Machine Learning Course in Gandhinagar

Full Machine Learning course in Gandhinagar with Artificial Intelligence, Data Science and Python

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Course Highlights

Gandhinagar is gradually emerging as the technology hub of Gujarat. Make the most out of it with TOPS Technologies’ machine learning course in Gandhinagar. Experience a career jump like never before as this machine learning course prepares you for a role in the data science field with deep learning. Also, you get the hands-on experience of implementing your in-class learning in the practical sessions that prepares you for roles like machine learning architects and engineers, technology and solution architects, etc.

TOPS Technologies offers a machine learning course in Gandhinagar, boasting top-class in-house faculties having extensive knowledge and experience in the field across industries and roles. It is your chance to unlock the true potential with a new learning curve by enrolling in this machine learning course.

We stand atop the rest with the experience of placing over 12,000 students to top positions in the IT industry over the years. Come alone, bring your friends and parents to TOPS Technologies at Gandhinagar to know more about the machine learning course. Also, you can register for a risk-free demo session to make the right choice.

Reach out TOPS Technologies in Gandhinagar to inquire about machine learning or drop us an email on

Personalized Machine Learning Course for Students and Working Professionals

We have an experienced team with us to tailor a perfect machine learning course for your college, school, or office. The intent is to fit in the machine learning framework that brings value to education for students and enhance employees’ knowledge.

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Course Curriculum

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  • Introduction to Python
  • Programming Style
  • Conditional Statements
  • Loops
  • Controls
  • String Manipulations
  • Accessing Strings
  • Functions and Methods
  • Collections
  • Modules
  • Exceptional Handling

  • Importing libraries and Databases
  • Categorizing Data
  • Creating a Data Preprocessing table
  • Understanding and identifying missing Data
  • Data Clustering
  • Data Classifications and its types

  • Regression
  • Classification
  • Supervised and Unsupervised Learning
  • Linear Regression
  • Data Regularization
  • Modeling of Time Series
  • Data Mining

  • Numpy
  • SciPy
  • Pandas
  • Matloplib
  • Sckit-learn
  • TensorFlow
  • Keras

  • Project Case Study
  • Ecommerce Purchase Predictions and Algorithm
  • Insurance Policy Quote Predictions and Algorithm

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Interview Questions

There are numerous ways to solve this problem. For example, you get a data set with thousands of Twitter interactions. By carefully examining the phrases used in the tweets, you will start by trying to study the connection that exists between individuals. Implementing Text Mining using Natural Language Processing techniques can resolve this problem statement. Each word in a paragraph is subdivided, and relationships between various words are discovered. In addition, NLP is vigorously used to comprehend customer feedback and conduct sentiment analysis on Facebook and Twitter. Therefore, text Mining and Natural Language Processing techniques are one way to address this issue.

The principal component analysis includes the important step of rotation (PCA.) Rotation maximises the separation inside this variance procured by the modules. This makes it simpler to interpret the components. PCA aims to select the more straightforward structure that can explain the most variance in a data set. The initial precise location of the focus is altered when rotation is performed. The components' relative positions do not change, though. Therefore, more extended elements must characterise the variance if the parts are not rotated.

A data set known as the "Training Set" is used in various information and machine learning applications to identify potentially predictive relationships. An example is provided to the learner in the training set. However, the learner's hypotheses are also tested for accuracy using the "Test set." It is the set of examples withheld from the student. As a result, the training set and test set are different.

On top of features and parameters, Machine Learning models are constructed in the real world. These characteristics may be numerous and multidimensional. Therefore, it can be challenging to visualise features that are occasionally irrelevant. In this situation, principal variables are used in conjunction with dimensionality reduction to reduce the number of unnecessary and redundant features. These primary variables, a subset of the parent variables, preserve the characteristics of the parent parameters.

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