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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Avoid These 5 Common Mistakes in AI that Every Novice Makes

Ayushi Trivedi
Ayushi Trivedi Last Updated : 25 Jul, 2024
5 min read

Introduction

Try to think of yourself as a student entering the first day at a new school. You are learning with enthusiasm but there are so many things which are new to you and this easily leads to the mistakes. The AI same is somewhat like that for a beginner – the world is interesting and full of possibilities and it is quite easy to fall into various traps. In this article, you will find five common mistakes in AI that people make in their first step in AI encounter and recommendations on how to avoid them. Of course, this guide will accompany you and help make your way into AI as enjoyable and productive as possible.

Avoid These 5 Common Mistakes Every Novice in AI Makes

Overview

  • Understand the importance of having a strong foundational knowledge in AI.
  • Recognize the significance of quality data and its impact on AI models.
  • Discover the importance of striking a balance between theory and application.
  • Determine the typical model evaluation traps and how to prevent them.
  • Recognize the importance of lifelong learning and keeping up with developments in artificial intelligence.

Table of contents

Common Mistakes in AI and How to Avoid Them

Let us now look into the common mistakes that every novice in AI makes and how we can avoid those in detail.

1. Neglecting the Fundamentals

Many learners make the mistake of diving right into higher-order algorithms and computer coding without bothering to get the fundamentals down first. It also implies a solid foundation as the corner stone of any architecture. However, if you do not gain a fundamental grasp of concepts such as AI and machine learning, you may feel adrift for some time as you attempt to perform a difficult calculation. These include linear algebra, probability and statics which are fundamental courses in IT. Learn about the basic concepts of machine learning including regression, classification, cluster analysis, and neural network analysis.

Solution: Spend time to understand the basics of AI, and machine learning as well as data science. Know what supervised and unsupervised learning are, what neural networks are, and data preprocessing. Literature like online courses, textbooks and tutorials can be of great assist.

Example: Imagine trying to build a house without knowing how to read blueprints or use basic tools. You might get a few walls up, but the structure won’t be sound. The same applies to AI; without understanding the basics, your projects may lack robustness.

2. Ignoring Data Quality

Lack of recognition of the value of high-quality data or utilizing low-quality dataset into the models. In the context of AI, data can be seen as the raw material for the AI related work. There are times when cheap fuel also leads to the deterioration of the engine while using good quality fuel at all times would not pose a problem. Likewise, quality data that has been cleaned and properly formatted for use optimizes your AI models’ performance.

Solution: Make your data as clean as possible and ensure that your datasets are preprocessed. Make sure the data gathered is relevant to the problem to be solved and is not wrongly gathered. Learn about methods such as data normalization, methods of dealing with missing values, and how to augment the existing dataset.

Example: Think of data as the fuel for your AI engine. Poor quality fuel can damage the engine, while high-quality fuel ensures smooth performance. Similarly, clean, well-prepared data ensures your AI models perform accurately and reliably.

3. Focusing Solely on Theory

Students devoting more time in learning theories instead of learning how to apply them practically. As they say knowledge is half baked till it is applied In this instance theory is explicit, but the skill of the actual process makes it much clearer. Be involved in practical activities, solve problems at hackthons, and take part in practical work. This will not only improve the considered skill but also gather important experience in problems solving and models deployment.

Solution: Better learning by relating the theory with practical problems. Engage in projects, internships, and Kaggle or any other competitions such as those by crowdtangle to gain practical experience. Constructing projects from the ground up strengthens principles and its difficulties improve the problem-solving ability.

Example: It’s like learning to swim by reading books about swimming techniques but never actually getting into the water. Practical experience is crucial to truly mastering AI concepts.

4. Improper Model Evaluation

Failing to properly evaluate models, leading to overfitting or underfitting. Use appropriate evaluation metrics and techniques to assess your models. Split your data into training, validation, and test sets. Utilize cross-validation, confusion matrices, and performance metrics like accuracy, precision, recall, and F1-score to ensure your model performs well.

Solution: Use appropriate evaluation metrics and techniques to assess your models. Split your data into training, validation, and test sets. Utilize cross-validation, confusion matrices, and performance metrics like accuracy, precision, recall, and F1-score to ensure your model performs well.

Example: Imagine a chef who never tastes their dishes before serving them. Proper evaluation is like taste-testing; it ensures your model is ready for real-world application and can perform as expected.

5. Not Staying Updated

Stay informed about the new tendencies in the rapidly progressing area of artificial intelligence. AI is an active field of research, and there is always something new on methods, resources, and studies with a very high frequency. Subscribing to the activities of AI community, following the publication of research papers, following the artificial intelligence personalities, and attending the conferences and webinars.

Solution: Ensure the AI model is up to date through subscribing to AI journals, attending AI conferences, and being a member of AI groups. One should read research papers, future AI blogs, and engage themselves in webinars to know what new technique or technology exists.

Example: AI is indeed recognized as a swift-growing field or domain, like many other domains that fall under technology. Employers should update themselves just like we update our devices to enjoy from the latest products and added security.

You can learn more about Artificial Intelligence and GenAI by enrolling in our course today!

Conclusion

Starting a career in AI has its rewards and challenges. You can improve your abilities and make a valuable contribution to the AI community by staying away from these typical blunders and adopting a balanced approach to learning. Recall that a solid foundation, high-quality data, real-world application, appropriate assessment, and ongoing learning are essential for success in AI. In this article we saw 5 common mistakes in AI that every beginner can make.

Frequently Asked Questions

Q1. Why are the fundamentals of AI important?

A. The fundamentals provide a necessary foundation that helps in understanding more complex concepts and algorithms. Without a solid grasp of basics, one might struggle with advanced topics and practical applications.

Q2. How can I improve the quality of my datasets?

A. Improving data quality involves cleaning and preprocessing your data. Techniques include removing duplicates, handling missing values, normalizing data, and augmenting data to increase its diversity and representativeness.

Q3. What are some good resources for learning AI fundamentals?

A. There are many resources available including online courses (Coursera, edX, Udacity), textbooks (e.g., “Introduction to Machine Learning with Python”), and tutorials on platforms like Medium and Towards Data Science.

Q4. How do I balance theory and practice in AI learning?

A. Balance can be achieved by applying theoretical knowledge to real-world projects. Engage in practical exercises, participate in coding competitions, and work on personal or open-source projects to reinforce learning.

Q5. Why is continuous learning important in AI?

A. AI is a rapidly evolving field with constant advancements. Continuous learning ensures that you stay updated with the latest technologies, techniques, and best practices, making you more competitive and effective in the field.

AIbeginnersCommon Mistakes
Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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