AI & Machine Learning Course

Build practical and industry-oriented Artificial Intelligence and Machine Learning skills with BASINDIA Academy. Learn how intelligent systems work, how data is prepared and how machine learning models can be developed and evaluated.

Our course starts from programming and data fundamentals and gradually progresses to Machine Learning, Deep Learning, Natural Language Processing, Computer Vision and practical AI projects using Python and popular machine learning tools.

What You Will Learn

01. Introduction to Artificial Intelligence: Understand Artificial Intelligence, Machine Learning, Deep Learning, AI applications, types of AI and the role of AI in modern software and business applications.

02. Python Programming – Basic: Learn Python fundamentals including variables, data types, operators, conditional statements, loops, functions, lists, tuples, sets, dictionaries, modules and exception handling.

03. Python for AI & Data Science: Learn NumPy, Pandas, data structures, DataFrames, data loading, filtering, sorting, grouping, merging, data transformation and working with datasets.

04. Mathematics & Statistics for Machine Learning: Understand basic statistics, mean, median, variance, standard deviation, probability, correlation and mathematical concepts used in Machine Learning.

05. Data Preprocessing: Learn data cleaning, missing value handling, duplicate removal, outlier detection, feature transformation, encoding categorical data and preparing datasets for Machine Learning.

06. Exploratory Data Analysis: Learn how to explore datasets, identify patterns, understand relationships between variables and visualize data using charts and graphs.

07. Machine Learning Fundamentals: Understand Machine Learning concepts, features, labels, training data, testing data, model training, prediction and the Machine Learning workflow.

08. Supervised Learning: Learn regression and classification techniques including Linear Regression, Logistic Regression, Decision Trees, Random Forest and K-Nearest Neighbors.

09. Unsupervised Learning: Learn clustering and pattern discovery techniques including K-Means Clustering and other unsupervised learning concepts.

10. Model Evaluation: Learn how to evaluate Machine Learning models using accuracy, precision, recall, F1-score, confusion matrix, MAE, MSE, RMSE and other evaluation techniques.

11. Feature Engineering: Learn feature selection, feature transformation, encoding techniques, scaling and creating useful features for Machine Learning models.

12. Deep Learning Fundamentals: Understand neural networks, neurons, activation functions, layers, training processes and the fundamentals of Deep Learning.

13. Natural Language Processing: Learn the fundamentals of Natural Language Processing, text preprocessing, tokenization, text classification and basic AI applications using text data.

14. Computer Vision: Understand image data, image preprocessing, image classification and the fundamentals of Computer Vision applications.

15. Generative AI Fundamentals: Understand the fundamentals of Generative AI, Large Language Models, prompts, AI-powered applications and practical use cases.

16. AI Tools & Application Development: Learn how AI technologies can be integrated into practical applications and how AI-based solutions can be developed for real-world use cases.

17. Real-World AI & ML Projects: Build practical projects such as prediction systems, classification applications, customer analysis, recommendation concepts, text-based applications and other AI-powered solutions.

18. Career Preparation: Build an AI & Machine Learning resume and portfolio, prepare project presentations, practice technical interview questions, participate in mock interviews and receive career guidance.