Starting with ML basics

A Beginner’s Dive into Machine Learning.

First of all,

Welcome to our in-depth tutorial to the fundamental ideas behind machine learning model construction techniques. We will give the fundamental ideas, methods, and strategies that guide the creation of machine learning models in this investigation. These principles are the foundation of artificial intelligence and data-driven decision-making, therefore it's important for both newcomers and seasoned professionals to grasp them. We will elucidate the nuances of every technique, including reinforcement learning, supervised and unsupervised learning, and their applications and algorithms . With the help of incisive explanations, mathematical expressions and Jupyter notebook's code snippet, we hope to provide you the information and abilities needed to confidently and completely understanding of machine learning.

Python is the primary programming language used in the machine learning community due to its simplicity and extensive libraries. We will see Python basics and then dive into libraries such as NumPy, Pandas, Matplotlib, TensorFlow and seaborn as well as which are essential for machine learning tasks. Which need Jupyter notebook as main tool or platform for python. Google collab can also be used as alternate option for python.

Machine Learning is done by major Three methods which explained as followed:

Machine Learning

1. Supervised Learning :

Definition: Supervised learning involves training a model on a labelled dataset, where the algorithm learns to map input data to the corresponding output labels.

Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), etc.

Techniques: Classification, Regression ,Feature Engineering, Cross-Validation, Regularization, Ensemble Methods.

Usage: Predictive modelling, classification, regression, recommendation systems.

2. Unsupervised Learning:

Definition: Unsupervised learning deals with unlabelled data, where the algorithm identifies patterns or structures within the dataset without explicit guidance.

 Algorithms: K-means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), etc.

Techniques: Dimensionality Reduction, Clustering Evaluation Metrics, Data Pre-processing.

Usage: Customer segmentation, anomaly detection, data compression, visualization.

3. Reinforcement Learning:

Definition: Reinforcement learning involves training agents to make sequential decisions in an environment to maximize cumulative rewards.

Algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods, Actor-Critic Models.

Techniques: Decision Making, Exploration vs. Exploitation, Reward Shaping, Temporal Difference Learning.

Usage: Game playing, robotics, autonomous vehicle control, resource management.

What will be the Next ?

Supervised Learning in Details with explanation of included algorithms.