Machine Learning Bootcamp: Python, Deep Learning & NLP
Published 7/2026
Created by Selfcode Academy
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Level: All Levels | Genre: eLearning | Language: English | Duration: 91 Lectures ( 37h 11m ) | Size: 22.5 GB
Master Machine Learning with Python, Scikit-learn, Deep Learning, NLP, PyTorch, model deployment, and real projects.
What you'll learn
Students will learn Python, NumPy, Pandas, statistics, and data preprocessing techniques required for machine learning. Students will build regression, classification, clustering, and ensemble models using Scikit-learn and real datasets. Students will train deep learning and NLP models using PyTorch, CNNs, RNNs, LSTMs, and Transformers. Students will deploy machine learning models using APIs, Docker, and cloud platforms and build end-to-end projects.Requirements
No prior experience in machine learning is required, as the course starts with Python and core concepts. Students should have access to a computer and an internet connection to run notebooks and projects. Basic programming knowledge is helpful, but all essential Python libraries are covered from scratch. A willingness to practice coding and complete hands-on projects will help students succeed.Description
Are you looking for a practical, project-based machine learning course that takes you from the fundamentals to professional-level AI development? Do you want to learn not only how machine learning algorithms work, but also how to build, evaluate, interpret, and deploy them in real-world applications?
Welcome toMachine Learning Bootcamp.
This course is designed to help you build a solid foundation in machine learning while gradually introducing advanced concepts such as deep learning, natural language processing, model explainability, and responsible AI. Whether you are a beginner starting your journey into artificial intelligence, a software developer looking to expand your skills, or a student preparing for internships and placements, this course will equip you with practical, industry-relevant knowledge.
Unlike courses that focus only on theory, this bootcamp emphasizes hands-on implementation. You will work with real datasets, build production-ready models, and understand the complete machine learning lifecycle-from collecting and preprocessing data to deploying intelligent applications.
The course begins with a refresher on Python programming and the mathematical foundations of machine learning. You will review NumPy, Pandas, statistics, probability, linear algebra, and the core concepts required to understand modern AI systems. You will also become familiar with Jupyter Notebooks, Git, and the machine learning ecosystem.
As you progress, you will explore the major branches of machine learning, including
Supervised learning Unsupervised learning Deep learning Natural language processing Model deployment Explainable AI Responsible AIYou will implement core machine learning algorithms, including
Linear regression Logistic regression K-nearest neighbors Decision trees Random forests Support vector machines K-means clustering DBSCAN Hierarchical clustering Principal component analysisThe course also covers practical data science workflows. You will learn how to clean data, handle missing values, engineer features, create pipelines, split datasets, and evaluate models using industry-standard metrics.
To help you improve model performance, you will study advanced topics such as
Feature selection Hyperparameter tuning Cross-validation Grid Search Random Search Bayesian optimization Ensemble learning XGBoost LightGBM Model stackingDeep learning is a major component of this bootcamp. You will understand how neural networks work internally and implement them using modern frameworks such as PyTorch. Topics include
Perceptrons Backpropagation Activation functions Convolutional neural networks (CNNs) Recurrent neural networks (RNNs) LSTMs and GRUs Transfer learning GPU-based trainingYou will then move into natural language processing and build applications using
Text preprocessing TF-IDF Word embeddings Sentiment analysis Hugging Face Transformers Large language modelsBuilding a model is only part of the journey. This course also teaches you how to deploy machine learning applications using
Flask FastAPI APIs Docker Streamlit Cloud deployment platformsModern AI systems must also be transparent and responsible. For that reason, you will learn how to interpret and audit machine learning models using
SHAP LIME Partial dependence plots Fairness metrics Bias detection Responsible AI practicesThroughout the bootcamp, you will complete multiple mini-projects and capstone projects that reinforce every major concept. Projects include spam classification, house price prediction, customer segmentation, sentiment analysis, customer retention systems, and more.
By the end of this course, you will not only understand the theory behind machine learning algorithms but also gain the confidence to build, optimize, explain, and deploy AI systems in real-world scenarios.
Who this course is for
This course is designed for beginners who want to start a career in machine learning and AI. This course is suitable for Python developers and software engineers expanding their skills. This course is ideal for students preparing for internships and careers in data science. This course is for anyone who wants to build real-world machine learning applications from scratch.
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