Day 13 - Concept 13 Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. #ArtificialIntelligence #MachineLearning

Day 14 - Concept 14 In the context of Large Language Models (LLM), "parameters" refers to the number of connections between the neurons in the model. They determines the complexity of the model and the amount of information it can store and process. #ArtificialIntelligence

Day 15 - Concept 15 Decision Tree models split the data into subsets based on the most important features, and this process continues until the data is fully classified or the tree reaches the maximum depth. #MachineLearning #DataScience

Day 16 - Concept 16 In reinforcement learning, an "epsilon greedy policy" refers to a strategy that either follows a random policy with epsilon probability or a greedy policy otherwise. #MachineLearning #DataScience

Day 18 - Concept 18 Precision measures the proportion of true positive predictions made by the model, while recall measures the proportion of actual positive cases correctly predicted by the model. #DataScience #artificalintelligence

Day 19 - Concept 19 The smaller the value of K, the more variance and less bias KNN will exhibit. For example, if we use K = 1, a single sample close to our observation will cause the algorithm to return the wrong prediction. #artificalintelligence #machinelearning

Day 20 - Concept 20 Linear Regression is probably the most popular Supervised Learning technique in machine learning. Its goal is to fit the best line through the data to predict a continuous output. #MachineLearning #DataScience

Day 21 - Concept 21 Decision Trees can handle imbalanced datasets. You will only have to adjust the weights of the classes. #DataScience #MachineLearning #ArtificialIntelligence

Day 22 - Concept 22 ML models are trained on a historical dataset and make predictions based on that data. If the data distribution changes over time, the model can become less accurate and give poor predictions which is known as data drift. #DataScience #MachineLearning

Day 23 - Concept 23 The Exploding Gradient Problem is an issue that occurs in training artificial neural networks when large error gradients accumulate and result in very large updates to the network weights during training. #ArtificialIntelligence #DataScience #neuralnetworks

Day 24 - Concept 24 Ensembling is where we combine a group of models to produce a new model that yields better results than any initial individual models. #DataScience #MachineLearning

Day 25 - Concept 25 Exploratory Data Analysis allows us to explore and understand our data before we build a model. During this process, we investigate the dataset to discover valuable patterns, spot any potential anomalies. #DataScience #MachineLearning #ArtificialIntelligence

Day 26 - Concept 26 The Vanishing Gradient Problem occurs when the gradients of the loss function approach zero. This prevents the model from updating the weights and learning effectively. #MachineLearning #deeplearning #DataScience

Day 27 - Concept 27 Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year.  #DataScience #artificalintelligence #Statistics

Day 28 - Concept 28 A deep learning CNN consists of three layers: a convolutional layer, a pooling layer and a fully connected (FC) layer. From the convolutional layer to the FC layer, the complexity of the CNN increases.  #deeplearning #MachineLearning #artificalintelligence

Day 29 - Concept 29 A Nash equilibrium is a state in which all players in a game have made their best decisions given the decisions of the other players. #DataScience #gametheory #ArtificialIntelligence

Day 30 - Concept 30 No concept for today, lets end this series with a great saying by Judea Pearl - "Good predictions may not have good explanations". You need to understand the data well to master it. #DataScience #ArtificialIntelligence

Salman bhai ko mujhe pyaar dikhaane ke liye koi look nahi karna padhta….woh dil se hi mujhe hamesha pyaar karte hain…bas keh diya so keh diya!!