🏹 Let's go Day 1 of #31DaysofML

💡What is #MachineLearning? 💡

ML = Using data to answer questions!
📌 Using data = Training
📌 Answer questions = Predictions

Let's keep going... 🧵👇
2/4 What are the 7 steps in Machine Learning?

1️⃣ Collect Data
2️⃣ Prepare Data
3️⃣ Choose a Model
4️⃣ Train the Model
5️⃣ Evaluate the Model
6️⃣ Parameter Tuning
7️⃣ Make Predictions

For more @yufengg amazing video 👉bit.ly/3j3j2ne

#31DaysofML
@yufengg 1️⃣ Collect Data

📌Quantity & quality of your data dictate how accurate our model is
📌The outcome of this step is usually a table with some values (features)
📌 If you want to use pre-collected data - get it from sources such as Kaggle or BigQuery Public Datasets

#31DaysofML
@yufengg 2️⃣ Prepare Data

📌Wrangle data and prepare it for training
📌Split data into training, test and evaluation sets
📌Clean data - correct errors, missing values, normalize etc

#31DaysofML
@yufengg 3️⃣ Choose a Model

There are different types of learning to choose from. These 4 are the most common that you usually come across. (will cover these in coming days of #31DaysofML)

📌 Supervised
📌 Unsupervised
📌 Semi-supervised
📌 Reinforcement
@yufengg 4️⃣ Train the Model

📌Goal = make a prediction correctly as often as possible
📌Depends on learning method chosen in step 3
📌Eg: Linear regression algorithm would learn values for weights & biases in y = mx + b
📌Each iteration of process is a training step

#31DaysofML
@yufengg 5️⃣ Evaluate the Model

📌 Test the model against unseen data (usually 20% of data is set aside for this)
📌 Use some metric or combination of metrics to "measure" objective performance of model

#31DaysofML
@yufengg 6️⃣ Parameter Tuning - will cover this later in the #31DaysofML but for now note the following:

📌Also called Hyperparameter Tuning
📌Tune model parameters for improved performance.
📌These include learning rate, training steps etc
@yufengg 7️⃣ Make Predictions

📌 Test the model on the test data (unseen)
📌 Helps understand how the model will perform in the real world

#31DaysofML

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