Machine Learning with Python Training in Nepal

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Machine Learning training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. You will gain in-depth knowledge on all the concepts of machine learning including supervised and unsupervised learning, algorithms. Also, this algorithm will be support vector machines, etc., through real-time industry use cases.

Machine Learning Algorithm is composed of Supervised and Unsupervised. Supervised Algorithm requires a Data Scientist (information researcher) or Data Analyst with AI abilities to give both input and desired output, in addition to feedback about the precision of predictions during the algorithm training. Unsupervised Algorithm (Also called Neural Network) need not be prepared with the desired information. Rather, Unsupervised algorithm uses an iterative method called deep learning to review data.

You can have a great career at AI with Machine Learning with Python Training in IIT Nepal, Kathmandu.


   80 hrs - 2hrs/day [ 4 days a week]

Machine Learning Training in Kathmandu Syllabus

  1.  Introduction to python
    • Installation of Python framework and packages: Anaconda & pip
    • Working with Jupyter notebooks
    • Creating Python variables
    • Numeric, string and logical operations
    • Data containers: Lists, Dictionaries, Tuples & sets
    • write for loop in python
    • while loop and conditional block in python
    • List / Dictionary comprehension in python
    • write your own function in python
    • write your own class and function in python
  2. Introduction to Statistics
    • Statistical Inference
    • Type of Variables
    • Probability Distribution
    • Normality
    • Measures of Central Tendencies
    • Normal Distribution
  3. Machine Learning Application & LandScape
    • Introduction to Machine Learning
    • Machine Learning Application
    • Introduction to AI
    • Different type of Machine Learning – Supervised and Unsupervised
    • Reinforcement
  4. Building end to end ML Project
    • Frame the problem
    • Get the data
    • Explore the data to gain insights
    • Prepare the data for Machine Learning
    • Fine tune model
  5. Classfification
    • Train a Binary classification
    • Performance measure
    • Confusion Matrix
    • Precision / Recall Trade off
    • The ROC curve
    • Multiclass classfication
    • Multilabel classification
    • Multi-output classification
  6. Training Models
    • Linear Regression
    • Gradient Descent
    • Polynomial Regression
    • Learning Curves
    • Regularized Linear Model
    • Logistic Regression
  7. Support Vector Machines (SVM)
    • Linear SVM Classfication
    • Normalized SVM classfication
    • SVM Regression
  8. Decision Trees
    • Training and Visualization a Decision Tree
    • Making Predictions
    • Estimating Class Probabilities
    • The CART training Algorithm
    • Gini Impurity or Entropy
    • Regularization Hyperparameter
    • Regression
    • Instability
  9. Ensemble Learning and Random Forest
    • Voting classifiers
    • Bagging and Pasting
    • Random Patches and Random Subspaces
    • Random Forrest
    • Boosting
    • Stacking
  10. Dimensionality Reduction
    • The Curse of Dimensionality
    • Main Approaches for Dimensionality Reduction
    • PCA
    • Kernel PCA
  11. Projects
    1. Predict the Median housing price in California form census data of 1960
    2. classify handwritten digits in MINIST dataset.
    3. Noise removal from the images
    4. Predict the class of flower in IRIS dataset
    5. Predict the which passengers survived in the TITANIC shipwreck
    6. Predict the bikes rental demand
    7. Build the spam classifier.


 Version Control

  • Need and Importance of Version Control
  • Setting up git and github accounts on local machine
  • Creating and uploading GitHub Repos
  • Push and pull requests with GitHub App
  • Merging and forking projects

Why Machine Learning with Python Training in Nepal

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

Opportunities after Training Machine Learning

In the research areas, Machine Learning  python is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. In What Is the Future of Machine Learning, Forbes predicts the theoretical research in ML will gradually pave the way for business problem solving. With Big Data making its way back to mainstream business activities, now smart (ML) algorithms can simply use massive loads of both static and dynamic data to continuously learn and improve for enhanced performance.

Why IIT Nepal?

IIT Nepal is a team of experienced Python Developers who have been working in the fields of Python for more than 8 years. IIT Nepal is the best IT training center in Nepal. Being taught at IIT Nepal provides you a better exposure. Training Machine Learning with Python in Nepal from the best institute gives you following benefits.

  1. Experienced Mentors and Trainers
  2. Interns for capable candidates
  3. Real Time Experience
  4. Better Price
  5. Quality Education
  6. International Level Certification