AI/ML - Program-Participants - Copy

Join SkillUp – Engineering Your Path to a Future-Ready Career!

Provides a comprehensive introduction to AI/ML, covering core concepts like supervised and/or unsupervised learning, neural networks, deep learning, NLP etc. It emphasizes practical applications, equipping learners with skills to build and deploy data-driven solutions

Program Framework & Schedule

Course Content

Introduction to Data Science, AI, and Data Engineering

Foundations of Python Programming

❖ Variables, data types, control structures
❖ Functions, OOP, and modules
❖ Core libraries: NumPy, Pandas, Matplotlib, Seaborn

Mathematics for Data Science

❖ Linear Algebra: Matrices, vectors, eigenvalues/eigenvectors
❖ Probability & Statistics: Bayes' theorem, distributions, hypothesis testing
❖ Calculus: Differentiation and integration for optimization

SQL and Databases Overview

❖ Writing basic to advanced SQL queries
❖ Data manipulation: Joins, Group By, Subqueries
❖ Intro to NoSQL databases: MongoDB, Redis basics

Exploratory Data Analysis (EDA)

❖ Data distributions, relationships, and feature engineering
❖ Data visualisation and interpretation with Matplotlib, Seaborn

Supervised Learning

❖ Algorithms: Linear Regression, Logistic Regression
❖ Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM)
❖ Evaluation Metrics: RMSE, MAE, ROC-AUC, F1-score

Unsupervised Learning

❖ Clustering: K-means, DBSCAN
❖ Dimensionality Reduction: PCA, t-SNE

Data Engineering Essentials

❖ ETL Processes and Data Pipelines
❖ Data extraction, cleaning, transformation
❖ Tools: Apache Airflow, Apache NiFi

Big Data and Distributed Processing

❖ Hadoop ecosystem: HDFS, MapReduce basics
❖ Apache Spark: RDDs, DataFrames, SparkSQL

Cloud Computing Basics

❖ AWS Services: S3, EC2, RDS, Lambda
❖ Equivalent services in Azure and GCP

Data Storage and Management

❖ Relational Databases: PostgreSQL, MySQL
❖ Data Lakes vs. Data Warehouses
❖ Distributed systems: Hive, HBase

Neural Networks Fundamentals

❖ Basics of Artificial Neural Networks, activation functions, backpropagation
❖ Deep Learning frameworks: TensorFlow, PyTorch

Deep Learning Models

❖ Convolutional Neural Networks (CNNs): Image classification, object detection
❖ Recurrent Neural Networks (RNNs) and LSTMs: Sequence modelling

Natural Language Processing (NLP)

❖ Text preprocessing and vectorization (TF-IDF, Word2Vec)
❖ Transformers and large language models: BERT, GPT

Reinforcement Learning (RL)

❖ Basics of Reinforcement Learning: Q-learning, policy gradient methods

AI/ML Deployment and MLOps

❖ Model Deployment Techniques
❖ REST APIs for model serving: FastAPI, Flask
❖ Model deployment with Docker and Kubernetes

MLOps for Continuous Integration and Deployment (CI/CD)

❖ Monitoring with MLflow, TensorBoard
❖ Model retraining pipelines

AI Governance and Ethics

❖ Explainability frameworks: SHAP, LIME
❖ Bias detection and fairness in AI models

Project: Building a Complete Data Pipeline

❖ End-to-end project incorporating data ingestion, cleaning, modelling, and deployment
❖ Sample Projects: Predictive maintenance, Recommender system, Sentiment analysis

Industry Standards Addons

❖ Advanced Feature Engineering and Hyperparameter Tuning
❖ Model evaluation and selection best practices

Total Program Fee:

INR 25,000/-

INR 2,50,000/-

Interactive sessions led by industry veterans
Performance based incentives
Practical experience with real-world projects
Real world Industry alignment and exposure

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Eligibility Criteria

Age

Must be between 18 and 45 years old

Technical Requirements

A laptop with a minimum of 8 GB RAM and a reliable internet connection is necessary

Education

Diploma holders in computer science, or graduates/postgraduates from any stream

Other Key Qualities

Pro activeness and determination

Language Skills

Basic English proficiency is required

Coding Experience

No prior coding skills are needed to join the course