A Full-fledged Machine Learning Course for Beginners. Master End-to-end ML & DL Process, Python, Math, EDA and Projects.
What you will learn
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Understand what Machine Learning is, its model types, AI concepts, programming tools, and how to take the course effectively.
Learn the complete ML workflow: data preparation, modeling, evaluation, deployment, and model performance metrics.
Master Python fundamentals including variables, data types, strings, conditionals, loops, functions, objects, and APIs.
Scrape data using BeautifulSoup, fetch data from APIs, and read/write datasets using pandas and Python file operations.
Clean real-world data by handling missing values, fixing inconsistencies, removing duplicates, sorting, slicing, and filtering.
Generate, extract, encode, bin, map, and create dummy variables to transform raw data into model-ready features.
Visualize distributions with KDE plots, test for normality, and apply transformations like log, sqrt, and boxcox.
Select key features, scale data, apply PCA for dimensionality reduction, and prepare inputs for model training.
Split data using train-test methods and build a reliable data pipeline for supervised learning workflows.
Learn linear algebra basics like vectors, matrices, tensors, and operations like dot product, transpose, and reshaping.
Understand and implement linear regression, logistic regression, and KMeans clustering with hands-on coding in Python.
Build and evaluate decision trees and random forest models for both regression and classification tasks.
Train advanced models including AdaBoost, Gradient Boosting, CatBoost, LightGBM, and XGBoost with Python and evaluate them.
Use k-fold validation, apply L1/L2 regularization, handle imbalanced data, and tune hyperparameters using BayesSearchCV.
Explore deep learning basics, neural networks, layers, initialization, and optimization using TensorFlow 2.0.
Preprocess data, train, evaluate deep learning models, and solve real problems with hands-on TensorFlow projects.
Learn AI workflow, Gen AI use cases, NLP, speech, vision, and craft effective prompts for real-world applications.
Build a GenAI chatbot with LLaMA and create a text-to-image generator using stable diffusion pipelines.
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