ML & AI Kickstart - Get SDE Ready

About Course

Welcome to the ML & AI Kickstart course, your gateway to mastering two of the most transformative technologies shaping the future. Whether you’re an aspiring data scientist, software developer, or AI enthusiast, this course is meticulously crafted to guide you from the foundational principles to advanced applications of machine learning and artificial intelligence.

Our curriculum offers an engaging blend of theory and hands-on practice, empowering you to build intelligent systems capable of learning, adapting, and making data-driven decisions. With expert-led instruction and interactive modules, you’ll explore key concepts such as supervised and unsupervised learning, neural networks, deep learning, and natural language processing.

Through real-world projects and case studies, you’ll gain practical experience that mirrors industry challenges. From building predictive models to creating AI-powered tools, this course equips you with the skills to innovate and excel in a field brimming with opportunities.

By the end of this journey, you’ll not only possess a deep understanding of machine learning and AI but also the confidence to apply these skills in diverse domains such as healthcare, finance, e-commerce, and more. Join us today and embark on a transformative learning experience that will redefine your career path in the age of AI.

Master the Fundamentals of AI and Machine Learning: Gain a deep understanding of supervised, unsupervised, and reinforcement learning with practical applications.
Build and Optimize Machine Learning Models: Learn to design, train, and fine-tune models to tackle real-world challenges effectively.
Harness Neural Networks and Deep Learning: Explore advanced techniques to process complex data and develop intelligent AI systems.
Dive Into Natural Language Processing (NLP): Create chatbots, text analysis tools, and language translation models for diverse use cases.
Gain Hands-On Experience With Industry Projects: Develop practical skills through projects like recommendation systems, predictive analytics, and image recognition.
Explore AI in Emerging Technologies: Understand how AI integrates with IoT, robotics, and edge computing to shape the future.
Learn Ethical and Scalable AI Practices: Deploy AI solutions responsibly, ensuring scalability and ethical compliance in production environments.
Kickstart a High-Growth Career: Enter the booming AI and Machine Learning industry with confidence and a portfolio to showcase your expertise.

Course Content

Introduction to the ML & AI Kickstart
A brief introduction to what's coming ahead, and how you can make the most out of it. In this module, we answer some very basic yet crucial questions to lay the foundations of the journey we are going to begin. Excited to have you onboard!!!

What is Machine Learning?

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How exactly does Machine Learning work?

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The Python Programming Language
Before starting with ML, let's first have a look at the programming language we'll be using. In this module, we'll dive into Python programming essentials tailored for Machine Learning. Expect to cover basic syntax, essential data structures, functions and modules. By the end, you'll be equipped with the foundational skills necessary to start programming in Python.

Numpy

Pandas
Pandas is a widely used Python library for data manipulation and analysis. It provides high-level data structures and functions designed to make working with structured data fast, easy, and expressive. This note serves as an introduction to Pandas, covering its key features, data structures, and common operations.

Data Visualisation
Data visualization is the graphical representation of data and information. It plays a crucial role in machine learning (ML) and data science by aiding in the understanding, interpretation, and communication of complex datasets. The primary objectives of data visualization in these fields are to explore, analyze, and present insights from data to facilitate informed decision-making.

Probability Distribution and Statistics
Probability distribution and statistics are fundamental concepts in the field of mathematics and are extensively used in various disciplines, including but not limited to, economics, engineering, social sciences, and natural sciences.

Linear Regression
Linear regression is a fundamental statistical method used in machine learning to model the relationship between a dependent variable and one or more independent variables. This technique aims to find the best-fit line that minimizes the difference between the observed data points and the predicted values. Linear regression is widely used for predictive analysis, trend forecasting, and determining the strength of predictors. In this lesson, we will explore the basics of linear regression, including its assumptions, the least squares method for fitting a model, and evaluating model performance using key metrics. Understanding linear regression is essential as it forms the basis for more complex regression techniques and serves as a foundational tool in data analysis and machine learning.

Data Preprocessing
Data preprocessing is an important step in the data mining process. It refers to the cleaning, transforming, and integrating of data in order to make it ready for analysis. The goal of data preprocessing is to improve the quality of the data and to make it more suitable for the specific data mining task.

Decision Trees and Random Forests
Decision trees are intuitive and powerful tools for both classification and regression tasks in machine learning. They work by splitting the data into subsets based on feature values, creating a tree-like model of decisions. Each node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome. This method is highly interpretable and easy to visualize, making it popular for understanding complex data. Random forests, on the other hand, are an ensemble learning method that enhances the predictive performance of decision trees. By constructing a multitude of decision trees during training and outputting the mode or mean prediction of the individual trees, random forests reduce the risk of overfitting and improve accuracy. This technique leverages the power of multiple trees to achieve robust and reliable predictions. In this module, we will explore the construction, advantages, and practical applications of both decision trees and random forests, highlighting their role in solving real-world machine learning problems.

Gradient Boost Models
In this module, we take a look at gradient boosting models and fine-tuning model hyperparameters using Optuna, GridSearch CV and RandomSearchCV.

Hyperparameter Tuning

Introduction to Deep Learning
Deep learning is a subset of machine learning based on artificial neural networks, inspired by the structure and function of the brain. It focuses on training models, known as deep neural networks, which consist of multiple layers of interconnected nodes or neurons, to automatically learn hierarchical representations of data.

Neural Networks
Neural Networks are computational models that mimic the complex functions of the human brain. The neural networks consist of interconnected nodes or neurons that process and learn from data, enabling tasks such as pattern recognition and decision making in machine learning. The article explores more about neural networks, their working, architecture and more.

Classification Measures
Classification is a fundamental task in machine learning (ML), involving the categorization of data into predefined classes or categories based on input features. Accurately evaluating the performance of classification models is essential for assessing their effectiveness in real world applications. This module provides a detailed examination of various classification measures used in ML.

Project Delivery

Deployment

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About Course

About Course

What Will You Learn?

Course Content

Introduction to the ML & AI Kickstart A brief introduction to what's coming ahead, and how you can make the most out of it. In this module, we answer some very basic yet crucial questions to lay the foundations of the journey we are going to begin. Excited to have you onboard!!!

What is Machine Learning?

How exactly does Machine Learning work?

Introduction to Python

Basics of Python: Arithmetic and Variables

Built-In Data Structures in Python: Integers and Booleans

Built-In Data Structures in Python: Lists and Tuples

Built-In Data Structures in Python: Strings and Dictionaries

Conditional Statements and Loops in Python

Numpy

NumPy

Working with Pandas

Data Viz with Matplotlib

Probability Distribution and Statistics Probability distribution and statistics are fundamental concepts in the field of mathematics and are extensively used in various disciplines, including but not limited to, economics, engineering, social sciences, and natural sciences.

Types of Probability Distributions

Descriptive Statistics

What is Linear Regression?

The Best Fit Line

Cost function for Linear Regression

Gradient Descent for Linear Regression

Assumptions in Linear Regression

Data Cleaning

Data Transformation

Data Reduction

Introduction to Decision Trees

Decision Tree Algorithms

The Concept of Entropy and Information Gain

Construction of Decision Trees

Random Forests

Gradient Boost Models In this module, we take a look at gradient boosting models and fine-tuning model hyperparameters using Optuna, GridSearch CV and RandomSearchCV.

Gradient Boost Models – Introduction

Types – Gradient Boosting Machine (GBM)

Types – XGBoost (Extreme Gradient Boosting)

Types – LightGBM

Types – CatBoost

Hyperparameter Tuning

Fine-Tuning Model Hyperparameters

What is Deep Learning

ML vs. DL

Evolution of Neural Networks

What are Neural Networks

How do Neural Networks work?

Learning Methods of Neural Networks

Advantages and Disadvantages of Working with Neural Networks

Classification Measures

Project Delivery

Project Delivery

Deployment

Deployment

Student Ratings & Reviews

Quick Links

Quick Links

Social Media

Introduction to the ML & AI Kickstart
A brief introduction to what's coming ahead, and how you can make the most out of it. In this module, we answer some very basic yet crucial questions to lay the foundations of the journey we are going to begin. Excited to have you onboard!!!

Probability Distribution and Statistics
Probability distribution and statistics are fundamental concepts in the field of mathematics and are extensively used in various disciplines, including but not limited to, economics, engineering, social sciences, and natural sciences.

Gradient Boost Models
In this module, we take a look at gradient boosting models and fine-tuning model hyperparameters using Optuna, GridSearch CV and RandomSearchCV.