Machine Learning & Artificial Intelligence

Enrolled (400+)

The Intermediate Machine Learning and Artificial Intelligence course is designed for technical graduates with a Bachelor of Engineering or Bachelor of Technology degree who are eager to deepen their knowledge and practical skills in the dynamic field of artificial intelligence (AI) and machine learning (ML).

Course Eligibility: – BE/B.Tech/BCA/MCA

Course Overview

This comprehensive 6-month program provides a structured learning path, offering a deep dive into the core concepts and advanced applications of AI and ML.

Course Benefits

By the end of this course, participants will be equipped with the skills and expertise required to tackle complex AI and ML challenges, making them valuable assets in the ever-evolving field of artificial intelligence. Whether you are aspiring to work in industry, research, or entrepreneurship, this course will provide you with the necessary tools and knowledge to succeed in your AI and ML endeavors.

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Course Details

Course overview and objectives
Importance of ML and AI in the industry
Understanding the learning path and outcomes
Assignment: Self-introduction and course expectations

Introduction to Python
Variables, data types, and operators
Control structures
Functions and modules
Exception handling & File handling
Assignment: Python coding exercises

Introduction to Linux OS
Linux commands and file system
File manipulation and permissions
Shell scripting basics
Assignment: Linux command-line exercises

Statistical operations using Numpy
Data analysis using Pandas
Visualization using Matplotlib and Seaborn
Assignment: Python coding exercises

Linear algebra essentials for ML
Probability and distributions
Descriptive and inferential statistics
Hypothesis testing
Assignment: Statistical analysis on a dataset

Introduction to ML and its types
Supervised learning: Regression & Classification
Unsupervised learning: Clustering and dimensionality reduction
Model evaluation and hyperparameter tuning
Assignment: Building and evaluating ML models

Introduction to databases and SQL
Basic SQL queries and data manipulation
Advanced querying and optimization techniques
Database design and normalization
Assignment: Database querying exercises

Introduction to PowerBI and data visualization
Data preparation and transformation
Creating interactive reports and dashboards
DAX (Data Analysis Expressions)
Sharing and publishing Power BI reports
Assignment: Creating a Power BI dashboard

Understanding big data and challenges
Introduction to Apache Spark and its ecosystem
Spark RDDs and transformations
Spark SQL and dataframes
Working with big datasets in Spark
Assignment: Spark data processing tasks

Introduction to time series data
Time series preprocessing and feature engineering
Time series forecasting methods
ARIMA and Seasonal decomposition
Time series analysis using Python libraries
Assignment: Time series forecasting project

Introduction to neural networks and deep learning
Building neural networks with TensorFlow
Building neural networks with PyTorch
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Assignment: Deep learning model implementation

Introduction to NLP and its applications
Text preprocessing and tokenization
Sentiment analysis and text classification
Named Entity Recognition (NER) and Part-ofSpeech tagging
Word embeddings and language models
Assignment: NLP project using Python

Introduction to Computer Vision
Image preprocessing and augmentation
Object detection and image segmentation
Image classification using CNNs
Transfer learning for image analysis
Assignment: Computer vision project

Introduction to reinforcement learning
Markov Decision Processes (MDPs)
Q-learning and policy gradients
Deep Reinforcement Learning
Applications of reinforcement learning
Assignment: RL algorithm implementation

Introduction to model deployment
Model serialization and deployment considerations
Deploying models with Flask
Containerization using Docker
Model monitoring and maintenance
Assignment: Deploying a machine learning model

Importance of data structures and algorithms
Arrays, linked lists, stacks, and queues
Trees, graphs, and hash tables
Sorting and searching algorithms
Algorithm complexity analysis
Assignment: Algorithm implementation tasks

Introduction to project management in tech
Version control using Git
Issue tracking and project boards
Collaborative coding with GitHub
Using Jira for project management
Agile methodologies and project planning
Assignment: Setting up a project management workflow

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