How do I start learning AI and machine learning?

Data Science, AI and ML
#1

I suggest that if you don’t have experience with Python, you better learn machine learning with Python together.

I have search for jobs in AI and Machine Learning on LinkedIn to analyze which languages and tools required the most. Python was the most required language by far.

Why does Python ? Well, it is simple to programming. Instead consume time by programming, you can focus the algorithms. Also, one of the biggest advantage of it I believe, it has great libraries for AI and Machine Learning.

#3

Machine learning (ML) is a type of artificial intelligence (AI) that allows software to increase prediction accuracy without being specifically designed to do so. Machine learning algorithms forecast new output values using historical data as input.
Recommendation engines commonly employ machine learning. Typical applications include fraud detection, spam filtering, malware threat identification, business process automation (BPA), and predictive maintenance.

Significance

Machine learning is important because it allows companies to discover patterns in customer behaviour and corporate operations while also assisting in the development of new products. Many of today’s most successful organisations, such as Facebook, Google, and Uber, use machine learning. Machine learning has become a critical component of many enterprises.

Types

The method through which an algorithm learns to increase its prediction accuracy is known as traditional machine learning. The four basic approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The algorithm that data scientists use is determined by the sort of data they wish to predict.

1.Supervised Learning: Data scientists supply labelled training data to algorithms and indicate the variables they want the software to look for connections between in this type of machine learning.

2.Unsupervised Learning: This type of machine learning employs algorithms that have been trained on unlabeled data. In big data sets, the programme hunts for important relationships. All of the data used to train algorithms, as well as the forecasts and recommendations generated by them, is predetermined.

3.Semi-supervised learning: It combines the two previous approaches to machine learning. Despite the fact that data scientists may feed an algorithm largely labelled training data, the model is free to explore the data and come to its own conclusions about the set.

4.Reinforcement learning: It is a technique used by data scientists to teach a machine to follow a set of well-defined rules in a multi-step operation. Data scientists develop an algorithm to fulfil a task and provide it with positive or negative feedback as it learns how to do so.However, for the most part, the algorithm chooses which steps to take along the way on its own.

#4

Description of types of Machine learning

The Supervised machine learning: In supervised machine learning, the data scientist must train the system with both labelled inputs and desired outputs.The following tasks benefit from supervised learning algorithms:

Binary classification is the division of data into two groups. Choosing between more than two categories of answers is referred to as multi-class classification. Predicting continuous values using regression modelling. Combining the predictions of numerous machine learning models to get an accurate prediction is referred to as assembling.

Unsupervised Machine Learning: Machine learning methods that are unsupervised do not require data to be labelled. They dig through unlabeled data in search of patterns that can be utilised to divide data into subgroups. Unsupervised algorithms make up the majority of deep learning algorithms, including neural networks. The following tasks are well-suited to unsupervised learning algorithms:

Clustering: Dividing a dataset into groups based on their similarity.
Anomaly detection: identifying unexpected data points in a set of data.
Association mining: Identifying groups of objects in a data collection that commonly occur together
Dimensionality reduction: Reducing the number of variables in a data set

Semi-Supervised Learning: Data scientists input a limited quantity of labeled training data to an algorithm in semi-supervised learning. The algorithm then learns the data set’s dimensions, which it may subsequently apply to new, unlabeled data. When algorithms are trained on labeled data sets, their performance usually improves. Labeling data, on the other hand, can be time consuming and costly. Semi-supervised learning falls between supervised and unsupervised learning in terms of performance and efficiency. Semi-supervised learning is utilized in the following areas:

Machine translation : teaching algorithms to translate languages using a smaller set of words than a full dictionary.

Fraud detection: identifying cases of fraud when there are just a few positive examples available.

Data labeling: Algorithms trained on tiny data sets can automatically apply data labels to bigger ones.

Reinforcement learning

Reinforcement learning is based on the programming of an algorithm with a specific goal and a set of rules for achieving that objective. The algorithm is also programmed to seek positive rewards (which it receives when it performs an activity that helps it get closer to its ultimate objective) and avoid negative rewards (which it receives when it performs an action that causes it to get further away from its ultimate goal). Reinforcement learning is widely employed in a variety of fields, including:

Robotics: Using this method, robots may learn to do tasks in the physical environment. Reinforcement learning has been used to train bots how to play a variety of video games.

Resource management: When faced with limited resources and a clear aim, reinforcement learning can assist businesses in determining how to allocate resources.