AI-Powered Machine Learning – Advanced Course

This 4-month course on AI-Integrated Machine Learning helps students develop practical machine learning skills through real datasets and predictive models. In this course, the trainees will learn algorithms, data analysis, and industry-standard AI tools. This course has been designed to prepare students for entry-level roles in data analysis, machine learning development, and AI-based applications.

4 Months
Online / offline

Why choose Cronus Consultants to learn AI-ML?

Cronus Consultants is offering AI/ML training focused on Python programming, data preprocessing, machine learning algorithms, and real-world model development. The program emphasizes hands-on projects and practical implementation to build a strong foundation 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 an end-to-end ML project
Model deployment on Cloud
Real-time prediction pipelines
Secure cloud data storage

Key Features

ML algorithms

Implement supervised and unsupervised learning algorithms.

Data processing

Apply feature scaling, encoding and transformations.

Model evaluation for accuracy check

Use accuracy, precision and recall metrics to evaluate models.

Use cases

Use machine learning models to solve real-world problems.

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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