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Machine Learning with Python Certification Training
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Machine learning is a branch of artificial intelligence (AI) that allows computers to learn without having to be explicitly programmed. Machine learning is concerned with the creation of computer programs that can adapt to new data. We’ll study the fundamentals of machine learning and how to develop a simple machine learning algorithm in Python in this course.

Course Highlights

  • 40+ hours of instructor-led training
  • Covering the basics of Machine Learning
  • Learning latest tools for Machine Learning using Python
  • Applied learning

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Course Description


A machine can only understand the language of 0’s and 1’s. To input this learning behavior into it, we develop a Machine Learning Model. These models are equivalent to mathematical equations with an added ability to change their parameters if new data is supplied to them. Python has a variety of libraries like NumPy, pandas, seaborn, etc which are well suited for all the tasks involved in an ML model development. Most of these are data manipulation, model selection, model training, etc. Machine learning with Python deep dives into the basics of Machine learning using python as a well-known programming language. Through Machine Learning, you can innovate solutions for common problems, like spam filters, assistants for any personal problems, and any fraud detections. Machine Learning also promotes the growth of Artificial Intelligence.

Why Machine learning with Python with InfosecTrain?

InfosecTrain is one of the finest security and technology training and consulting organizations, focusing on a range of IT Security training and Information Security services. InfosecTrain offers complete training and consulting solutions to its customers globally. Whether the requirements are technical services, certification, or customized training, InfosecTrain is consistently delivering the highest quality and best success rate in the industry.

  • We offer entire certification-based training.
  • We have certified and highly experienced trainers who have an in-depth knowledge of the subject.
  • Our training schedule is flexible and we also provide recording of the lectures.
  • We deliver post-training support.
  • We also bring forth an interactive Q & A session.

Target Audience

  • Beginner with Application Development
  • Software Developers
  • Software Engineers


  • Prior knowledge of high-level programming language like Java or basics of Python
  • Know how to use IDEs


7 + 11 =

Course Objectives

  • Introduction
  • Regression
  • Classification and Clustering
  • Support Vector Machine
  • Tree
  • Ensemble Machine Learning

Course Content

Introduction and Regression      

  • Introduction
  • Anaconda
  • Regression
    • Scikit-Learn
    • Correlation Analysis and Feature Selection
    • Linear Regression with Scikit-Learn
    • Robust Regression
    • Evaluate Regression Model Performance
    • Multiple Regression
    • Polynomial Regression
    • Dealing with Non-linear Relationships
    • Data Pre-processing

Classification and Clustering      

  • Classification
    • Introduction to Classification
    • Understanding MNIST
    • Stochastic Gradient Descent (SGD)
    • Confusion Matrix
    • Precision
    • Recall
    • F1 Score
    • Precision Recall Trade-off
  • Clustering

Support Vector Machine and Tree          

  • Support Vector Machine
    • Support Vector Machine (SVM) Concepts
    • Linear SVM Classification
    • Support Vector Regression
    • Polynomial Kernel
  • Tree
    • Introduction to Decision Tree
    • Training and Visualizing a Decision Tree
    • Visualizing Boundary
    • Tree Regression, Regularization and Over Fitting
    • End to End Modelling

Ensemble Machine Learning      

  • Ensemble Machine Learning
    • Ensemble Learning Methods Introduction
    • Bagging
    • AdaBoost
    • Gradient Boosting Machine
    • XGBoost
    • Random Forests and Extra-Trees

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Course Benefits

  • The course will equip you with a thorough grasp and knowledge of Machine Learning.
  • It motivates you to improve your skills even more.
  • It also shows that you are serious about professional development and lifelong learning.
  • It will help you develop in your job while also increasing your pay.
  • It qualifies you to provide value to businesses.
  • This course opens doors because Machine Learning is expanding its realm.
  • It provides better solutions to problems and puts you one step ahead of the competitors in the employment market.

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1. What is Python Machine Learning (ML)?
Machine learning is a branch of computer science that employs statistical techniques to enable computer systems to learn from their previous experiences and improve their performance.
2. Is Python knowledge required for machine learning?
To use Python for machine learning, you must have a fundamental understanding of the language. Anaconda is a Python version that runs on all major operating systems, including Windows, Linux, and Mac OS X. It incorporates sci-kit-learn, matplotlib, and NumPy, making it a complete machine learning package.
3. How much Python knowledge is needed for machine learning?
You only need a basic understanding of Python to utilize it for Machine Learning, which includes concepts like writing to the screen, getting user input, conditional statements, looping statements, object-oriented programming, and so on.
4. Is Python difficult to use for Machine Learning?
Python is used for all machine learning applications. It’s not difficult to learn, but the amount and breadth of information you’ll need for the interview will be challenging. Python is used in the real world for data manipulation and modeling.
5. When it comes to learning Python, how long does it take?
Learning the fundamentals of Python programming, such as object-oriented programming, basic Python syntax, data types, loops, variables, and functions, can take anywhere from five to ten weeks on average.
6. Should I begin by learning Python or Machine Learning?
Each language has its own set of advantages. I’ve been using both languages for my Data Science and Machine Learning projects for the past two years. If you want to get into this industry, I strongly advise you to start with Python.
7. Is it possible to self-learn machine learning?
Despite the fact that there are many different skills to learn in Machine Learning, you can still learn ML all by yourself. Many courses are now available that will take you from having no prior experience of Machine Learning to being able to understand and execute ML algorithms on your own.
8. Is Machine Learning a viable career option?
Yes, machine learning is an excellent job choice. Machine Learning Engineer is the top job in terms of compensation, growth in listings, and overall demand, according to a 2019 research by Indeed. Machine learning is the appropriate career choice for you if you’re interested in data, automation, and algorithms.