About course

Machine learning, a facet of artificial intelligence, employs algorithms allowing systems to learn and make predictions without explicit programming. Widely applied in areas such as image recognition, it streamlines tasks and bolsters decision-making. Its adaptability and self-improvement mark it as a pivotal technology driving innovation. As it continues to evolve, machine learning plays a central role in automating processes and extracting valuable insights from vast datasets, contributing significantly to the rapidly advancing landscape of modern computing.

About Course Objectives

Pre-Requisites

The course can learn by any IT professional having basic knowledge of:

  • Basic Computer Skills
  • Understanding of Programming Concepts

you will be expertise and eligible for:

  • Advanced knowledge of machine learning algorithms, statistical modeling, and deep learning architectures.
  • Proficiency in implementing and deploying machine learning models, programming languages (e.g., Python, TensorFlow, PyTorch), and software engineering principles.
  • Ability to understand business problems, recommend machine learning solutions, and communicate effectively with non-technical stakeholders.
  • Proficiency in computer vision algorithms, image processing, and deep learning for visual recognition tasks.
  • Ability to teach machine learning concepts, algorithms, and applications effectively.

Who should go for this course

  • Any IT experienced Professional who are interested to build their career in Machine Learning
  • Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc/ M.Tech/ BCA/ BCom College Students in any stream.
  • Fresh Graduates.

Duration of the Course

  • Duration of 6 Months
  • 90 Minutes per day
  • Provides class recording sessions.

Training Curriculum

  • Overview of machine learning concepts and applications.
  • Types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
  • Basic terminology: features, labels, algorithms.
  • Linear algebra, calculus, and probability basics.
  • Statistical concepts: mean, variance, probability distributions.
  • Optimization techniques in machine learning.
  • Introduction to Python for data manipulation and analysis.
  • Libraries: NumPy, Pandas, Matplotlib.
  • Building a foundation for machine learning in Python.
  • Linear regression and logistic regression.
  • Decision trees and ensemble methods (Random Forest, Gradient Boosting).
  • Support Vector Machines (SVM).
  • K-Means clustering.
  • Hierarchical clustering.
  • Principal Component Analysis (PCA) for dimensionality reduction.
  • Introduction to neural networks.
  • Building and training deep learning models with TensorFlow or PyTorch.
  • Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
  • Tokenization, stemming, and lemmatization.
  • Building text classification models.
  • Introduction to language models like BERT.
  • Basics of reinforcement learning.
  • Markov Decision Processes (MDP) and Q-learning.
  • Implementing reinforcement learning algorithms.
  • Metrics for classification and regression models.
  • Cross-validation techniques.
  • Hyperparameter tuning.
  • Strategies for handling missing data.
  • Feature scaling and normalization.
  • Feature selection methods.
  • Creating end-to-end machine learning pipelines.
  • Deployment considerations.
  • Understanding ethical considerations in machine learning.
  • Strategies to identify and mitigate biases.
  • Hands-on project applying machine learning concepts.
  • Presentation and peer review.
  • Real-world applications of machine learning in various industries.
  • Case studies showcasing successful implementations.
  • Staying updated with the latest trends in machine learning.
  • Engaging with research papers and community forums.