to schedule the classes according to your availability.
    • You will get the study materials to study further.
    • Machine Learning course is about how we use the Python theoretical knowledge into practice.
    • Machine Learning Course is designed approximately one and half months.
    • The duration can be adjustable according to your needs.
    • If you need to complete the course as soon as possible we can provide you for fast track.
    • Machine Learning Class will depend on the studentโ€™s and the trainerโ€™s availability.
    • You can select any three days including both weekdays and weekends.
    • Timing will be flexible ( 2 hours per day)
    • It is open from 9.30am-9.00pm

    SpotOn Institute offer two Certificates:

    • Institutional certificate
    • KHDA certificate (Knowledge & Human Development Authority of Dubai)

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