💛 Machine Learning from Scratch for beginners !!
A Thread 🧵 👇
#100DaysOfCode #Ml #MachineLearning #CodeNewBie #AI
A Thread 🧵 👇
#100DaysOfCode #Ml #MachineLearning #CodeNewBie #AI
🔹What is Machine Learning ?
Machine Learning is a growing technology which enables computer to learn automatically from past data . It uses various algorithms for building mathematical models and making predictions using historical data or information .
Machine Learning is a growing technology which enables computer to learn automatically from past data . It uses various algorithms for building mathematical models and making predictions using historical data or information .
🔹 What is a model in ML ?
A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.
A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.
🔹 How ML works ?
A ML system learns from historical data , builds the prediction model and whenever it receives new data ,it predicts the output for it .
A ML system learns from historical data , builds the prediction model and whenever it receives new data ,it predicts the output for it .
The Accuracy of predicted output depends upon the amount of Training Data .The huge number of data helps to build a better model which can predict the output more accurately.
🔹 Difference between AI and ML : -
Artificial intelligence and Machine Learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.
Artificial intelligence and Machine Learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.
AI is bigger a concept to create intelligent systems that can stimulate human thinking capability and behaviour .
Whereas , machine learning is an application or subset of AI that allows machines to learn from past data without being programmed explicitly.
Whereas , machine learning is an application or subset of AI that allows machines to learn from past data without being programmed explicitly.
🔹 Algorithm Vs Model : -
An “Algorithm” in machine learning is a procedure that is run on data to create a machine learning “Model.”
Machine learning algorithms perform “pattern recognition.” Algorithms “learn” from data, or are “fit” on a dataset.
An “Algorithm” in machine learning is a procedure that is run on data to create a machine learning “Model.”
Machine learning algorithms perform “pattern recognition.” Algorithms “learn” from data, or are “fit” on a dataset.
🔹 Examples of Machine learning algorithms : -
• Linear Regression
• Logistic Regression
• Decision Tree
• Artificial Neural Network
• k-Nearest Neighbors
• k-Means
• Linear Regression
• Logistic Regression
• Decision Tree
• Artificial Neural Network
• k-Nearest Neighbors
• k-Means
A “Model” in machine learning is the output of a machine learning algorithm run on data.
The model is the “thing” that is saved after running a machine learning algorithm on training data.
The best analogy is to think of the machine learning model as a “program.”
The model is the “thing” that is saved after running a machine learning algorithm on training data.
The best analogy is to think of the machine learning model as a “program.”
🔹Features: -
Features are defined as individual independent variables that act as input. Prediction models use features to make predictions.
🔹Labels : -
Labels are referred to as the final output based on it's features. It is a dependent variable .
Features are defined as individual independent variables that act as input. Prediction models use features to make predictions.
🔹Labels : -
Labels are referred to as the final output based on it's features. It is a dependent variable .
🔹Types Of Machine Learning : -
1. Supervised Learning.
2. Unsupervised learning.
3. Semi-Supervised Learning.
4. Reinforcement learning.
1. Supervised Learning.
2. Unsupervised learning.
3. Semi-Supervised Learning.
4. Reinforcement learning.
1️⃣ Supervised Learning : -
Supervised Learning is the type of Machine Learning in which machines are trained using well "labelled" training data and on the basis of that data , machine predicts the output .Labelled data means data with some input data tagged with correct output.
Supervised Learning is the type of Machine Learning in which machines are trained using well "labelled" training data and on the basis of that data , machine predicts the output .Labelled data means data with some input data tagged with correct output.
2️⃣ Unsupervised learning : -
Unsupervised learning is a machine learning technique in which models are not Supervised using training datasets. Instead , model itself finds the hidden patterns and insights form the given data.
Unsupervised learning is a machine learning technique in which models are not Supervised using training datasets. Instead , model itself finds the hidden patterns and insights form the given data.
3️⃣ Semi-Supervised Learning:-
Semi-supervised learning algorithms represent a middle ground between supervised and unsupervised algorithms. In essence, the semi-supervised model combines some aspects of both into a thing of its own. It uses a limited set of labeled sample data.
Semi-supervised learning algorithms represent a middle ground between supervised and unsupervised algorithms. In essence, the semi-supervised model combines some aspects of both into a thing of its own. It uses a limited set of labeled sample data.
4️⃣ Reinforcement learning : -
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment.
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment.
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