Machine Learning

"Unlock the Potential of Machine Learning with Python - Empower Your Skills Today"

Best Seller Expert 30 Participants
Mentors Adarsha Shivananda

What you will learn/The learning objectives of machine learning course:

  • Understanding the fundamental concepts and theories of machine learning
  • Acquiring knowledge of various machine learning algorithms such as linear regression, decision trees, k-nearest neighbors, and others.
  • Learning how to implement machine learning algorithms using Python libraries such as scikit-learn, pandas, and TensorFlow.
  • Understanding the steps involved in the machine learning process, including data preparation, model training, and evaluation.
  • Gaining hands-on experience in building and deploying machine learning models in real-world scenarios.
  • Developing the ability to interpret the results of machine learning models and making informed decisions based on the results.
  • Gaining an understanding of the ethical and societal implications of machine learning.

Course Content

In this course, you will learn about the fundamentals of Supervised Learning, a type of machine learning where the algorithm learns from labeled training data to make predictions. You will understand how the algorithm maps input variables to a target variable, and how it can be trained and tested on separate datasets. This course will cover applications of Supervised Learning, including classification, regression, and anomaly detection.

supervised learning. The course covers the fundamental concepts of classification, including binary classification and multi-class classification. You will learn about different algorithms for classification such as k-Nearest Neighbors, Decision Trees, Random Forest, Support Vector Machines, and Neural Networks. The course will also cover evaluation metrics used to assess the performance of a classifier such as accuracy, precision, recall, and F1-score.

Discover Regression Techniques in Machine Learning with this hands-on course. Learn about algorithms such as Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and more using Python. Develop practical skills in evaluating performance, avoiding overfitting/underfitting, and feature selection. Apply your knowledge to real-world case studies and projects.

data to find patterns and relationships. You will learn popular unsupervised techniques such as clustering, dimensionality reduction, and anomaly detection using Python libraries like scikit-learn and TensorFlow. With hands-on exercises, case studies, and projects, you'll leave with a practical understanding of unsupervised learning and how to apply it in real- world scenarios

Build Personalized Recommendations with Recommendation Engines. Learn to implement algorithms using Python and popular libraries. Gain practical skills in evaluating performance and overcoming real-world challenges.

Discover the Power of Time Series Analysis . This course provides a comprehensive introduction to Time Series, a set of data points collected at regular intervals over time. You will learn about the various components of Time Series data such as trend, seasonality, and residuals. You will also learn how to decompose Time Series data and build models using techniques such as ARIMA and SARIMA.

About this Course

This course is designed for individuals who are interested in learning about Machine Learning and its implementation using Python. The course covers various fundamental concepts of machine learning such as supervised learning, unsupervised learning, and reinforcement learning. Participants will also learn how to apply these concepts using popular Python libraries such as scikit-learn, pandas, and TensorFlow. The course is hands- on and includes coding exercises, case studies, and projects to help participants develop a practical understanding of machine learning with Python. By the end of the course, participants will have a solid understanding of the key concepts and be able to build and deploy machine learning models in real-world scenarios.

Why Machine learning?

Data Scientists are still in high demand. Companies recognize data scientists as an essential skill that provides valuable insights. The increase in data availability leads to a growing skill gap in data analytics, providing more job opportunities and career growth.

There are several factors that motivate individuals to pursue a career as a Data Analyst, including:

  • Rising demand: The field of data analytics is rapidly expanding, and organizations are seeking professionals to make sense of the growing data.
  • Diverse career options: Data Analysts can work in a range of industries, such as finance, healthcare, marketing, and others. They can also progress into related roles, like Data Scientist, Business Analyst, or management positions.
  • High salary prospects: Data Analysts generally have high earning potential, and this trend isexpected to continue as the demand for data analytics professionals rises.
  • Dynamic and ever-changing field: Data analysis is a constantly evolving field with new tools and methods emerging regularly, making it an exciting and dynamic career path.

Common job titles:

ML Engineer, Data Analyst, Senior Data Analyst, Associate Data Analyst, Business Analyst, Junior Data Scientist, Finance Analyst, Operations Analyst, Marketing Analyst, Healthcare Analyst

Mentor

Swayam Mittal
Machine Learning | Simulation
30 Participants

Swayam Mittal is an experienced Machine Learning and Simulation expert with a passion for data-driven decision making. He has a vast experience in both industry and academia, having worked with companies like Hitachi, UpGrad, and MUST Research Academy. He earned degrees in Electronics and Communication and Data Science of IOT from Oxford University after graduating from Sathyabama University.

A curious mind searching for ways to bridge the gap between business and science, Swayam has strong technical skills in different fields of Artificial Intelligence including Robotics and IOT, and loves solving challenging and interesting problems.

He is well-versed in a wide range of Machine Learning algorithms and techniques, and is an expert in developing simulations and data visualizations. He is an enthusiastic and engaging trainer and mentor, and is committed to helping others unlock the potential of Machine Learning and Simulation.

Believes in working for the welfare of the community motivated by a noble cause. JGD

Research Area - NLP deep learning, transformer, semantic search, context learning, speech processing, embedded Intelligence

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