A Full-fledged Machine Learning Course for Beginners. Master End-to-end ML & DL Process, Python, Math, EDA and Projects.
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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.
Explore related topics
This course includes:
- 25.5 hours on-demand video
- 65 coding exercises
- Assignments
- 17 articles
- 16 downloadable resources
- Access on mobile and TV
- Full lifetime access
- Audio description in existing audio
- Certificate of completion
Coding Exercises
This course includes our updated coding exercises so you can practice your skills as you learn.
Course content
18 sections • 212 lectures • 25h 21m total length
- How to take the classes
- Take a refund now, if….
- Something Extra for you!
- Resources for this chapter
- What is Machine learning?
- The Regression models
- The Classification models
- The Clustering models
- Knowledge Assessment
- Deep Learning Starting
- Artificial Intelligence
- Programming languages
- Development environments
- Knowledge Assessment
- Resources of this chapter
- Data preparation
- Data modeling
- Model evaluation
- Model deployment
- Model evaluating metrics
- Knowledge Assessment
- Resources of this chapter
- Expressions and Variables
- Hands-on: Expressions and Variables
- Starting with python programming
- Understanding Data Types
- Hands-on: Data Types
- Converting data types
- Various String Operators
- Hands-on: String Operators
- Understanding Tuples and Lists
- Hands-on: Tuples and Lists
- Creating list
- Indexing list
- Slicing list
- Adding elements
- Removing elements
- Replacing elements
- Understanding Set
- Hands-on: Set operators
- Set integration
- Set reduction
- Understanding Dictionaries
- Hands-on: Dictionaries
- Create dictionary
- Adding keys and values
- Condition and Branching
- Hands-on: Condition & Branching
- Conditional statement #1
- Conditional statement #2
- Logical expression #1
- Logical expression #2
- Logical expression #3
- Loops for Iteration
- Hands-on: Loops
- For loop
- While loop
- Developing Functions
- Hands-on: Functions
- Dealing with function
- Object and Classes
- Hands-on: Object & Classes
- Dealing with function #2
- API, REST API & Request
- HTML and BeautifulSoup
- Hands-on Lab: BeautifulSoup
- Web scrapping in Python
- Reading and Writing with Pandas
- Hands-on Lab: Importing datasets
- Reading Files with Python
- Resources of this chapter
- Understanding Missing values
- Python for missing value identification
- Hands-on: Missing value identification
- Identifying missing values
- Understanding Imputation
- Python for missing value imputation
- Hands-on: Imputing missing values
- Imputing missing values
- Dataframe’s data types
- Python for casting data types
- Hands-on: Data types in dataframe
- Checking data type
- Assigning data type
- What is Inconsistent value?
- Python for dealing inconsistencies
- Hands-on: Working with inconsistencies
- Finding the unique values
- Removing inconsistent value
- Understanding duplicates
- Python for dealing duplicates
- Hands-on Working with duplicates
- Identify duplicates
- Removing duplicates
- What is data sorting?
- Python for sorting data
- Hands-on: Sorting data
- dataset sorting
- Understanding slicing
- Python for data slicing
- Hands-on: Data slicing
- Slicing with loc
- Slicing with iloc
- Methods of Data filtering
- Python for data filtering
- Hands-on: Data filtering
- Boolean filtering
- Multiple conditions
- Understanding data merge
- Python for merging data
- Hands-on: Merging dataframes
- Merging dataframes
- Understanding concatenation
- Python for concatenation
- Hands-on: Data concatenation
- Concatenating dataframes
- Resources of this chapter
- How to generate new feature?
- Python for generating new features
- Hands-on: Generating new features
- Feature generation
- Extracting date elements
- Python for extracting date elements
- Hands-on: Extracting date elements
- Extracting day, month and year
- When to encode feature
- Python for encoding feature
- Hands-on: Feature encoding
- Feature encoding
- When to bin feature
- Python for binning feature
- Hands-on: Feature binning
- Feature binning
- When to map feature
- Python for mapping feature
- Hands-on: Feature mapping
- Feature mapping
- When to generate dummies
- Python for generating dummies
- Hands-on: Generating dummies
- Generating dummies
- Resources of this chapter
- Kdeplot for distribution
- Python for creating Kdeplot
- Hands-on: Kdeplot
- Kdeplot for distribution
- Shapiro Wilk test
- Python for Shapiro Wilk test
- Hands-on: Shapiro Wilk test
- Normality test
- Data transformations methods
- Python for data transformation
- Hands-on: Data transformation
- SQRT transformation
- LOG transformation
- BOXCOX transformation
- Feature selection
- Python for Feature selection
- Hand-on: Feature selection
- Feature selection
- Methods of Feature scaling
- Python for scaling features
- Hands-on: Feature scaling
- Standard scaling
- MinMax Scaling
- Dimensionality reduction
- Python for Dimensionality reduction
- Hands-on: Dimensionality reduction
- Explained variance ratio
- Select n_component
- Principal component analysis
- Train-test split
- Python for train-test set
- Hands-on: Train-test set
- Train test split
- Resources of this chapter
- What is Matrix?
- Scalars and Vectors
- Linear algebra introduction
- Knowledge Assessment
- What is Tensor?
- Transpose of Matrix
- Dot product and Matrix
- Knowledge Assessment
- Resources of this chapter
- How Linear regression works
- Python for Linear regression model
- Hands-on: Linear regression model
- Build Linear Regression
- Prediction with Linear Regression
- Model evaluation
- How Logistic regression works
- Python for logistic regression
- Hands-on: Logistic Regression
- Build Logistic Regression
- Evaluate the LGR model
- How KMeans clustering works
- Python for KMeans clustering
- Hands-on: KMeans clustering
- Calculating WCSS
- Building KMeans cluster
- Resources of this chapter
- How Decision tree regression works
- Python for decision tree regression
- Hands-on: Decision tree regression
- Decision tree regression
- How Decision tree classification works
- Python for Decision tree classification
- Hands-on: Decision tree classification
- Decision tree classification
- How Random forest regression works
- Python for Random forest regression
- Hands-on: Random forest regression
- Random forest regression
- How Random forest classification works
- Python for Random forest classification
- Hands-on: Random forest classification
- Random forest classification
- Resources of this chapter
- How AdaBoost Models work
- Python for AdaBoost Models
- Hands-on: AdaBoost Models
- AdaBoost Classification Model
- How Traditional GBM works
- Python for Traditional GBM Model
- Hands-on: Traditional GBM Model
- Trad GBM Classification Model
- How CatBoost Models work
- Python for CatBoost Models
- Hands-on: CatBoost Models
- CatBoost Classification Model
- How LightGBM Models work
- Python for LightGBM Models
- Hands-on: LightGBM Models
- LightGBM Classifier
- How XGBoost Models work
- Python for XGBoost Models
- Hands-on: XGBoost Models
- XGBoost Models
- Resources of this chapter
- K-fold cross validation
- Python for cross-validation
- Hands-on: k-fold cross validation
- K-fold cross validation
- L1, L2 regularization
- Python for regularization
- Hands-on: Model regularization
- L1 & L2 regularization
- Oversampling methods
- Python for oversampling methods
- Hands-on: oversampling methods
- SMOTE – oversampling
- Undersampling methods
- Python for undersampling methods
- Hands-on: Undersampling methods
- Tomek Links – Undersampling
- Hyperparameter tuning
- Python for Hyperparameter tuning
- Hands-on: Hyperparameter tuning
- Bayes search CV
- Resources of this chapter
- Understanding Deep Learning
- Neural Networks in Deep Learning
- Layers v/s Deep net
- What is TensorFlow?
- How TensorFlow 2.0 works
- Knowledge Assessment
- What is Initialization?
- Glorot Initialization
- Stochastic Gradient Descent
- Knowledge Assessment
- Resources of this chapter
- Understanding the data
- Preprocessing the data
- Model training
- Model evaluation
- Solve Real problem with Deep Learning
- Resources of this chapter
- What is AI and AI workflow?
- Various types of Artificial intelligence
- Generative AI and Its use cases
- NLP, Speech Technology & Computer vision
- Knowledge Assessment
- What is Prompt?
- The Prompt engineering
- Best practices in PE
- Knowledge Assessment
- Resources of this chapter
- Project overview
- Diffusers – Stable Diffusion Pipeline
- runwayml/stable-diffusion-v1-5
- Developing Gen AI Text to Image Model
- Hands-on Lab: Gen AI Text2Image
- Resources of this chapter
- Project overview
- Gemma-2-2b Model for Chatbot
- Llama-2-7b Model for Chatbot
- Developing Gen AI Chatbot
- Hands-on Lab: Gen AI Chatbot
- Classifying the Best Strikers
- 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 Needed
This 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 Different
This 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 Intelligence
- What 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 Beginners
- Starting 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 Essentials
- Handling 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 Intelligence
- Generating 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 Visualization
- Creating 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 Intuition
- Linear 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 Scratch
- Linear 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 Favorites
- AdaBoost, 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 Improvement
- K-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 2.0
- Understanding 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 Engineering
- AI 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 Deployment
- Real-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 With
By 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 Note
This 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 Here
Machine 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.
Who this course is for:
- Absolute beginners with no prior experience in machine learning, coding, or data science who want to master the foundation of ML strongly before diving into ADVANCED steps.
Note: The course is free for a limited time and limited to 500 users. Enroll as soon as possible. If it is no longer free, it means you are late, and it has already exceeded its limits.
🎫 After adding the course into Cart at Checkout page, Apply Coupon Code➛. 030184B120A44AAB8327 Note:- After applying coupon, if it says ”This coupon has exceeded its maximum possible redemptions…”, then it means you’re Late, and first 500 coupons already redeemed.
