Course Description

This comprehensive guide outlines our Data Science course, designed to equip learners with the essential skills needed to excel in the field. Whether you're a beginner or an experienced professional, our course offers a robust curriculum that spans foundational concepts to advanced techniques in machine learning, deep learning, and beyond. This article will walk you through each module of the course, highlighting key topics, learning outcomes, and practical applications.

Module 1 - Foundations of Machine Learning & Deep Learning Advanced Python & Python Libraries

• Python Libraries: A refresher on Python and its essential libraries, including NumPy, Pandas, Matplotlib, and Seaborn.
• Data Acquisition: Learn to gather data using Web APIs and web scraping techniques with BeautifulSoup and Tweepy.
• Advanced Python: Dive into advanced concepts like time and space complexity, OOP, functional programming, and exception handling.

Maths for Machine Learning

• Probability & Applied Statistics: Explore the fundamentals of probability, Bayes Theorem, distributions, and hypothesis testing.
• Calculus, Optimization & Linear Algebra: Understand the mathematical underpinnings of machine learning models, including hyperplanes, gradients, and principal component analysis.

Introduction to Neural Networks & Machine Learning

• Fundamentals of ML: Start with classical machine learning techniques such as linear regression, logistic regression, and clustering methods like K-Means.
• Neural Networks: Get an introduction to perceptrons, softmax classification, and the basics of neural networks.

Module 2 - Specialization in Machine Learning OR Deep Learning Specialization 1: Machine Learning (8 Weeks)

• Supervised Learning: Learn key techniques like Maximum Likelihood Estimation (MLE), classification metrics, and handling imbalanced data.
• Unsupervised Learning & Recommender Systems: Delve into clustering methods, anomaly detection, and building recommendation engines.
• Time Series Analysis: Gain expertise in analyzing and predicting time-dependent data.

Specialization 2: Deep Learning (10 Weeks)

• Neural Networks: Advance your understanding of multilayer perceptrons, TensorFlow, and Keras.
• Computer Vision: Explore CNNs, data augmentation, and popular architectures like ResNet and MobileNet.
• Natural Language Processing (NLP): Learn text processing, tokenization, LSTM, and named entity recognition.

Module 3 - ML Pipeline Development & Deployment + DSA (Optional) Machine Learning Ops

• End-to-End ML Pipelines: Develop and deploy ML models using tools like Streamlit, Flask, Docker, and MLFlow.
• Cloud Platforms: Work with AWS services such as SageMaker, Data Wrangler, and Pipelines to deploy models in a cloud environment.
• Data Structures & Algorithms (DSA): Prepare for technical interviews by mastering data structures like arrays, linked lists, trees, and algorithms like sorting, searching, and recursion.

Module 4 - Get Placed as a Data Scientist at Top Companies Building a Strong Profile

• Resume Creation: Learn to craft a standout resume and optimize your LinkedIn profile.
• Applying the Right Way: Leverage our collaboration with 250+ tech companies for job referrals and application support.
• Acing the Interview: Prepare with mock interviews, online interview guidelines, and salary negotiation tips.

Expert Insights & Case Studies

Throughout the course, you'll benefit from expert insights and real-world case studies. These practical examples will help solidify your understanding and demonstrate how the concepts you learn are applied in industry.