AI and Machine Learning

3 Months (Weekends Only)

This intensive course offers a deep dive into the world of Artificial Intelligence (AI) and Machine Learning (ML). Delivered by senior professional software engineers, participants will gain hands-on experience, theoretical knowledge, and practical skills required to excel in the field. The course includes a Live Project in the final three weeks, allowing students to implement AI and ML techniques in real-world scenarios.

Module 1: Introduction to AI and ML

Understanding the foundations of AI and ML
Historical development and current applications
Role of data in AI and ML
Setting up the development environment

Module 2: Data Collection and Preprocessing

Data acquisition strategies
Data cleaning and transformation
Exploratory Data Analysis (EDA)
Feature engineering and selection

Module 3: Supervised Learning

Linear regression and logistic regression
Decision trees and random forests
Support Vector Machines (SVM)
Model evaluation and selection

Module 4: Unsupervised Learning

Clustering techniques: K-Means, Hierarchical clustering
Dimensionality reduction: PCA, LDA
Association rule mining
Anomaly detection

Module 5: Neural Networks and Deep Learning

Introduction to neural networks
Building and training neural networks with TensorFlow/Keras
Convolutional Neural Networks (CNN) for image analysis
Recurrent Neural Networks (RNN) for sequence data

Module 6: Natural Language Processing

Introduction to NLP and its applications
Text preprocessing and tokenization
Building NLP models with libraries like NLTK and spaCy
Sentiment analysis and text classification

Module 7: Reinforcement Learning

Introduction to reinforcement learning
Markov Decision Processes (MDP)
Q-learning and policy gradient methods
Building RL agents for simple tasks

Module 8: Time Series Analysis

Introduction to time series data
Time series decomposition
Forecasting techniques
Applications in finance and forecasting

Module 9: Advanced Topics in AI and ML

Transfer learning
Generative Adversarial Networks (GANs)
Autoencoders
Hyperparameter tuning and optimization

Module 10: Ethics and Bias in AI

Understanding ethical considerations in AI
Recognizing bias in AI models
Mitigating bias in AI applications
Responsible AI development

Live Project - Part 1

Project kick-off and problem definition
Data collection and preparation
Model selection and initial training

Live Project - Part 2

Continued model training and refinement
Evaluation of project outcomes
Final project presentation and demonstration

Live Project Details:

The Live Project spans the last six weeks of the course, allowing students to work on a real-world problem related to AI and Machine Learning. They will apply their acquired knowledge to address the problem, from data collection and preprocessing to model building and evaluation. The project concludes with a comprehensive presentation and demonstration of their solutions.

Assessment:

Weekly quizzes and assignments
Final project evaluation and presentation

Certification:

Upon successful completion of the course and the Live Project, participants will receive a certificate in “AI and Machine Learning” from “Industry Professionals.”

This course outline offers a comprehensive and structured approach to AI and Machine Learning, guided by experienced industry professionals, and emphasizes hands-on learning through the Live Project. Students will gain both theoretical knowledge and practical skills to excel in the field of AI and ML.

Fee: 39,999

(can be paid in three equal installments plan)

3 Months (Weekends Only)