Published 7/2025
Created by Shahriar’s Intelligence Academy
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 212 Lectures ( 25h 21m ) | Size: 8.3 GB
A Full-fledged Machine Learning Course for Beginners. Master End-to-end ML & DL Process, Python, Math, EDA and Projects.
What you’ll learn
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.
Requirements
No experience is required. You will learn everything from scratch.
Just make sure you have laptop/desktop and good internet connection.
Description
Master the End-to-End Machine Learning Process with Python, Mathematics, and Projects — No Prior Experience NeededThis course is not just another introductory tutorial. It is a complete and intensive roadmap, carefully crafted for beginners who want to become confident and capable Machine Learning practitioners. Whether you’re a student, a job-seeker, or a working professional looking to transition into AI/ML, this course equips you with the core skills, hands-on experience, and deep understanding needed to thrive in today’s data-driven world.Why This Course Is DifferentThis masterclass solves both problems by following a clear, layered, and project-oriented curriculum that blends coding, theory, and practical intuition — so you not only know what to do, but why you’re doing it.You’ll go step-by-step from foundational Python to building real ML models and deploying them in real-world workflows — even touching advanced topics like ensemble models, hyperparameter tuning, regularization, and generative AI.What You’ll Learn — Inside the Masterclass#______Foundations of Machine Learning and Artificial IntelligenceWhat is ML, how it differs from AI and Deep Learning.Key ML model types: Regression, Classification, Clustering.Understanding AI applications, Gen AI, and the future of intelligent systems.Knowledge checks to reinforce conceptual understanding.#______Python Programming from Scratch – for Absolute BeginnersStarting with variables, data types, conditionals, loops, and functions.Data structures: Lists, Sets, Tuples, Dictionaries with hands-on labs.Object-oriented programming, API requests, and web scraping with BeautifulSoup.Reading and writing real-world datasets using pandas.#______Data Cleaning and Preprocessing – Real-World EssentialsHandling missing values, data types, inconsistencies, and duplicates.Sorting, slicing, filtering, merging, and concatenating datasets.Performing these operations with structured labs and real datasets.#______Feature Engineering – Turning Raw Data into IntelligenceGenerating new features from date/time and domain knowledge.Encoding categorical variables, binning, mapping, and generating dummies.Prepping datasets to enhance model performance.#______Exploratory Data Analysis (EDA) and VisualizationCreating distribution plots using KDE.Checking for normality with Shapiro-Wilk tests.Performing data transformations (Log, Sqrt, Box-Cox).Selecting meaningful features and reducing dimensions via PCA.#______Mathematics for Machine Learning – Build True IntuitionLinear Algebra: Vectors, Matrices, Dot Product, and Transpose.Understanding tensors and their applications in deep learning.Grasping the math behind model architecture and training logic.#______Machine Learning Algorithms – Explained and Built from ScratchLinear Regression, Logistic Regression, KMeans Clustering.Decision Trees, Random Forests (Regressor & Classifier).Building models line-by-line in Python with evaluations and predictions.Working with real datasets in guided hands-on labs.#______Advanced Boosting Algorithms – The Industry’s FavoritesAdaBoost, Gradient Boosting (GBM), CatBoost, LightGBM, and XGBoost.Step-by-step breakdown of how these models work and how to train them.Understanding when and why to use each one.#______Model Evaluation, Optimization, and ImprovementK-fold cross-validation, L1 & L2 regularization.Oversampling & undersampling methods (SMOTE, Tomek Links).Hyperparameter tuning using GridSearch, RandomSearch & Bayesian methods.Making your models more robust, fair, and generalizable.#______Deep Learning Fundamentals with TensorFlow 2.0Understanding how neural networks learn.Layers, activation functions, weight initialization (Glorot), and SGD.Preprocessing data, training neural nets, evaluating and improving DL models.#______Introduction to Generative AI and Prompt EngineeringAI workflow, types of AI, and Gen AI applications in NLP, vision, and speech.Prompt engineering: what it is, how it works, and real-world best practices.Projects like building a chatbot with LLaMA and generating images using Stable Diffusion.#______Hands-On Real Projects – From Scratch to DeploymentReal-life ML tasks including classification and regression case studies.Deep learning projects: text-to-image generation and chatbot development.Walkthroughs of full ML pipelines: cleaning, modeling, evaluating, and presenting results.Building portfolios worthy of recruiters and hiring managers.What You’ll Walk Away WithBy the end of this course, you’ll have the ability to:Write clean Python code for machine learning projects.Understand and explain how various ML algorithms work.Perform data cleaning, EDA, feature engineering, and model training.Evaluate and fine-tune models using advanced techniques.Work on real ML projects that simulate professional work environments.Understand deep learning fundamentals and generative AI workflows.Build a portfolio that can help you land entry-level to intermediate ML jobs or freelance gigs.One Honest NoteThis course emphasizes real understanding, not animated fluff. Lessons are code-first, explanation-rich, and designed for learners who want depth, not shortcuts. If you’re ready to invest the effort, the rewards are real.Final Thought: Your Transformation Starts HereMachine Learning is not just a hot trend — it’s the future of decision-making, automation, and innovation. But mastering it takes commitment.This 2025 Machine Learning Masterclass will guide you through that journey step-by-step — helping you not only learn ML, but think like an ML practitioner, and work like one too.Join now and start your transformation into a Machine Learning expert.
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