PG Certificate in Data Science, Machine Learning & Generative AI – Batch 10

• Data Structures, Loops, and Control Structures
• Functional Programming in python, creating UDF’s
• Linear Algebra with NumPy/SciPy
• Data Pre-Processing using Pandas
• Generating plots with matplotlib

Learning Outcomes

• Develop proficiency in Python for data analysis.
• Perform data pre-processing using Python libraries.
• Use Python to generate visual insights from data.

• Data Wrangling
• Cleaning date and text columns
• Exploratory Data Analysis (EDA)
• Regular Expressions
• RDBMS and SQL
• Data Visualization and the Art of storytelling with data using Tableau/PowerBI

Learning Outcomes

• Apply techniques for cleaning and transforming data.
• Conduct Exploratory Data Analysis (EDA) to uncover insights.
• Create data visualisations that communicate findings effectively.

• Python ML Library - Scikit Learn
• Introduction to ML- Types of Learning
• Linear Regression
• Logistic Regression
• k Nearest Neighbors
• Naïve Bayes
• Decision Trees
• Support Vector Machines
• Unsupervised Learning: Clustering & Dimensionality Reduction
• Hands-on Case Studies for ML

Learning Outcomes

• Understand various machine learning algorithms and their use cases.
• Implement supervised and unsupervised learning models.
• Evaluate model performance using hands-on case studies.

• Hyper parameter Tuning
• Overfitting and Regularisation
• Ensemble Models
• Gradient Boosting Machines
• Feature Engineering & Feature Selection Techniques
• Time Series Forecasting

Learning Outcomes

• Optimise machine learning models using hyperparameter tuning.
• Build robust models through regularisation and ensemble techniques.
• Apply time series forecasting to predict trends in data.

• Text Analytics Overview
• Sentiment Analysis on Text Data
• Naïve-Bayes Model for Sentiment Classification
• Document Summarisation
• Topic Modelling
• Hands-on Practice

Learning Outcomes

• Gain insights into text data using sentiment analysis.
• Classify text using Naive Bayes models.
• Apply topic modeling for document clustering.

• Fundamentals of TensorFlow
• TensorFlow Programming Model
• Performing basic operations using TensorFlow
• Introduction to Perceptron
• Perceptron Training
• Deep Neural Networks
• Keras API

Learning Outcomes

• Implement deep learning models using TensorFlow and Keras.
• Understand the functioning of perceptrons and multi-layer neural networks.
• Perform basic operations using TensorFlow.

• Version Control Systems
• Containerization (Docker) Orchestration tools (Kubernetes),
• CI/CD Pipelines (Jenkins, GitHub Action),
• Integration of machine learning models into production environments, including model deployment, monitoring, and lifecycle management.

Learning Outcomes

• Use version control systems and CI/CD pipelines in machine learning workflows.
• Deploy machine learning models in production environments.
• Manage the lifecycle of machine learning models post-deployment.

• Computer Vision with Open CV
• Convolutional Neural Networks (CNN)
• Pretrained CNN Models
• Image Classification with KERAS
• Object Detection and Face recognition
• Hands-on Practice
• Overview of Speech Recognition and Basic APIs
• Advanced NLP - using Word Embeddings
• Word2Vec, GLOVE
• Working with sequence data - RNN and LSTM
• Transformers Architecture, Generative AI, ChatGPT & Prompt Engineering
• BERT, GPT-3, and applications in text analytics, sentiment analysis, and language translation.

Learning Outcomes

• Develop image classification models using CNNs and pre-trained networks.
• Work with RNNs and LSTMs for sequence data analysis.
• Explore the applications of GPT-3 in generative AI and text analytics.

• Introduction to Data Engineering & Big Data
• Introduction to Hadoop, HDFS and map reduce
• Data Analytics using Apache hive
• Working with Cloud (Microsoft Azure)
• Working with Apache Spark and Spark through databricks
• Data /ingestion tools – Sqoop, Flume and Kafka
• Working with NoSQL databases – Cassandra, Hbase and MongoDB
• Introduction to Big data analytics with Spark ML
• Implementing ML algorithms through spark ML

Learning Outcomes

• Perform large-scale data analytics using Apache Spark and Hadoop.
• Ingest data efficiently using Kafka and Sqoop.
• Implement data engineering solutions on cloud platforms like Microsoft Azure.

• Apply all learned concepts in a comprehensive real-world project.
• Showcase the ability to solve industry-relevant problems.
• Gain practical experience in managing and deploying a complete data science solution.