AI & Machine Learning
Build future ready skills for real innovation.
Want to upgrade your current profile, or hope to build intelligent applications, mastering these technologies gives you a strong edge.
- LEARN FROM THE EXPERTS
- INDUSTRIAL EXPOSURE
- INTERACTIVE CLASSES
- COUNSELOR’S SUPPORT
Students Enrolled
0
+
Course Duration
1
months
Modules
0
Languages/Tools
0
What you'll be ready for
Master the skills and tools used to build intelligent AI-powered solutions.
Intro to ML & Python
Understand ML fundamentals, applications, challenges, and master Python data types, loops, and functions.
Data Pre-Processing
Dataset preparation, training/testing splits, NumPy, Pandas, Matplotlib, and handling missing data.
ML Advanced Topics
SVM, KNN, classification trees, Naive Bayes, clustering algorithms, and market basket analysis.
Intro to NLP
Text classification, NLTK, tokenization, POS tagging, named entity recognition, and SpaCy.
Deep Learning Basics
Neural networks, activation functions, ANN, gradient descent, and deep learning vs ML comparison.
Real-World AI Projects
Create practical AI applications and portfolio-ready projects.
Who This Course is For
Aspiring Tech Professionals: Ideal for individuals looking to start or
pivot their careers into AI, data science, or machine learning.
Business Leaders and Entrepreneurs: For those seeking to
integrate AI into their business strategies to drive innovation and
efficiency.
Students and Fresh Graduates: Perfect for those wanting to build a
strong foundation in AI and its applications across industries.
Working Professionals: Designed for engineers, developers, and analysts
aiming to upskill and stay competitive in their careers.
Tech Enthusiasts: Anyone passionate about understanding AI and
exploring its transformative potential in various fields.
Benefits of this Course
From automating routine tasks to unleashing your creative potential, AI is the key to efficiency, productivity, and staying ahead in your field.
In-Demand Skills
Gain AI skills that are highly valued across industries and job roles.
Industry Aligned Curriculum
Master tools and techniques used by leading companies.
Job Ready Training
Build practical expertise to confidently enter the workforce.
Expert Led Sessions
Learn directly from experienced industry professionals.
Hands On Learning
Gain real-world experience through practical exercises and labs.
High Growth Opportunities
Unlock career paths in one of the fastest-growing fields.
Real World Projects
Work on industry-relevant projects to strengthen your portfolio.
Future Proof Skills
Develop skills that remain valuable in an AI-driven future.
Course Curriculam
Module 1 - Introduction to ML and Python
- Introduction to ML
- Applications of ML and Challenges
- Introduction to Python
- Python Data Types
- Data Types Practical
- List in Python
- Tuples in Python
- Dictionary in Python
- Sets in Python
- Loops in Python
- Comprehension in Python
- Functions in Python
- Lambda Functions in Python
- Exception Handling in Python
- Regular Expressions in Python
Module 2 - Data Pre Processing in Python
- Dataset Preparation
- Training and Testing Set
- Importing Dataset
- Essential Libraries for Pre Processing
- Numpy Introduction
- Numpy Operations & Manipulation
- Pandas in Python
- Pandas Operations for Data
- Pandas Bonus Operations
- Matplotlib in Python
- Handling Missing Data
Module 3 - Regression Model
- Linear Regression
- Plot for Predicted vs. Test Data
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Evaluation of Regression Models
- Regression Model Selection in Python
- Logistic Regression
Module 4 - ML Advanced Topics
- Support Vector Machine
- K Nearest Neighbours
- Decision Tree Classification
- Random Forest Classification
- Naive Bayes Classification
- Evaluation Metrics in Classification
- K Means Clustering
- Optimising K Means Clustering and Elbow
- Method
- Hierarchical Clustering
- Apriori Algorithm
- Market Basket Analysis
Module 5 - Deep Learning Basics
- Introduction to Deep Learning
- History and Context
- Machine learning vs deep learning
- DL Basics Neurons,Synapses,Activation Funcns
- Understanding Activation Functions
- Artificial Neural Networks
- Gradient Descent vs Brute Force Optimization
- ANN Code in Python
Module 6 - Conventional Neural Network & Recurrent Neural Network
- Convolutional Neural Networks
- Understanding Layers of CNN
- Convolution in CNN
- Keras & TensorFlow for CNN
- CNN using Python
- Introduction to Transformers
- Encoder Decoder Achitecture
- What is RNN & Working of RNN
- Vanishing Gradient Problem in RNN
- LSTM in Deep Learning
- Working of LSTM
- Variations of LSTM
Module 7 - Self Organizing Maps(SOM)
- What is Self Organizing Maps(SOM)
- Creation & Working of SOM
- K Means Clustering Introduction
- K Means Clustering
- K Means Clustering Code Python
- Optimising K Means Clustering and Elbow Method
Module 8 - Boltzmann Machines & Auto Encoders
- Boltzmann Machines
- Boltzmann Machines vs Neural Networks
- Boltzmann Machine Code in Python
- Auto Encoders in Deep Learning
- Types of Autoencoders
- Autoencoders in Machine Learning
- Training Process in Autoencoders
- AutoEncoder Code in Python
Module 9 - Introducing NLP
- NLP-Introduction, Challenges & Applications
- Text Classification & Translation Pipeline
- Text Clustering & Classification
- Introduction to NLTK
- Tokenization
- Stop Word Removal
- Stemming and Lemmatization
- POS Tagging using Spacy
- Dependency Parsing using SpaCy
- Lemmatization using SpaCy
- Named Entity Recognition
- Introduction to POS Tagging
- POS Tagging Architecture
- POS Tagging Approach & Applications
- Rare Word Removal
- Correcting Words using NLTK
- Tokenization using SpaCy
- NER using SpaCy
- Vector Similarity in spaCy
- NLTK vs spaCy
Module 10 - Text Representation & Sentiment Analysis
- Bag of Words Technique
- TF-IDF
- Word Embeddings
- Sentence Embeddings
- Working with Text Files
- What is Sentiment Analysis
- Challenges, Applications & Takeaways
- How sentiment is detected
- Sentiment Analysis Implementation
- Text Preprocessing for Sentiment Analysis
- Text Cleaning Code
- Sentiment Analysis with machine Learning
- ML for Sentiment Analysis Code
- Training & Hyperparameter Tuning for Sentiment Analysis
Module 11 - Advanced Sentiment Analysis
- Multi Label Classification
- Multi Label Binarization
- Word2Vec Embeddings
- Data Preparation for Sentiment Analysis using Word2Vec
- Embeddings
- Data preprocessing for sentiment analysis using
- word2vec embeddings
- Emotion Recognition in Text using GloVe
- What is CV Parsing in NLP
- Implementation of CV Parsing using Spacy3
Project Hate Speech Analysis
Module 12 - Working with Transformers
- Introduction to Transformers
- Encoder Decoder Achitecture
- Applications of Transformers
- Pretrained Language Models - BERT & GPT
- Prompt Engineering - Techniques & Examples
- Multi-modal NLP & Applications
- Introduction to GEN AI
Module 13 - Open CV
- Getting Started With Images
- Basic Image Manipulation
- Image Annotation
- Image Enhancement
- Accessing the Camera
- Video Writing
- Image Filtering
- Image Features and Alignment
- Panorama
- HDR
- Object Tracking
- Face Detection
- TensorFlow Object Detection
- Pose Estimation using OpenPose