Mastering Machine Learning 2025

This 4-month comprehensive course will immerse you in the core concepts and advanced techniques of Machine Learning, including supervised, unsupervised, and reinforcement learning. You'll explore the latest in machine learning algorithms, tools, and frameworks, gaining hands-on experience in building and deploying models for real-world applications such as predictive analytics, computer vision, and NLP.

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Duration4 months

Lessons250

Levelintermediate

ModeLive Online

What you will learn

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

Corporate training outcomes

Completion certificate
Practical assignments and project work
Mentor support and progress tracking
Custom batch options for teams
Yes

Requirements

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

Curriculum

Course Introduction

Introduction of Data science, AI, ML, DL and its application in Day to Day life
Course overview and Dashboard complete walkthrough

Installation and setup of the required software

Installation and setup of Anaconda Distribution and Jupyter Notebook using Neurolab

Introduction of Python

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

String

What is a string?
Creating a string
Different ways of accessing strings
Operators that work on strings
Built-in string functions
Printing strings using f-string
Modifying strings
String conversion methods
String comparison methods
String searching methods
String replace methods

List

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

Tuples

What is a tuple and how to create a tuple?
Differences between lists and tuples
Benefits of tuples
Packing and unpacking a tuple
Accessing a tuple
Changing the tuple
Deleting the tuple
Functions used with tuple
Methods used with tuple
Operations allowed on tuple

Dictionaries and set

What is a dictionary?
What is a key-value pair?
Creating a dictionary
Important characteristics of a dictionary
Different ways to access a dictionary
Updating elements in a dictionary
Removing elements from a dictionary
Functions used in dictionary
Dictionary methods
Set introduction
Set methods

Decision Control Statements and Loops in Python

if statement
Concept of indentation
if-else statement
if-elif-else statement
Types of loops supported by Python
While loop
while-else loop
break, continue, and pass statement
For loop
For loop in Python
Differences with other languages
range() function
Using for with range()

Python Functions

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

OOPS Concepts

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

Exception Handling

Introduction to exception handling
Exception handling keywords
Exception handling syntax
Handling multiple exceptions
Handling all exceptions

Python logging

What is logging?
When to use logging?
Logging to a file
Different levels of logging
Logging from multiple modules
Logging variable data
Display date & time in logging file

Working With Files

Working with files
Reading and writing files
Buffered read and write
Other file methods

Decorators and Namespaces

Namespaces
Scope and LEGB rule
Decorators with examples

Iterator and Generator

Iterators, iterables
Concept of generator
What is a generator
Yield vs return
Benefits of generator

Database

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

API

Introduction to Flask
Flask variable rules and conventions
Flask templates and handling static files
App routing in Flask
URL building in Flask
HTTP methods in Flask
Flask request object
Sending form data to templates in Flask

GIT and GitHub

What is Git?
What is VCS/SCM?
Why Git/VCS is essential?
Types of version control systems (VCS)
How Git works?
Installing Git
Creating and cloning repositories in Git
Using add, commit, add ., and gitignore in Git

Python Pandas Modules

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

Python Numpy Modules

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

Python Visualization Modules

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

Statistics

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

Exploratory Data Analysis

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

Feature Engineering

Imputing missing values
Outlier detection and removal techniques
Feature scaling methods
Feature encoding strategies
Handling imbalanced data
Using power transformers for data normalization
Applying Box-Cox transformation for data preprocessing

Linear Algebra

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

Differential Calculus

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

Machine Learning Module 1

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

Machine Learning Module 2

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

Machine Learning Module 3

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

Machine Learning Module 4

Stochastic Gradient Descent: Concepts and advantages
AdaBoosting: Improving weak learners
Gradient Boosting: Key principles and applications
XGBoosting: Why it outperforms other boosting methods
Hands-on XGBoost: Implementing it in Python

Machine Learning Module 5

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

Unsupervised Machine Learning

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

Dimension Reduction Techniques

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

Natural Language Processing

Text Analytics Overview
Tokenizing and Chunking Techniques
Creating a Document-Term Matrix
Understanding TF-IDF (Term Frequency-Inverse Document Frequency)
Sentiment Analysis: Hands-On Approach
Implementing Naive Bayes Classifier for Text Classification
Applications of Naive Bayes in Text Analytics

Deep Learning

Introduction to Deep Learning
Understanding Neural Network Architecture
Exploring Loss and Cost Functions
Optimizers and Their Role in Deep Learning
Convolutional Neural Network (CNN) Architecture
Building Your First Classifier with CNN
Deploying a Deep Learning Classifier on the Cloud

Time series

ARIMA Model Overview
SARIMA Model and Its Applications
Auto ARIMA for Time Series Forecasting
Predicting NIFTY Stock Price Using ARIMA Models

Machine Learning Deployment

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

Machine Learning Extra Sessions

Project Explanation in Interviews
Roles and Responsibilities of a Data Scientist
A Data Scientist's Day-to-Day Tasks
Companies Hiring Data Scientists
Resume Review and Discussion with Our Team

Python projects

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

Machine Learning Projects

Healthcare Analytics: Predicting Medication Requirements Based on Fitbit Data
Predicting Inventory Order Cancellations
Anomaly Detection in Packaged Material Inventory
Forecasting Food Prices Using Zomato Data
Fault Detection in Semiconductor Wafers Using Sensor Data

Deep Learning projects

Customer Feedback analysis using RNN LSTM
Family member detection
Industry financial growth prediction.
Speech recognization based attendance system