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Machine Learning Course

Embark on a transformative journey into the world of artificial intelligence with our comprehensive Machine Learning course. Whether you're a novice or seasoned professional, our expert-led program offers extensive training in machine learning, a cutting-edge field revolutionizing industries worldwide. Join us to unlock the full potential of machine learning and carve your path in the era of intelligent technology.

|| What will I learn?

Understand the fundamental principles of machine learning and its applications.
Implement machine learning algorithms using programming languages such as Python.
Perform data preprocessing and feature engineering.

Evaluate and validate machine learning models using appropriate metrics.
Apply supervised and unsupervised learning techniques to solve various problems.
Address practical issues such as overfitting, underfitting, and model selection.

|| What will I learn?

Understand the fundamental principles of machine learning and its applications.
Implement machine learning algorithms using programming languages such as Python.
Perform data preprocessing and feature engineering.
Evaluate and validate machine learning models using appropriate metrics.
Apply supervised and unsupervised learning techniques to solve various problems.
Address practical issues such as overfitting, underfitting, and model selection.

|| Requirements

Basic programming knowledge (preferably in Python)
Understanding of fundamental statistical concepts

Basic knowledge of linear algebra and calculus

|| Requirements

Basic programming knowledge (preferably in Python)
Understanding of fundamental statistical concepts
Basic knowledge of linear algebra and calculus

Python Basic Building
Python Keywords and identifiers
Comments, indentation, statements
Variables and data types in Python
Standard Input and Output

Operators
Control flow: if else elif
Control flow: while loop
Control flow: for loop
Control flow: break & continue
Python Data Structures

Strings
Lists, Lists comprehension
Tuples, Sets
Dictionary, Dictionary Comprehension

Python Functions
Python Builtin Functions.
Python Userdefined Functions.
Python Recursion Functions.
Python Lambda Functions.
Python Exception Handling,
Logging And Debugging

Exception Handling
Custom Exception Handling
Logging With Python
Debugging With Python
Python OOPS
Python Objects And Classes
Python Constructors
Python Inheritance
Abstraction In Python
Polymorphism in Python
Encapsulation in Python

File Handling
Create
Read
Write
Append

Introduction to NumPy
NumPy Array
Creating NumPy Array
Array Attributes,
Array Methods
Array Indexing,
Slicing Arrays
Array Operation
Iteration through Arrays

Introduction to Pandas
Pandas Series
Creating Pandas Series
Accessing Series Elements
Filtering a Series
Arithmetic Operations
Series Ranking and Sorting
Checking Null Values
Concatenate a Series

Data Frame Manipulation
Pandas Dataframe
Introduction Dataframe Creation
Reading Data from Various Files
Understanding Data
Accessing Data Frame Elements using Indexing
Dataframe Sorting
Ranking in Dataframe
Dataframe Concatenation
Dataframe Joins
Dataframe Merge
Reshaping Dataframe
Pivot Tables,
Cross Tables
Dataframe Operations

Checking Duplicates
Dropping Rows and Columns
Replacing Values
Grouping Dataframe
Missing Value Analysis & Treatment
Visualization using Matplotlib
Plot Styles & Settings
Line Plot,
Multiline Plot
Matplotlib Subplots
Histogram, Boxplot
Pie Chart ,Scatter Plot
Visualization using Seaborn
Strip Plot ,Distribution Plot
Joint Plot,
Violin Plot,
Swarm Plot
Pair Plot,
Count Plot
Heatmap
Visualization using Plotly
Boxplot
Bubble Chart
Violin Plot
3D Visualization

EDA and Feature Engineering
Introduction of EDA
Dataframe Analysis using Groupby
Advanced Data Explorations

Working with SQL Using MySQL
Work Bench / SQL Server"
USE, DESCRIBE,
SHOW TABLES
SELECT, INSERT
UPDATE & DELETE
CREATE TABLE
ALTER: ADD, MODIFY, DROP
DROP TABLE, TRUNCATE, DELETE
LIMIT, OFFSET
ORDER BY
DISTINCT
WHERE Clause
HAVING Clause
Logical Operators
Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
GROUP BY
SQL Primary And Foreign Key
Join and Natural Join
Inner, Left, Right and Outer joins

Advance SQL
Subqueries/Nested Queries/Inner Queries
SQL Function And Stored Procedures
SQL Window Function
CTE In SQL
Normalization In SQL

Basic Math
Linear Algebra
Probability
Calculus
Develop a comprehensive understanding of coordinate geometry and linear algebra.
Build a strong foundation in calculus, including limits, derivatives, and integrals.

Descriptive Statistics
Sampling Techniques
Measure of Central Tendency
Measure of Dispersion
Skewness and Kurtosis
Random Variables
Bassells Correction Method
Percentiles and Quartiles
Five Number Summary
Gaussian Distribution
Lognormal Distribution
Binomial Distribution
Bernoulli Distribution

Inferential Statistics
Standard Normal Distribution
ZTest
TTest
ChiSquare Test
ANOVA / FTest
Introduction to Hypothesis Testing
Null Hypothesis
Alternet Hypothesis

Probability Theory
What is Probability?
Events and Types of Events
Sets in Probability
Probability Basics using Python
Conditional Probability
Expectation and Variance

Introduction to Machine Learning
Machine Learning Modelling Flow
"Supervised and Unsupervised
Types of Machine Learning Algorithms

Linear Regression using OLS
Introduction of Linear Regression
Types of Linear Regression
OLS Model
Math behind Linear Regression
Decomposition Variability
Metrics to Evaluate Model
Feature Scaling
Feature Selection
Regularisation Techniques
Ridge Regression
Lasso Regression
ElastivNet Regression

Optimisation Techniques
What is Optimisation?
Gradient Descent
Adagrad Algorithm
Adam Algorithm
Linear Regression with SGD
Prerequisites

Introduction to Stochastic Gradient Descent (SGD)
Preparation for SGD
Workflow of SGD
Implementation of SGD on Linear Regression

Logistic Regression
Maximum Likelihood Estimation
"Logistic Regression Using Sigmoid
Activation Function"
Performance Metrics
Confusion Matrix
Precision, Recall, F1Score
Receiver Operating Characteristic Curve

KNN
Euclidean Distance
Manhattan Distance
Implementation for KNN

SVM
Support Vector Regression
Support Vector Classification
Polynomial Kernel
Cost Function
GridSerchCV

Decision Trees
Decision Tree for Classification
Decision Tree for Regression
ID3 Algorithm
CART Algorithm
Entropy
Gini Index
Information Gain
Decision Tree: Regression
Mean Square Error
PrePruning and PostPruning

Naive Bayes
Introduction to Bayes Theorem
Explanation for naive bayes

Ensemble Technique
Bagging
Random Forest Classifier
Random Forest Regression
Random Forest – Why & How?
Feature Importance
Advantages & Disadvantages

Boosting
Bootstrap Aggregating
AdaBoost
XgBoost
Project For Random Forest
Project Penguin Classification
Project Texi Prediction

Kmeans Clustering
Prerequisites
Cluster Analysis
Kmeans
Implementation of Kmeans
Pros and Cons of Kmeans
Application of Kmeans
Elbow Method
Model building for Kmeans Clustering

Hierarchical Clustering
Types of Hierarchical Clustering
Dendrogram
Pros and Cons of Hierarchical Clustering
Model building for Hierarchical Clustering

DBSCAN Clustering
Introduction for DBSCAN Clustering
implementation of DBSCAN

Principal Components Analysis
Prerequisites
Introduction to PCA
Principal Component
Implementation of PCA
Case study
Applications of PCA
Project on PCA

Time Series Modelling
Understand Time Series Data
Visualising Time Series Components
Exponential Smoothing
ARIMA
SARIMA
SARIMAX
Project on Forecasting
Cloud Basics
ML on Cloud

Introduction of MLOps
What and why MLOps
MLOps fundamentals
MLOps vs DevOps
Why DevOps is not sufficient for MLOps
Challenges in traditional ML Pipeline
DevOps and MLOps tools and platform
What is SDLC?
Types of SDLC
Waterfall vs AGILE vs DevOps vs MLOps

MLOps Foundation
Fundamental of Linux for MLOps and data scientist
Important Linux Commands
Source code managements using GIT
GIT configuration and GIT commands
YAML for Configuration Writing
YAML vs JSON Schema
Docker for Containers
Docker Basic Command, Dockerhub, Dockerfile
Cloud Computing and Cloud Infrastructure
Cloud Service Provider- AWS, GCP, AZURE
Data Managements and Versioning with DVC
Monitoring, Alerting, Retraining With Grafana and
prometheus
Experiment tracking with MLFLOW
Model Serving With BENTOML

End to End project implementation with Deployment implementation with Deployment
Understanding Machine learning Workflow and Project Setup
Project Template Setup with GitHub
Modular workflow Introduction and Implementation
Understanding the Training Pipeline and Its Components

Data Ingestion, Data Transformation Model Trainer Model
Evaluation
Creating Prediction Pipeline and End Point Creation
Continues Integration, Continues Delivery and Continues
Training understanding and Project Deployment

Prompt Engineering
Why Prompt Engineering?
ChatGPT
Few Standard Definitions:
Label
Logic
Model Parameters (LLM Parameters)
Basic Prompts and Prompt Formatting
Elements of a Prompt:
Context
Task Specification
Constraints
General Tips for Designing Prompts:
Be Specific
Keep it Concise
Be Contextually Aware
Test and Iterate
Prompt Engineering Use Cases
Information Extraction
Text Summarization
Question Answering
Code Generation
Text Classification
Prompt Engineering Techniques
N-shot Prompting
Zero-shot Prompting
Chain-of-Thought (CoT) Prompting
Generated Knowledge Prompting

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|| Frequently asked question

A Machine Learning Course teaches participants the fundamental concepts, techniques, and applications of machine learning, a subset of artificial intelligence focused on building algorithms that can learn from data and make predictions or decisions without explicit programming.

This course is suitable for individuals interested in pursuing a career in machine learning, data science, or related fields. It caters to beginners with little to no background in machine learning as well as professionals looking to expand their knowledge and skills in this area.

Prerequisites may include a basic understanding of mathematics (e.g., calculus, linear algebra, probability) and programming (e.g., Python, R). Some courses may also require familiarity with data analysis concepts and tools.

Most reputable Machine Learning courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

Some courses may offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities. However, this varies depending on the course provider.

Yes, many Machine Learning courses are available online, offering flexibility in terms of timing and location. Online courses often include video lectures, interactive coding exercises, and discussion forums to facilitate learning.

Yes, Machine Learning courses typically include hands-on projects and case studies to apply the techniques learned in real-world scenarios. This practical experience is crucial for developing proficiency and building a portfolio to showcase to potential employers.

Some course providers offer financial aid or scholarships based on merit, financial need, or specific criteria. It's advisable to inquire with the course provider or check their website for information on available assistance programs.

After completing the course, you may continue to have access to resources such as course materials, coding exercises, alumni networks, coding communities, and additional learning resources. Some providers offer lifetime access to course materials or alumni benefits to support your continued growth and success.

Related courses

Full Stack Data Science Course

Data Structures & Algorithms Course

Data Science using Python Course

Deep Learning Course

Artificial Intelligence Course

Computer Vision Course

Generative AI Course

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