Machine learning Program

A comprehensive machine learning training course covers a wide range of topics related to machine learning, which is a subfield of artificial intelligence. The Machine learning, covering the fundamental algorithms and techniques used in various machine learning applications. The depth and breadth of coverage may vary depending on the level and focus of the course.

  • Training from Industry Level Project Coders
  • Accredited Training Certification
  • Average Starting Package: 7-8 LPA
  • Top Hiring Partners

Batch Schedule for Machine learning Program

Azsm Enterprizes provides flexible timings to all our students. Here are the Machine learning Program Shedule for our branch. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

Mon (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fee
Thu (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fee
Sat (Sat - Sun)Weekend Batch 11:00 AM (IST) (Class 3Hrs) / Per Session Get Fee

Why I Should Enroll for Machine learning Program?

Machine learning Program has been curated after thorough lookup and pointers from industry experts. It will help you differentiate yourself with multi-platform fluency, and have real-world experience with the most necessary equipment and platforms. Azsm will be via your side for the duration of the studying ride – We’re Ridiculously Committed.

  • Higlight Topics Linear RA, Logistic RA, KNN, PCA & SVM
  • Programming Python, Java, R Programming, C++, JavaScript
  • Tools MS Azure, OpenNN, PyTorch & BigML
  • Offered Fee Rs. 35,000/-
  • Discount Offered Fee Rs. 30,000/-
  • Amount To Pay (+GST) Rs. 30,000/-

Machine learning Course Curriculum

Curriculum Designed by Experts

  • This software follows a set structure with 6 core courses and four electives spread throughout 21 weeks. It makes you a specialist in key technologies associated with Machine learning Program. At the end of each core course, you will be working on a real-time venture to obtain palms-on expertise. By the quiet of the program, you will be ready for seasoned Machine learning Program job roles.


    • Linear Regression Algorithm

    • Logistic Regression Algorithm

    • Decision Tree

    • SVM

    • Naive Bayes

    • KNN

    • K-Means Clustering

    • Random Forest

    • PCA


  • Introduction to Machine Learning:

    • What is machine learning?
    • History and evolution of machine learning.

    Types of Machine Learning:

    • Supervised learning.
    • Unsupervised learning. Semi-supervised learning. Reinforcement learning.

    Data Preprocessing:

    • Data cleaning and feature engineering.
    • Handling missing data. Data normalization and scaling.

    Exploratory Data Analysis (EDA):

    • Data visualization.
    • Descriptive statistics.
    • Identifying patterns and trends in data.

    Supervised Learning Algorithms:

    • Linear regression.
    • Logistic regression.
    • Decision trees and random forests.
    • Support vector machines.
    • k-Nearest Neighbors (k-NN).
    • Naive Bayes.

    Model Evaluation and Validation:

    • Cross-validation.
    • Evaluation metrics (accuracy, precision, recall, F1-score).
    • Overfitting and underfitting.

    Unsupervised Learning Algorithms:

    • Clustering (k-Means, Hierarchical, DBSCAN).
    • Principal Component Analysis (PCA).
    • Association rule learning (Apriori).

    Dimensionality Reduction:

    • Techniques like PCA and LDA.
    • Reducing feature space.

    Feature Selection and Engineering:

    • Selecting relevant features.
    • Creating new features.

    Ensemble Methods:

    • Bagging and boosting.
    • Random forests.
    • Gradient Boosting (XGBoost, LightGBM).

    Introduction to Deep Learning:

    • Artificial neural networks.
    • Feedforward neural networks.
    • Backpropagation.

    Convolutional Neural Networks (CNNs):

    • CNN architecture.
    • Image classification with CNNs.
    • Transfer learning with CNNs.

    Recurrent Neural Networks (RNNs):

    • RNN architecture.
    • Sequence prediction with RNNs.
    • LSTMs and GRUs.

    Natural Language Processing (NLP):

    • Text preprocessing.
    • Text classification.
    • Named Entity Recognition (NER).

    Reinforcement Learning:

    • Markov Decision Processes (MDPs).
    • Q-learning.
    • Policy gradients.

    Model Deployment:

    • Deploying models into production.
    • Model APIs and services.

    Tools and Libraries:

    • Python and libraries like scikit-learn, TensorFlow, and PyTorch.
    • Jupyter Notebooks.

    Ethical Considerations:

    • Bias and fairness in machine learning.
    • Ethical considerations in AI.

    Case Studies and Real-World Applications:

    • Examples of machine learning in healthcare, finance, autonomous vehicles, and more.

    Capstone Project:

    • Applying machine learning techniques to a real-world project.