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Do you know what Machine Learning really is? 🤖 It’s the reason your apps feel like they “understand” you. Machine Learning allows computers to learn from data and improve over time — without being
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Feature Encoding in Machine Learning In real-world datasets, we often have categorical values like city names, colors, education levels, or product types. But machine learning algorithms understand on
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Scaling and Normalization in Machine Learning Scaling and normalization are data preprocessing techniques used to standardize the range of independent variables. 🔹 Feature Scaling Adjusts data to a s
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Data Processing in Machine Learning Data processing, also known as data preprocessing, converts raw data into a clean and structured format suitable for machine learning models. 🔹 Key Steps Data Clea
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Data Collection in Machine Learning Data collection is the process of gathering raw data required to train and evaluate machine learning models. 🔹 Sources of Data Databases Sensors and IoT devices AP
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End-to-End Machine Learning Workflow The end-to-end machine learning workflow describes the complete lifecycle of an ML model from problem definition to deployment. Problem Definition: Identify the p
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Model evaluation is a critical step in machine learning that determines how well a trained model performs on unseen data. After training a model, its predictions are compared with actual outcomes usin
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Learning Curves in Machine Learning Learning curves are graphs that show how a machine learning model’s performance changes as the size of the training dataset increases. 🔹 Training Error Training er
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🔁 Cross-Validation — Testing Your Model the Smart Way Don’t trust just one split of data. Test it again… and again… for real-world accuracy. ✅ Cross-validation is a powerful technique used in machin
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Bias vs Variance — Finding the Sweet Spot in Machine Learning Too simple? You miss patterns. Too complex? You learn noise. The best models live in the balance. 📊✨ In machine learning, every model fa
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📉📈 Underfitting vs Overfitting — Finding the Right Balance in Machine Learning In machine learning, a model must learn the true pattern of the data — not too little and not too much. 🔹 Underfitting
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Regression models are fundamental techniques in machine learning and data science used to understand relationships between variables and make accurate predictions. Each type of regression serves a dif
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About @codingwithsoham

@codingwithsoham is a Instagram growing creator in the Education category. With 20 followers and 12 posts, codingwithsoham's account shows a follower-to-following ratio of 0.5:1. View all content anonymously on Pictame - no login required.

codingwithsoham's engagement rate sits at 51.67%. That's 933% above what you'd typically see for accounts with 1K-10K followers (average 5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 8% are videos and 92% are photos. Notably, videos tend to get 1.5x more engagement than photos. Content shows up about every 8 days. Captions lean detailed, averaging 879 characters.

51.67% engagement rate — 933% above average for 1K-10K follower accounts.
8% videos, 92% photos. Videos get 1.5x more engagement here.
Posts about every 8 days.

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codingwithsoham
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@codingwithsoham

PublicEducation
Posts
12
Followers
20
Top Hashtags
#MachineLearningConcepts#DataScienceLearning#learning#AIForStudents#ai#DataScienceStudents#machinelearning#datascience

Explore the most impactful hashtags used in recent posts. These tags highlight the core interests and trending topics that define this profile's digital influence and content strategy.

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Popular Hashtags

#viral#reels#fashion#art#photooftheday#explore#beautiful#nature#travel#follow#explorepage#happy
Following
37
Avg. Likes
10
Avg. Comments
0
Engagement
50.00%

Code. Learn. Repeat. 🔁 Sharing the highs and lows of learning to code.

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