Essentials of Machine Learning and Artificial Intelligence

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About Course

About Course

Welcome to the Essentials of Machine Learning and Artificial Intelligence 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.

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What Will You Learn?

  • 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 Course
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?
    00:00
  • How exactly does Machine Learning work?
    00:00
  • Busting Myths about Machine Learning and Artificial Intelligence
    00:00
  • Understanding the Relationship Between Data Science, Machine Learning, and AI
    00:00
  • Navigating your Journey: What to Expect from this Course
    00:00

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.

Introduction to Git and Github
In the world of software development, version control is essential for managing changes to source code over time. Git and GitHub are powerful tools that facilitate version control and collaboration among developers. This module introduces you to the basics of Git and GitHub, highlighting their significance and functionality.

Data Acquisition
Data acquisition is a critical step in the data science pipeline. It involves gathering data from various sources to use in analysis, model training, and decision-making processes. The quality and quantity of data collected significantly impact the insights and outcomes of any data science project. This module will cover the key aspects of data acquisition, including types of data sources, methods of data collection, and best practices to ensure data quality and relevance.

Web Automation using Selenium
Selenium is a powerful tool commonly used for web automation, allowing developers to interact with web pages programmatically. In this module we'll be discussing Selenium for web automation, covering its features, benefits, and common use cases.

Databases
Data is the new fuel of this world but data is unorganized information, so to organize that data we make a database. A database is the organized collection of structured data usually controlled by a database management system (DBMS). Databases help us easily store, access, and manipulate data held on a computer.

Getting Started with Machine Learning
Machine Learning (ML) is a branch of artificial intelligence (AI) concerned with the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. ML algorithms are designed to learn patterns and relationships from data, allowing them to make predictions or decisions based on new or unseen data. The core objective of machine learning is to develop systems that can automatically improve their performance over time as they are exposed to more data, thereby enabling them to adapt to new situations and make informed decisions.

Numpy

Linear Algebra for ML
Linear algebra is essential for understanding and creating machine learning algorithms, especially neural networks and deep learning models. It is the mathematical foundation that solves the problem of representing data as well as computations in machine learning models. Linear algebra enables the representation of data points as vectors and allows for efficient computation of operations on these vectors. Linear algebra is used in machine learning algorithms such as neural networks, support vector machines, image processing, and principal component analysis

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.

Scikit – Learn
This module introduces you to Scikit Learn - one of the most important and widely used libraries by data scientists all over the world. Scikit-Learn, also known as sklearn, is an open-source machine learning library for the Python programming language. It provides simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and matplotlib.

Optimisation Algorithms

Locally Weighted Scatterplot Smoothing

Maximum Likelihood Estimation
Maximum Likelihood Estimation (MLE) is a widely used statistical method for estimating the parameters of a statistical model. It is based on the principle of maximizing the likelihood function, which measures the probability of observing the given data under the assumed model.

Logistic Regression
Logistic regression is a supervised machine learning algorithm used for classification tasks where the goal is to predict the probability that an instance belongs to a given class or not. Logistic regression is a statistical algorithm which analyzes the relationship between two data factors. The module explores the fundamentals of logistic regression, its types and implementations.

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.

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.

Principal Component Analysis (PCA)
As the dimensionality of a dataset increases, the requisite volume of data needed to achieve statistically significant results escalates exponentially, giving rise to challenges such as overfitting, prolonged computational time, and diminished accuracy of machine learning models. This phenomenon, known as the curse of dimensionality, poses significant issues when dealing with high-dimensional data.

Project 1: Implementing Regression Techniques

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.

K-Nearest Neighbors (KNN)
K-Nearest Neighbors is a simple yet powerful supervised machine learning algorithm used for classification and regression tasks. It's considered a non-parametric and instance-based learning algorithm, meaning it makes predictions based on the similarity of input data points.

Support Vector Machines
Support Vector Machine (SVM) is a powerful machine learning algorithm used for linear or nonlinear classification, regression, and even outlier detection tasks. SVMs can be used for a variety of tasks, such as text classification, image classification, spam detection, handwriting identification, gene expression analysis, face detection, and anomaly detection. SVMs are adaptable and efficient in a variety of applications because they can manage high-dimensional data and nonlinear relationships.

Clustering Fundamentals
Clustering in machine learning is a form of unsupervised learning where the goal is to partition a dataset into groups, or clusters, based on the similarity of data points within each group.

Natural Language Processing
Natural language processing (NLP) is a field of computer science and a subfield of artificial intelligence that aims to make computers understand human language. You may have used some of these applications yourself, such as voice-operated GPS systems, digital assistants, speech-to-text software, and customer service bots. Now, it's time to see how these applications work behind the scenes.

Naive Bayes Classifier
Naive Bayes is a family of simple yet powerful probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Despite its simplicity, Naive Bayes performs remarkably well in many real-world situations and is particularly popular for text classification problems like spam detection, sentiment analysis, and more.

Bonus Module 1 – Gradient Boost Models and more
In this module, we take a look at gradient boosting models and fine-tuning model hyperparameters using Optuna, GridSearch CV and RandomSearchCV.

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.

TensorFlow

Keras

PyTorch

Introduction to Image Processing

Project 2: Facial Recognition System

Convolutional Neural Networks
A Convolutional Neural Network (CNN) is a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks.

Training Data Loaders, Data Augmentation, and Google Colab
In machine learning, especially in computer vision tasks, preparing the data is as crucial as choosing the model architecture. This involves creating data loaders to efficiently handle and preprocess the data, applying data augmentation techniques to enhance the training dataset, and utilizing platforms like Google Colab to streamline the training process.

Transfer Learning
We, humans, are very good at applying the transfer of knowledge between tasks. This means that whenever we encounter a new problem or a task. Similarly Transfer learning is a smart method in machine learning where a model uses knowledge from one task to help with a different, but related, task.

Back to NLP: Markov Chains
Markov chains are a mathematical system that undergoes transitions from one state to another on a state space. They are a powerful tool for modeling sequential data and can be applied to various fields, including text generation. This module will explore how Markov chains can be used to generate text, providing both a theoretical foundation and practical implementation.

Recurrent Neural Networks
Recurrent Neural Network (RNN) is a type of Neural Network where the output from the previous step is fed as input to the current step. In traditional neural networks, all the inputs and outputs are independent of each other.

Long Short-Term Memory Networks
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to better capture long-term dependencies in sequential data. LSTMs address the vanishing gradient problem that plagues standard RNNs, making them more effective at learning patterns over extended sequences.

Gated Recurrent Units (GRUs)
Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture introduced to address the vanishing gradient problem inherent in standard RNNs. They are designed to capture long-term dependencies more efficiently than traditional RNNs while being computationally lighter and simpler than Long Short-Term Memory (LSTM) networks.

Word Embeddings
Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space.

Contextual Embeddings

Bonus Module 2 – The Art of Ensembling Models
Ensembling models refers to the technique of combining multiple machine learning models to improve overall performance. The idea is to leverage the strengths of different models to achieve better predictive accuracy and robustness

Reinforcement Learning
Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

Generative Models

Introduction to LLMs

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