4 to 6 Months
Online + Offline
15 Students Only Batch Size
Anyone
Beginner to Advanced Training
Understand the fundamentals of Data Science, Artificial Intelligence, Machine Learning, and their real-world applications. Learn the complete data science lifecycle, problem-solving methodology, industry workflows, data-driven decision making, and the tools and technologies used by modern Data Scientists.
Master Python programming from beginner to advanced level by learning Python fundamentals, variables, data types, operators, strings, conditional statements, loops, functions, object-oriented programming (OOP), lists, tuples, sets, dictionaries, file handling, modules, exception handling, iterators, generators, NumPy, Pandas, and writing efficient code for Data Science and AI applications.
Develop a strong mathematical and statistical foundation by learning descriptive statistics, probability, sampling techniques, probability distributions, Central Limit Theorem, hypothesis testing, Z-Test, T-Test, ANOVA, Chi-Square Test, correlation, covariance, linear algebra fundamentals, and statistical concepts required for Machine Learning algorithms.
Learn the complete data preparation process, including data collection, data cleaning, handling missing values, removing duplicates, treating outliers, feature engineering, feature selection, data transformation, encoding categorical variables, scaling, normalization, dimensionality reduction using PCA, handling imbalanced datasets using SMOTE, and preparing high-quality datasets for Machine Learning models.
Explore datasets using NumPy, Pandas, Matplotlib, Seaborn, and Plotly to understand patterns, relationships, trends, distributions, and hidden insights through statistical analysis and visualization techniques before building Machine Learning models.
Learn supervised and unsupervised machine learning algorithms, including Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, Random Forest, Ensemble Learning, Naive Bayes, K-Means Clustering, Hierarchical Clustering, Dimensionality Reduction, Principal Component Analysis (PCA), model training, hyperparameter tuning, cross-validation, performance evaluation, and real-world model development using Scikit-learn.
Master Artificial Intelligence concepts by learning Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), TensorFlow, Keras, optimization techniques, transfer learning, Natural Language Processing (NLP), Computer Vision, Transformer models, and building intelligent AI applications.
Understand modern Big Data technologies including Hadoop, HDFS, MapReduce, Apache Spark, and PySpark. Learn distributed computing, parallel processing, scalable data pipelines, and processing massive datasets efficiently.
Master SQL by learning relational databases, DBMS, RDBMS, SQL commands, joins, subqueries, aggregate functions, views, indexing, stored procedures, query optimization, and database management techniques used in Data Science projects.
Learn to build interactive dashboards and business intelligence reports using Power BI Desktop, Power Query, Data Modeling, DAX Functions, Power BI Service, and professional visualization techniques for presenting Data Science insights.
Learn Git and GitHub for version control, repository management, branching, merging, collaboration, project documentation, and maintaining professional Data Science projects.
Build multiple end-to-end Data Science and AI projects covering predictive analytics, classification, regression, clustering, recommendation systems, deep learning, NLP, computer vision, SQL, Power BI dashboards, and Big Data applications. Complete the program with resume building, GitHub portfolio development, mock interviews, technical interview preparation, and placement assistance.
Once you have completed the course, you will be able to generate your certificate and will also be eligible for placement assistance.
Note: Internship letter and global certifications will be issued exclusively upon the successful completion and submission of 80% of the projects and assignments.
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