Join the 'AI-DS (Artificial Intelligence & Data Science)' Learning Programme.

Combine AI and Data Science to extract insights and develop intelligent systems for solving real-world challenges.

AI-DS

Artificial Intelligence and Data Science are a powerful duo transforming the way we approach decision-making and problem-solving. AI enhances the capability to predict outcomes, while Data Science unravels insights hidden in vast datasets. Together, they form a robust toolkit for building intelligent applications and driving innovation. At Code Vision Solutions, our AI-DS program covers statistical analysis, machine learning, and AI integration using Python, R, and advanced frameworks. Learn to preprocess data, train AI models, and visualize results effectively. Gain industry-ready skills to thrive in domains like healthcare, finance, and technology. Join us to unlock the potential of AI-DS.

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Students Feedback

We have helped build 2100+ Careers

Code Vision Solutions helped me transform my theoretical knowledge into practical skills. The hands-on projects and expert mentorship prepared me for the real-world challenges of the tech industry. I secured my dream job within months of completing my training!

Rahul Verma

Software Developer

The industrial training program provided me with an incredible learning experience. The exposure to live projects and advanced technologies gave me the confidence to excel in my career. Thanks to Code Vision Solutions, I’m now working with one of the top tech companies!

Priya Sharma

Data Analyst

The placement assistance was exceptional! Code Vision Solutions not only helped me build a strong resume but also offered mock interviews and personalized career guidance. I’m grateful for the support in landing a role with a leading MNC.

Sandeep Patel

Full Stack Developer

The internship I completed at Code Vision Solutions was a game-changer. It provided valuable industry insights and boosted my professional skills. I’m now part of an exciting startup, all thanks to the hands-on experience I gained during the internship.

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Web Developer

Structured Curriculum for Smooth Learning

Introduction to AI and Data Science

Overview of AI, machine learning, deep learning, and data science.

The role of data science and AI in various industries.

Ethical considerations and privacy concerns in AI.

Programming for Data Science

Python basics and libraries: NumPy, pandas, Matplotlib, Seaborn, SciPy.

Working with data structures (lists, arrays, dictionaries, etc.).

Object-oriented programming in Python.

File handling and data manipulation.

Data Collection and Data Cleaning

Data collection techniques (web scraping, APIs, databases).

Handling missing data, duplicates, and outliers.

Feature engineering and preprocessing.

Working with structured and unstructured data (CSV, JSON, text, etc.)

Exploratory Data Analysis (EDA)

Data visualization (Matplotlib, Seaborn, Plotly).

Descriptive statistics (mean, median, variance, correlation).

Identifying patterns, trends, and insights in the data.

Univariate and multivariate analysis.

Machine Learning

Supervised learning: Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), k-NN.

Unsupervised learning: Clustering (K-Means, Hierarchical), PCA (Principal Component Analysis), Anomaly Detection.

Model evaluation techniques: Cross-validation, confusion matrix, ROC-AUC curve, F1-score.

Hyperparameter tuning and grid search.

Advanced Machine Learning

Ensemble methods: Bagging, Boosting, AdaBoost, Gradient Boosting.

Feature selection techniques.

Dimensionality reduction (PCA, LDA).

Model deployment strategies (saving models, Flask API for deployment).

Deep Learning

Introduction to neural networks.

Working with TensorFlow and Keras.

Building deep neural networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN).

Transfer learning and fine-tuning models.

Natural Language Processing (NLP)

Text preprocessing: Tokenization, stemming, lemmatization.

Bag of words, TF-IDF, and Word2Vec.

Sentiment analysis, text classification, and Named Entity Recognition (NER).

Working with libraries: NLTK, spaCy, Gensim.

Time Series Analysis

Components of time series: Trend, seasonality, noise.

ARIMA, Exponential Smoothing, and other forecasting techniques.

Time series data preprocessing and visualization.

Reinforcement Learning (Optional Advanced Topic)

Basics of reinforcement learning.

Markov Decision Processes (MDP).

Q-Learning and Deep Q-Networks (DQN).

Applications of reinforcement learning.

Big Data and Cloud Computing

Working with large datasets using Hadoop and Spark.

Introduction to cloud platforms: AWS, Google Cloud, Azure.

Using cloud-based tools for data storage and computation.

Capstone Project

Real-world problem-solving with a combination of AI, machine learning, and data science techniques.

End-to-end project: Data collection, preprocessing, model building, evaluation, and deployment.

Soft Skills and Career Guidance

Data storytelling and effective communication.

Resume building and interview preparation.

Navigating the data science job market.

Instructors

Shravan

Founder and T&P Officer

Naveen

Founder & Marketing Head

Vijay

Founder & CEO

Manohar

Founder & Tech Lead

support and guidance

Your Future Starts Here.

Redefine the future with AI-DS expertise. Begin your journey in Artificial Intelligence and Data Science today!