Machine Learning – Saarthilms

About Course

Machine learning is a branch of artificial intelligence that enables systems to learn from data and improve performance over time without being explicitly programmed. It relies on algorithms that identify patterns, make predictions, and adapt based on new information. There are three main types of machine learning: supervised learning, which uses labeled data to train models; unsupervised learning, which identifies hidden patterns in unlabeled data; and reinforcement learning, where agents learn by interacting with an environment to maximize rewards. Applications of machine learning span across industries, including healthcare for disease prediction, finance for fraud detection, and e-commerce for recommendation systems. Techniques such as neural networks, decision trees, and support vector machines power these advancements. Machine learning also plays a key role in emerging technologies like self-driving cars and natural language processing. However, challenges like data privacy, bias, and interpretability remain critical. Despite these challenges, machine learning continues to shape the future of technology and innovation.

In a machine learning course, you will learn the following key concepts and skills:
Foundations of Machine Learning: Understand the basic principles behind machine learning, including different types of learning (supervised, unsupervised, and reinforcement learning).
Data Preprocessing: Learn how to clean, prepare, and transform raw data into a usable format for training models.
Supervised Learning: Study algorithms like linear regression, logistic regression, decision trees, and support vector machines, which are used for classification and regression tasks.
Unsupervised Learning: Explore clustering algorithms such as k-means and hierarchical clustering, and dimensionality reduction techniques like PCA (Principal Component Analysis).
Neural Networks and Deep Learning: Dive into deep learning, understanding the architecture of neural networks, backpropagation, and frameworks like TensorFlow and PyTorch.
Model Evaluation and Selection: Learn how to evaluate model performance using metrics like accuracy, precision, recall, and F1-score, and how to choose the right model for different tasks.
Optimization Techniques: Study optimization methods like gradient descent to improve model training and performance.
Real-world Applications: Learn how machine learning is applied in fields like healthcare, finance, e-commerce, natural language processing, and computer vision.
Ethics and Bias in AI: Understand the ethical implications of machine learning, including challenges like bias, fairness, and transparency in AI systems.
Hands-on Projects: Apply what you've learned by working on practical projects, building machine learning models, and solving real-world problems.

Course Content

Module 1: Introduction to Machine Learning

Overview of AI and ML
Types of machine learning: Supervised, Unsupervised, Reinforcement Learning
Applications of ML in real-world problems
Data, Features, Labels, and Target Variables
Model, Training, Testing, and Validation
Overfitting vs. Underfitting

Module 2: Data Preprocessing and Feature Engineering

Module 3: Supervised Learning

Module 4: Model Evaluation and Tuning

Module 5: Unsupervised Learning

Module 6: Deep Learning and Neural Networks

Perceptron and Multi-Layer Perceptron (MLP)
Activation Functions: Sigmoid, Tanh, ReLU
Backpropagation and Gradient Descent
Deep Feedforward Networks
Training Neural Networks: Loss Function and Optimizers
Regularization techniques in Deep Learning
Convolutional Layers, Pooling Layers, Fully Connected Layers
Applications: Image Classification, Object Detection
Vanishing Gradient Problem
LSTM and GRU for sequence data

Module 7: Reinforcement Learning

Module 8: Natural Language Processing (NLP)

Module 9: Computer Vision

Module 10: Machine Learning Deployment and Production

Module 11: Ethics in Machine Learning

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About Course

What Will You Learn?

Course Content

Module 1: Introduction to Machine Learning

Overview of AI and ML

Types of machine learning: Supervised, Unsupervised, Reinforcement Learning

Applications of ML in real-world problems

Data, Features, Labels, and Target Variables

Model, Training, Testing, and Validation

Overfitting vs. Underfitting

Module 2: Data Preprocessing and Feature Engineering

Handling Missing Data

Data Normalization and Standardization

Encoding Categorical Variables

Feature Selection and Extraction

Dimensionality Reduction (PCA, t-SNE)

Feature Scaling: Min-Max, Z-score

Module 3: Supervised Learning

Simple and Multiple Linear Regression

Gradient Descent and Cost Function

Model Evaluation: MSE, RMSE

Logistic Regression

Decision Trees and Random Forests

k-Nearest Neighbors (k-NN)

Support Vector Machines (SVM)

Naive Bayes

Module 4: Model Evaluation and Tuning

Accuracy, Precision, Recall, F1-Score, ROC and AUC

Cross-validation (k-fold)

Grid Search and Random Search

Bias-Variance Tradeoff

L1 and L2 Regularization (Lasso and Ridge)

Cross-Validation and Early Stopping

Module 5: Unsupervised Learning

k-Means Clustering

Hierarchical Clustering

DBSCAN

Principal Component Analysis (PCA)

t-SNE (t-Distributed Stochastic Neighbor Embedding)

Autoencoders for unsupervised learning

Module 6: Deep Learning and Neural Networks

Perceptron and Multi-Layer Perceptron (MLP)

Activation Functions: Sigmoid, Tanh, ReLU

Backpropagation and Gradient Descent

Deep Feedforward Networks

Training Neural Networks: Loss Function and Optimizers

Regularization techniques in Deep Learning

Convolutional Layers, Pooling Layers, Fully Connected Layers

Applications: Image Classification, Object Detection

Vanishing Gradient Problem

LSTM and GRU for sequence data

Module 7: Reinforcement Learning

RL Terminology: Agent, Environment, Action, Reward

Markov Decision Processes (MDP)

Q-Learning

Policy Gradient Methods

Deep Q Networks (DQN)

Module 8: Natural Language Processing (NLP)

Tokenization, Stop Words Removal, Lemmatization, and Stemming

Vectorization Techniques: Bag of Words, TF-IDF

Sentiment Analysis

Topic Modeling using LDA (Latent Dirichlet Allocation)

Word Embeddings (Word2Vec, GloVe)

Recurrent Neural Networks (RNN) for NLP

Transformer Models: BERT, GPT

Module 9: Computer Vision

Image Resizing, Normalization

Data Augmentation

CNN Architectures: LeNet, AlexNet, VGG, ResNet

Transfer Learning with Pre-trained Models

Module 10: Machine Learning Deployment and Production

Serving Models with Flask or FastAPI

REST APIs for Model Inference

Module 11: Ethics in Machine Learning

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