Key Features
Completion Certificate
Internship
Internship Certificate
7 Days Refund Policy
Expert Instructors
One-to-One Session
What Will You Learn?
Accelerate your learning journey with our comprehensive course designed to equip you with essential skills and practical knowledge in Mastering Machine Learning 2025.
- Introduction to Machine Learning
- Supervised Learning (Linear Regression, Decision Trees, Random Forest)
- Unsupervised Learning (K-Means, DBSCAN, PCA)
- Reinforcement Learning
- Deep Learning and Neural Networks
- Model Evaluation and Hyperparameter Tuning
- Natural Language Processing (NLP)
- Computer Vision with Convolutional Neural Networks (CNNs)
- Time Series Forecasting
- Machine Learning in Real-World Applications (Finance, Healthcare, Marketing)
- AI Ethics and Responsible AI Design
Requirements
Before getting started with this course, it's beneficial to have the following:
- Laptop with internet access
- Basic understanding of programming (preferably Python)
- Familiarity with mathematics (linear algebra, calculus, statistics)
- Curiosity to explore machine learning and data science solutions
- Willingness to work with real-world datasets and build solutions
Course Completion
Yes
Curriculum
- Python Introduction and its comparison with other programming languages
- Key Features of Python and why it is widely used
- Testing Python installation with a Hello World program
- Introduction to predefined functions and commonly used Python modules
- Naming conventions, Python reserved words, and an overview of data types in Python
- Arithmetic, bitwise, comparison, and assignment operators, along with their precedence and associativity
- Compound operators, identity operators, and membership operators explained
- What is a list?
- Creating a list
- Accessing the list elements
- Adding new data in the list
- The slice operator with list
- Modifying a list
- Deletion in a list
- Appending/prepending items in a list
- Multiplying a list
- Membership operators on list
- Built-in functions for list
- Methods of list
- List comprehension
- What is a function?
- Function vs method
- Steps required for developing user-defined functions
- Calling a function
- Returning values from a function
- Arguments vs parameters
- Types of arguments
- Variable scope
- Local scope
- Global scope
- Argument passing
- Anonymous functions or lambda functions
- The map() function
- The filter() function
- Using map() and filter() with lambda expressions
- Iterators and generator functions
- Procedure-oriented programming vs object-oriented programming
- What are classes and objects?
- init() method
- Types of variables in a class
- Types of methods in a class
- Difference between local variable, class variable, and instance variable
- Difference between instance method, class method, and static methods
- Concept of encapsulation
- How to declare private members in Python?
- The setattr() and getattr() functions
- Object class, repr() and str() methods
- Concept of inheritance
- Types of inheritance
- Single inheritance
- Using super()
- Method overriding
- Multilevel inheritance
- Hierarchical inheritance
- Multiple inheritance
- The MRO algorithm
- Hybrid inheritance
- The diamond problem
- Operator overloading
- What is abstraction?
- Abstract class
- What is a database?
- Steps for connecting to MySQL from Python
- Exploring connection and cursor objects
- Executing SQL queries in Python
- Different methods for fetching data
- Executing INSERT commands in databases
- Performing UPDATE operations in databases
- Executing DELETE commands in databases
- Introduction to MongoDB
- What is Apache Atlas and its features
- Setting up MongoDB Atlas
- Querying documents in MongoDB
- Inserting, deleting, and updating MongoDB documents
- Bulk insert operations in MongoDB
- Updating multiple documents in MongoDB
- Understanding insertOne vs insertMany()
- updateOne() vs updateMany() in MongoDB
- Understanding find() and fetchall() in databases
- Understanding deleteOne() and deleteMany() in MongoDB
- Filtering documents in MongoDB
- Pandas Series overview
- Pandas DataFrame basics
- Introduction to Pandas Panel
- Key functionalities in Pandas
- Reading CSV files with Pandas
- Reading JSON files with Pandas
- Loading data from MySQL using Pandas
- Performing aggregations in Pandas
- Grouping data with Pandas
- Merging and joining data in Pandas
- Concatenating data in Pandas
- Date handling in Pandas
- Using .loc() and .iloc() functions in Pandas
- Working with Windows functions in Pandas
- Indexing and selecting data in Pandas
- Data cleaning with Pandas
- Handling missing data in Pandas
- Working with categorical data in Pandas
- NumPy ndarray object overview
- NumPy data types
- Attributes of NumPy arrays
- Array creation routines in NumPy
- Creating NumPy array from existing data
- Generating arrays from numerical ranges in NumPy
- Indexing and slicing in NumPy
- Advanced indexing in NumPy
- Broadcasting in NumPy
- Iterating over arrays in NumPy
- Array manipulation with NumPy
- Binary operators in NumPy
- String functions in NumPy
- Mathematical functions in NumPy
- Arithmetic operations in NumPy
- Statistical functions in NumPy
- Sorting, searching, and counting functions in NumPy
- Byte swapping in NumPy
- Copies and views in NumPy
- NumPy matrix library
- Linear algebra operations in NumPy
- Matplotlib Pyplot overview
- Plotting with Matplotlib
- Creating subplots in Matplotlib
- Line charts with Matplotlib
- Bar charts with Matplotlib
- Histogram charts with Matplotlib
- Pie charts with Matplotlib
- Creating histograms with Seaborn
- Kernel density estimates in Seaborn
- Seaborn FacetGrid
- PairGrid in Seaborn
- Boxplot, violin plot, and contour plot in Seaborn
- Countplot in Seaborn
- Heatmaps with Seaborn
- Plotly bar charts, histograms, and pie charts
- Plotly scatter plots and bubble charts
- Plotly distplot, density plot, and error bar plots
- Heatmaps with Plotly
- 3D scatter plot and surface plot in Plotly
- Plotly with pandas and cufflinks
- Plotly with Matplotlib and ChartStudio
- Visualizing pairwise relationships
- Statistical estimation with visualizations
- Finding linear relationships
- Correlation analysis between variables
- Introduction to Statistics
- Different types of statistics
- Population vs sample
- Measures of central tendency: Mean, Median, and Mode
- Variance and Standard Deviation
- Why sample variance uses n-1
- Understanding Standard Deviation
- Types of variables
- Introduction to random variables
- Percentiles and quartiles
- The 5-number summary
- Understanding histograms
- Gaussian or normal distribution
- Standard normal distribution
- Application of Z-score
- Basics of probability
- Addition rule in probability
- Multiplication rule in probability
- Permutation theory
- Combination theory
- Log-normal distribution
- Central Limit Theorem
- Left and right-skewed distributions and their relationship with Mean, Median, and Mode
- Covariance
- Pearson and Spearman Rank Correlation
- Understanding P-value
- Confidence intervals explained
- Performing hypothesis testing with confidence intervals and Z-test
- Hypothesis testing part 2
- Hypothesis testing part 3
- Finalizing statistics concepts
- Data profiling techniques
- Statistical analysis methods
- Univariate, bivariate, and multivariate analysis
- Performing EDA using automated libraries
- Analyzing bike-sharing trends
- Sentiment analysis of movie reviews
- Customer segmentation and cross-selling strategies
- Analyzing wine types and their quality
- Analyzing music trends and recommendations
- Forecasting stock and commodity prices
- Introduction to Linear Algebra
- Understanding 1D, 2D, 3D, 4D, and 5D tensors
- Exploring vectors in linear algebra
- Defining and understanding vectors
- Vector examples in machine learning
- Row and column vectors
- Distance from the origin in vector space
- Euclidean distance and its application
- Vector addition and subtraction operations
- Dot product of vectors
- Equation of a hyperplane in vector space
- Introduction to matrices
- Different types of matrices
- Matrix addition, subtraction, and multiplication
- Transposing matrices
- Concept of linear transformations
- Linear transformation in 3D space
- Matrix multiplication as a composition of transformations
- Introduction to differentiation
- Derivative of a constant function
- Power rule for differentiation
- Sum rule in differentiation
- Product rule in differentiation
- Quotient rule in differentiation
- Chain rule for derivatives
- Understanding partial differentiation
- Higher-order derivatives
- Matrix differentiation and its applications
- Introduction to machine learning fundamentals
- Types of machine learning: Supervised, Unsupervised, Semi-supervised, Reinforcement learning
- Key differences between Supervised, Unsupervised, and Semi-supervised learning
- Linear regression: Mathematical intuition
- Assumptions of linear regression
- Multiple linear regression
- Deep dive into Ordinary Least Squares (OLS)
- OLS vs Gradient Descent
- Training methodologies in machine learning
- Splitting data: Train, Test, and Validation sets
- Hands-on linear regression in Python from scratch
- Implementing linear regression with scikit-learn
- Understanding bias and variance trade-off
- Intuitive understanding of overfitting and underfitting
- Ridge regression and its applications
- Lasso regression explained
- Elastic Net regression overview
- Polynomial regression
- Regression metrics: R² score, Adjusted R², MAE, MSE, RMSE
- Logistic regression: Introduction and use cases
- Linear regression vs logistic regression
- Performance metrics for classification: Confusion matrix, Precision, Recall, ROC, AUC
- F-beta score and its relevance in model evaluation
- Implementing Gradient Descent from scratch
- Support Vector Regressor (SVR) explained
- Support Vector Classifier (SVC) and its use cases
- Understanding Support Vector Machines (SVM) and their applications
- K-Nearest Neighbors (KNN) Classifier: Basics and implementation
- KNN Regressor: Key concepts and differences from KNN Classifier
- K-Nearest Neighbor algorithm overview
- Lazy learning in machine learning
- Common issues faced with KNN
- Performance measurement of KNN models
- Batch vs Mini-Batch Gradient Descent: Key differences
- Decision Tree Classifier: Introduction and implementation
- Decision Tree Regressor: How it differs from the classifier
- Understanding Cross-Validation and its importance
- Bias vs Variance: A deeper look at the trade-off
- Ensemble Learning: Combining multiple models for better performance
- Bagging: Concepts and how it improves models
- Boosting: Techniques and their advantages
- Stacking: Combining predictions from multiple models
- Random Forest: Understanding the ensemble of decision trees
- Challenges in ML: Overcoming common obstacles
- Data Collection Challenges: Strategies for better data
- Insufficient/Labelled Data: Tackling the problem
- Non-representative Data: Ensuring fairness and accuracy
- Poor Quality Data: Techniques for data cleaning
- Irrelevant Features: Feature selection methods
- Offline Learning: Addressing limitations in real-time systems
- Cost Function Selection: Optimizing for the best outcome
- Planning a Data Science Project: A step-by-step approach
- Machine Learning Development Life-cycle: Key phases explained
- Data Leakage: Prevention and detection strategies
- Cross Validation: Enhancing model reliability
- Data Drift: Identifying and adapting to changes
- Hyperparameter Optimization: Techniques for better performance
- Introduction to Clustering Techniques
- K-Means Clustering: Hard vs Soft
- Visualizing K-Means Clustering Steps
- Choosing the Optimal K Value
- Pros and Cons of K-Means Clustering
- K-Means Failures and How to Address Them
- Evaluating Clustering Algorithms
- Silhouette Coefficient for Clustering Evaluation
- Dunn's Index: A Method for Clustering Quality
- Implementing K-Means in Python with Real Data
- Real-time Applications of Clustering
- Hierarchical Clustering: A Visual Walkthrough
- Using Hierarchical Clustering and Dendrogram Interpretation
- Python Implementation of Agglomerative Clustering
- DBSCAN: A Density-Based Clustering Approach
- DBSCAN for Outlier Detection in Clustering
- Python Implementation of DBSCAN
- Principal Component Analysis (PCA) and its Importance
- Understanding the Curse of Dimensionality
- How PCA Helps in Dimensionality Reduction
- Applications of PCA
- PCA Algorithm Steps
- Eigenvalues and Eigenvectors in PCA
- Interpreting Principal Components
- Curse of Dimensionality: Challenges in High-Dimensional Data
- Overcoming the Curse of Dimensionality in Machine Learning Models
- Cloud Deployment of Machine Learning Projects
- Deploying on CloudFoundry, AWS, Azure, and Google Cloud
- Exposing ML APIs to Web and Mobile Apps
- Retraining Machine Learning Models with New Data
- Setting Up DevOps Infrastructure for ML Projects
- Discussing Infrastructure Costs and Data Volumes
- Prediction from Streaming Data Sources
- Web Crawlers for Image Data and Sentiment Analysis
- Product Review Sentiment Analysis Using Web Crawlers
- Integration with Web Portal and MongoDB on Azure
- REST API Integration with Web Portal
- Deployment on Azure Web Portal
- Text Mining Techniques for Data Analysis
- Social Media Data Analysis for Churn Prediction