Machine Learning With Python

Know your way around the Machine Learning With Python.

Machine learning has become the new buzzword in computer science circles and has even leaked out into the general technological world. The concept is novel and exciting. Teaching machines to learn sounds like something out of a science fiction novel. But it has been brought to reality by intelligent computer scientists who have made it possible to teach machines to learn.

Machine learning is a subset of Artificial Intelligence that is important enough in its own right to have a course on it. Through machine learning, we can write computer programs that change on their own without the manual intervention of programmers.

They change when exposed to new data. So simply by providing massive data sets to the computer programs, we can give them ‘memories’ of past experiences according to which they can make more informed and accurate decisions. This is done with the aid of complex and advanced statistical techniques which help machines decide which actions are more favorable for performing a given task and which are not so favorable for the same.

Machine learning with Python is very straightforward and easy to do. This is because the Python community has developed several libraries and frameworks to solve problems commonly faced by programmers in the field of Machine Learning.

Another plus point of Python is that its syntax is very sparse and minimal. Python uses whitespace for formatting and indentation. This makes Machine learning in Python even more convenient because a machine learning programmer will not have to struggle to learn a complicated or obscure syntax and will be able to pick up Python with ease.

There are several other good reasons to carry out machine learning using Python. Python as a language is full of powerful and clever features which will make it possible to accomplish more with lesser code.

Python contains functions, built-in data structures, lists, tuples, if-else condition blocks, a well-designed package and module system, dynamic typing, dynamic binding, etc. So using Python for machine learning is a very wise choice.

What you will learn -

Learn machine learning with Python by imbibing the following lessons -

  • Types of machine learning algorithms – The learner will learn Supervised learning, Unsupervised Learning, and Reinforcement learning algorithms. They will learn how to differentiate between the 3 and identify which kind an algorithm belongs to.
  • Data cleaning – The learner will learn how to clean the data by putting it through a process of modification and removal which will weed out all the incorrect, incomplete, irrelevant, duplicated, or improperly formatted data.
  • Model selection – The learner will learn how to select the appropriate machine learning model for a training dataset. He will learn how to weigh the pros and cons of different candidate machine learning models and select the right one for the problem set.
  • Linear regression – The learner will learn how to implement linear regression machine learning algorithms where they will have to deal with a continuous predicted output with a constant slope.
  • Classification – Learners will learn how to put a class label on a given example of input data. For example, sorting emails and classifying them as spam or not spam, or classifying handwritten characters as alphabets of the English language.
  • Clustering – Learners will learn how to implement clustering algorithms on datasets. Thus they will be able to find natural clusters or groups in data, such as a particular group of customers based on their transaction history and behavior.

Who should take this course?

Our Machine learning Python course is suitable for all classes of learners provided they have a technical background. One should have some basic prior programming experience. They should have a rudimentary grasp of Data Science, although it won’t be a big issue if they do not have it.

There is no educational qualification requirement since the course will teach all the basics from scratch and doesn’t involve many technical fields. One should have a strong inclination towards mathematics and statistics in particular.

Why Choose Careerera -

Our Machine Learning with Python course has several salient points, some of which are mentioned below.

  • The instructors - We have a policy of hiring the instructors only after subjecting them to a very rigorous screening process. They are tested extensively to make sure that they are experts in their subject matter and have well-developed teaching skills. They teach the learners using the latest teaching methodologies and techniques. They have decades of experience working in the machine learning field.
  • The course study materials - The instructors provide video lectures, notes, assignments, surprise quizzes, and capstone projects. The study materials are specially designed to help the learners engage with them in a beneficial way.
  • The course curriculum - The course curriculum has been created carefully and painstakingly to provide the most comprehensive and relevant learning experience to the learners. All the latest industry trends are included in the course.
  • The feedback - Our course is designed in such a way that the instructors get input from the learners in the form of assignments, surprise quizzes, and capstone projects. After receiving this input, they evaluate it with great care and attention and give feedback to the learners in return.
  • Available 24/7 - Our Machine Learning with Python course provides 24/7 support to the learners via video, call, email, and chat. The learners can ask any questions at any time through any of these channels and the instructors will respond to them. They can get their doubts cleared and get their queries resolved at any time.

Program Curriculum

courseID =264

See which topics you will have to assimilate.

  • 1
  • Introduction to Machine Learning using Python
  • 2
  • Introduction to Statistics & Probability
  • 3
  • Machine Learning-Supervised learning
  • 4
  • Machine Learning-UnSupervised learning
  • 5
  • Dimensionality Reduction-PCA Concept
  • 6
  • Text Mining And Sentimental Analysis
  • 7
  • Time Series Forecasting
  • 8
  • Industry Graded project

Why Careerera

Learn from the greatest faculty around the world.



Top Ranked Programs


Hiring partners


Best Faculty


Hiring Partners

We Assure You Job Assitance at the End of the Program.

Sign Up

8 + 5 =


Get the answers to your questions here.

Q1 : Is Python good for machine learning?

Python is a very good programming language for machine learning. Machine learning is a very advanced and complex field of Artificial Intelligence. So the code written for machine learning algorithms, tools, and techniques can become extremely verbose, intricate, and obscure. Machine learning applications’ code usually tends to be tens of thousands of lines long. So it is very important that the programming language used to write the machine learning code is full of powerful and clever features which will make it possible to accomplish more with lesser code. Also, the machine learning code should be readable and easy to understand because it is already very complex and involved, filled with mathematical formulae and computer science algorithms. So a programming language which is not verbose and has a sparse syntax is required. The Python programming language fulfills all the above requirements.

Q2 : What is the best Python version for machine learning?

The latest Python version, Python 3.9. 0 is the newest major release of the Python programming language, and it contains many new features and optimizations. It is the best Python version for machine learning.

Q3 : How long does it take to learn Python for machine learning?

Learning Python itself is not a very difficult task. If the beginner is dedicated and refers to the official documentation and other high-quality web resources, he can learn Python in 2-4 months. But learning machine learning will take at least 1 year because it is a very advanced and complex subset of the field of artificial intelligence.

Q4 : Is Python fast enough for machine learning?

One of the drawbacks of Python is that it is very slow very algorithms which perform a lot of big calculations and other straightforward tasks related to numbers. Another is that it is not able to handle large data sets efficiently or at a fast speed. So one may naturally be lead to question that why is Python the most popular programming language for Machine learning. The answer is that in Python it is very easy to write bindings or extensions for other languages. So what machine learning programmers typically do is that they offload the number crunching and data processing tasks to other faster languages such as C or Fortran. This lets them take the advantage of the speed and processing power of the faster languages while at the same time retaining the simplicity, convenience, syntax, modules, libraries, and interoperability of Python, which is a high-level language.

Q5 : Is Python machine learning hard?

Again, as has been mentioned before, Python itself is a fairly easy language to learn compared to many other high-level languages. It has many beginner-friendly features. The syntax is very simple, sparse, and minimal. Python uses whitespace for formatting and indentation. It has many high-level features such as functions, built-in data structures, lists, tuples, if-else condition blocks, a well-designed package and module system, dynamic typing, dynamic binding etc. So it is not a very difficult task to learn the Python programming language. Machine learning on the other hand, is a very advanced and complex subset of artificial intelligence. It involves many different fields of knowledge such as computer science, statistics, mathematics, data science, and domain specific knowledge. So it will take at least 1 year to learn Machine learning and it is a fairly difficult subject matter to study.

Q6 : Can you do Machine Learning with Python?

Yes. The Python community has created several libraries specifically for the field of machine learning. Some of them are -

  • Scikit-learn for handling basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.
  • Pandas for high-level data structures and analysis. It allows merging and filtering of data, as well as gathering it from other external sources like Excel, for instance.
  • Keras for deep learning. It allows fast calculations and prototyping, as it uses the GPU in addition to the CPU of the computer. 
  • TensorFlow for working with deep learning by setting up, training, and utilizing artificial neural networks with massive datasets. 
  • Matplotlib for creating 2D plots, histograms, charts, and other forms of visualization. 
  • NLTK for working with computational linguistics, natural language recognition, and processing. 
  • Scikit-image for image processing.
  • PyBrain for neural networks, unsupervised and reinforcement learning. 
  • Caffe for deep learning that allows switching between the CPU and the GPU and processing 60+ mln images a day using a single NVIDIA K40 GPU. 
  • StatsModels for statistical algorithms and data exploration.



John Carter


The Training Course Of The Machine Learning With Python with
all the Mandatory Course Requirements with Distinction.

Certificate ID:32480XXX

Certificate URL :