Data Science and Artificial Intelligence (AI) Training with Internship Program

Module 1: Introduction to Python Programming

Python Installation and Set up the Working Environment
Fundamentals of Python
List and Boolean variables
Sets, Dictionaries & Tuples
Functional, Positional and Keyword Arguments
Functions and Modules

Module 2: Working with Python

OOPS concepts
List Comprehension
Dictionary comprehension
String Formatting
File Handling
Exception Handling
Data Structures
Generators and Iterators
Regular Expression

Module 3: Working with Python Libraries

Numpy
Pandas
Seaborn
Matplotlib
Sklearn
Data Acquisition (Import & Export)
Indexing
Selection and Filtering Sorting & Summarizing
Combining and Merging Data Frames
Removing Duplicates
Discretization and Binning

Module 4: Essential Statistics

Data and Data Types
Measures of Central Tendency in Data
Measures of Dispersion
Understanding Skewness in Data
Data Distribution
Sampling and Sampling Distributions
Hypothesis Testing - Large and Small Samples
Analysis of Variance and Covariance

Module 5: Data Modelling and Feature Analysis

Exploratory Data Analysis
Feature Engineering
Dimensionality Reduction
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Information Gain

Module 6: Machine Learning

Classification
Logistic Regression
Decision Tree
Random Forest
Neural Networks
SVM
KNN
Naive Bayes
Regression
Linear Regression
Random Forest Regressor
Clustering
K-Mean clustering
Hierarchical Clustering
Association Rule Mining
Semi-Supervised Learning
Ensemble Learning

Module 7: Data Visualization and Model Validation

Data Visualization & Reporting
Machine Learning Model testing, validation and performance Evaluation
Confusion Matrix
Precision
Recall
F-Measure
Accuracy
AUC
MSE
Performance Visualization

Module 8: Introduction to Deep Learning

Introduction to Artificial Intelligence and Neural Networks
History and Evolution of Deep Learning
Deep Learning Frameworks and Tools
Setting up the Development Environment

Module 9: Fundamentals of Neural Networks

Basics of Neural Networks
Activation Functions and Loss Functions
Feed Forward/Backpropagation Algorithm
Optimization Techniques (Gradient Descent, Adam, RMSProp)

Module 10: Convolutional Neural Networks (CNN)

Introduction to CNN
Convolutional Layers, Pooling Layers, and Padding
Object Detection and Image Classification using CNN
Transfer Learning and Fine-tuning CNN Models

Module 11: Recurrent Neural Networks (RNN)

Introduction to RNN
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
Language Modeling and Text Generation using RNN
Sequence-to-Sequence Models and Machine Translation

Module 12: Generative Adversarial Networks (GAN)

Introduction to GANs
Generator and Discriminator Networks
Training GANs and Mode Collapse
Applications of GANs (Image Generation, Style Transfer)

Module 13: Deep Reinforcement Learning

Introduction to Reinforcement Learning
Markov Decision Processes (MDP) and Q-Learning
Deep Q-Network (DQN) and Policy Gradient Methods
Applications of Deep Reinforcement Learning (Game Playing, Robotics)

Module 14: Topics in Deep Learning

Autoencoders and Variational Autoencoders
Attention Mechanisms in Deep Learning
Transformer Models for Natural Language Processing
Deep Learning for Time Series Analysis and Forecasting

Module 15: Model Optimization and Deployment

Model Regularization and Dropout
Hyperparameter Tuning and Cross-Validation
Model Evaluation Metrics and Techniques
Model Deployment and Serving (TensorFlow Serving, Flask API)

Module 16: Introduction to Natural Language Processing

Introduction to NLP and its Applications
Basics of Text Processing and Linguistics
Language Models and Corpora
NLP Tools and Libraries (NLTK, SpaCy)
Text Preprocessing and Tokenization
Text Representation and Feature Extraction
Text Cleaning and Normalization
Tokenization and Lemmatization
Stop Word Removal and Stemming
Part-of-Speech Tagging

Module 17: Sentiment Analysis and Opinion Mining

Sentiment Analysis Techniques
Lexicon-based Approaches
Machine Learning Models for Sentiment Analysis
Aspect-based Sentiment Analysis
Named Entity Recognition and Entity Extraction
Introduction to Named Entity Recognition (NER)

Module 18: Text Classification and Topic Modeling

Supervised Text Classification
Introduction to Topic Modeling (LDA)
Latent Semantic Analysis and Latent Dirichlet Allocation
Sequence-to-Sequence Models and Machine Translation

Module 19: Latest Models in Deep Learning 1

Multimodal Deep Learning
Federated Deep Learning
Self-Supervised Learning
Zero Shot and Few Shot Learning

Module 20: Latest Models in Deep Learning 2

Computer Vision Tasks
Pretrained Image Models
Real Time Object Detection
Image to Text
Text to Image

Who Should Attend?

Students Looking for a Professional Career in DS and AI: B. E / B.Tech / M.E / M.Tech / M.Sc / Masters with any discipline

Aspiring Data Scientists: Individuals looking to start a career in data science and gain the necessary skills to analyze data and extract valuable insights.

Professionals Seeking Career Transition: Professionals from diverse backgrounds who want to transition into the field of data science and enhance their career prospects.

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Data Science and Artificial Intelligence (AI) Training with Internship Program

Best Data Science and Artificial Intelligence (AI) Course in Chennai

Data Science and Artificial Intelligence (AI) Course Syllabus

Module 1: Introduction to Python Programming

Module 2: Working with Python

Module 3: Working with Python Libraries

Module 4: Essential Statistics

Module 5: Data Modelling and Feature Analysis

Module 6: Machine Learning

Module 7: Data Visualization and Model Validation

Module 8: Introduction to Deep Learning

Module 9: Fundamentals of Neural Networks

Module 10: Convolutional Neural Networks (CNN)

Module 11: Recurrent Neural Networks (RNN)

Module 12: Generative Adversarial Networks (GAN)

Module 13: Deep Reinforcement Learning

Module 14: Topics in Deep Learning

Module 15: Model Optimization and Deployment

Module 16: Introduction to Natural Language Processing

Module 17: Sentiment Analysis and Opinion Mining

Module 18: Text Classification and Topic Modeling

Module 19: Latest Models in Deep Learning1

Module 20: Latest Models in Deep Learning2

Module 1: Introduction to Python Programming

Module 2: Working with Python

Module 3: Working with Python Libraries

Module 4: Essential Statistics

Module 5: Data Modelling and Feature Analysis

Module 6: Machine Learning

Module 7: Data Visualization and Model Validation

Module 8: Introduction to Deep Learning

Module 9: Fundamentals of Neural Networks

Module 10: Convolutional Neural Networks (CNN)

Module 11: Recurrent Neural Networks (RNN)

Module 12: Generative Adversarial Networks (GAN)

Module 13: Deep Reinforcement Learning

Module 14: Topics in Deep Learning

Module 15: Model Optimization and Deployment

Module 16: Introduction to Natural Language Processing

Module 17: Sentiment Analysis and Opinion Mining

Module 18: Text Classification and Topic Modeling

Module 19: Latest Models in Deep Learning 1

Module 20: Latest Models in Deep Learning 2

Hands-On Learning & Real-World Experience

Real-World Projects: Two Projects of the Student's Choice

Project Internships: Candidates who completed the course successfully are eligible.

Duration: Two Months – Real-time projects based on client requirements.

Who Should Attend?

Students Looking for a Professional Career in DS and AI: B. E / B.Tech / M.E / M.Tech / M.Sc / Masters with any discipline

Aspiring Data Scientists: Individuals looking to start a career in data science and gain the necessary skills to analyze data and extract valuable insights.

Professionals Seeking Career Transition: Professionals from diverse backgrounds who want to transition into the field of data science and enhance their career prospects.

Business Analysts: Business professionals who want to develop data-driven decision-making skills and leverage data to drive business growth.

IT Professionals: IT professionals interested in expanding their skill set to include data science and machine learning techniques.

Do not pass up this fantastic chance to launch your computer science career. Please put in your application now to join our cutting-edge journey!

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