AI-Powered Machine Learning – Basic Course

This 2-Month foundation course has been designed to introduce the trainees to Artificial Intelligence and Machine Learning concepts. Enrolling in this course helps students learn basic Python programming, data handling, and simple machine learning models. This course is most suitable for beginners interested in understanding AI technology and exploring further specialization

2 Months
Online / offline

Why choose Cronus Consultants to learn AI-ML?

Cronus Consultants is offering AI/ML training with a focus on Python programming, data preprocessing, machine learning algorithms, and real-world model development. The program emphasizes hands-on projects and practical implementation for building strong foundations for AI and data science careers.

Curriculum Overview

Download Syllabus

Concepts: History of AI, AI vs. ML vs. Deep Learning, supervised vs. unsupervised learning.
Tools: Setting up Jupyter Notebooks, Git/GitHub, Introduction to Windows Subsystem for Linux (WSL).
Python Essentials: Basic syntax, data types, control structures (loops, if-else), functions.

Libraries: NumPy (numerical analysis) and Pandas (data manipulation).
Techniques: Data cleaning, handling missing data (NaN), data transformation, grouping, and aggregation.
Visualization: Matplotlib/Seaborn for data plotting.

Statistics: Central Tendency (Mean, Median, Mode), Standard Deviation, Variance, Probability basics.
Linear Algebra: Vectors, Norms, Matrices, Matrix Operations, Eigenvalues/Eigenvectors.

Regression: Linear Regression, Logistic Regression, Lasso/Ridge.
Classification: K-Nearest Neighbors (KNN), Decision Trees, Support Vector Machines (SVM), Random Forests.
Clustering: Unsupervised learning, K-Means Clustering, Dimensionality Reduction (PCA).

Neural Networks: Introduction to Perceptron, Multi-Layer Perceptron (MLP).
Frameworks: Introduction to TensorFlow/Keras or PyTorch.
CNNs: Basics of Convolutional Neural Networks for Image Processing.

Generative AI: Introduction to Large Language Models (LLMs), Prompt Engineering, RAG (Retrieval-Augmented Generation).
Deployment: Model saving, API creation (FastAPI), basics of Cloud Deployment.

Build end-to-end ML projects
Deploy models on cloud platforms
Manage datasets and pipelines
Monitor model performance metrics

Key Features

ML model basics

Understand ML types, workflows and fundamental model concepts

Python setup to start coding

Use Python libraries like Pandas and NumPy for ML tasks.

Data handling

Clean, process, and structure datasets for model training.

Simple ML models

Use datasets to build a regression and classification model.

Who can pursue this course?

BBA B.A. B.Sc (Computer Science) M.Sc (Computer Science) B.TECH M.TECH Commerce MBA

      
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