Machine Learning & Artificial Intelligence | Data Science Free Courses

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Machine Learning with Python Free Course 👇👇 https://www.freecodecamp.org/learn/machine-learning-with-python/

©How fresher can get a job as a data scientist?© Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from? The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice. Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way. All the major data science jobs for freshers will only be available through off-campus interviews. Some companies that hires data scientists are: Siemens Accenture IBM Cerner Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job. Credits: https://t.me/datasciencefun

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Data Science Tutorial for beginners 👇👇 https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners

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If you're a data science beginner, Python is the best programming language to get started. Here are 7 Python libraries for data science you need to know if you want to learn: - Data analysis - Data visualization - Machine learning - Deep learning NumPy NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. Pandas Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging. Matplotlib Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively. Scikit-learn Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation. Seaborn Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code. TensorFlow or PyTorch TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements. SciPy Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.

Python For Data Science Cheat Sheet Python Basics 📌 cheatsheet

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Have you ever thought about this?... 🤔 When you think about the data scientist role, you probably think about AI and fancy machine learning models. And when you think about the data analyst role, you probably think about good-looking dashboards with plenty of features and insights. Well, this all looks good until you land a job, and you quickly realize that you will spend probably 60-70% of your time doing something that is called DATA CLEANING... which I agree, it’s not the sexiest topic to talk about. The thing is that logically, if we spend so much time preparing our data before creating a dashboard or a machine learning model, this means that data cleaning becomes arguably the number one skill for data specialists. And this is exactly why today we will start a series about the most important data cleaning techniques that you will use in the workplace. So, here is why we need to clean our data 👇🏻 1️⃣ Precision in Analysis: Clean data minimizes errors and ensures accurate results, safeguarding the integrity of the analytical process. 2️⃣ Maintaining Professional Credibility: The validity of your findings impacts your reputation in data science; unclean data can jeopardize your credibility. 3️⃣ Optimizing Computational Efficiency: Well-formatted data streamlines analysis, akin to a decluttered workspace, making processes run faster, especially with advanced algorithms.

Data Scientist Roadmap | |-- 1. Basic Foundations | |-- a. Mathematics | | |-- i. Linear Algebra | | |-- ii. Calculus | | |-- iii. Probability | | `-- iv. Statistics | | | |-- b. Programming | | |-- i. Python | | | |-- 1. Syntax and Basic Concepts | | | |-- 2. Data Structures | | | |-- 3. Control Structures | | | |-- 4. Functions | | | `-- 5. Object-Oriented Programming | | | | | `-- ii. R (optional, based on preference) | | | |-- c. Data Manipulation | | |-- i. Numpy (Python) | | |-- ii. Pandas (Python) | | `-- iii. Dplyr (R) | | | `-- d. Data Visualization | |-- i. Matplotlib (Python) | |-- ii. Seaborn (Python) | `-- iii. ggplot2 (R) | |-- 2. Data Exploration and Preprocessing | |-- a. Exploratory Data Analysis (EDA) | |-- b. Feature Engineering | |-- c. Data Cleaning | |-- d. Handling Missing Data | `-- e. Data Scaling and Normalization | |-- 3. Machine Learning | |-- a. Supervised Learning | | |-- i. Regression | | | |-- 1. Linear Regression | | | `-- 2. Polynomial Regression | | | | | `-- ii. Classification | | |-- 1. Logistic Regression | | |-- 2. k-Nearest Neighbors | | |-- 3. Support Vector Machines | | |-- 4. Decision Trees | | `-- 5. Random Forest | | | |-- b. Unsupervised Learning | | |-- i. Clustering | | | |-- 1. K-means | | | |-- 2. DBSCAN | | | `-- 3. Hierarchical Clustering | | | | | `-- ii. Dimensionality Reduction | | |-- 1. Principal Component Analysis (PCA) | | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE) | | `-- 3. Linear Discriminant Analysis (LDA) | | | |-- c. Reinforcement Learning | |-- d. Model Evaluation and Validation | | |-- i. Cross-validation | | |-- ii. Hyperparameter Tuning | | `-- iii. Model Selection | | | `-- e. ML Libraries and Frameworks | |-- i. Scikit-learn (Python) | |-- ii. TensorFlow (Python) | |-- iii. Keras (Python) | `-- iv. PyTorch (Python) | |-- 4. Deep Learning | |-- a. Neural Networks | | |-- i. Perceptron | | `-- ii. Multi-Layer Perceptron | | | |-- b. Convolutional Neural Networks (CNNs) | | |-- i. Image Classification | | |-- ii. Object Detection | | `-- iii. Image Segmentation | | | |-- c. Recurrent Neural Networks (RNNs) | | |-- i. Sequence-to-Sequence Models | | |-- ii. Text Classification | | `-- iii. Sentiment Analysis | | | |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) | | |-- i. Time Series Forecasting | | `-- ii. Language Modeling | | | `-- e. Generative Adversarial Networks (GANs) | |-- i. Image Synthesis | |-- ii. Style Transfer | `-- iii. Data Augmentation | |-- 5. Big Data Technologies | |-- a. Hadoop | | |-- i. HDFS | | `-- ii. MapReduce | | | |-- b. Spark | | |-- i. RDDs | | |-- ii. DataFrames | | `-- iii. MLlib | | | `-- c. NoSQL Databases | |-- i. MongoDB | |-- ii. Cassandra | |-- iii. HBase | `-- iv. Couchbase | |-- 6. Data Visualization and Reporting | |-- a. Dashboarding Tools | | |-- i. Tableau | | |-- ii. Power BI | | |-- iii. Dash (Python) | | `-- iv. Shiny (R) | | | |-- b. Storytelling with Data | `-- c. Effective Communication | |-- 7. Domain Knowledge and Soft Skills | |-- a. Industry-specific Knowledge | |-- b. Problem-solving | |-- c. Communication Skills | |-- d. Time Management | `-- e. Teamwork | `-- 8. Staying Updated and Continuous Learning |-- a. Online Courses |-- b. Books and Research Papers |-- c. Blogs and Podcasts |-- d. Conferences and Workshops `-- e. Networking and Community Engagement

13. Dealing with Missing Data: Techniques like mean, median, or mode imputation, or more advanced methods like multiple imputation or k-nearest neighbors imputation can be used. 14. Parametric vs. Non-parametric Tests: Parametric tests assume specific data distributions, while non-parametric tests do not. Parametric tests are more powerful but require data to meet certain assumptions. 15. Data Normalization and Standardization: Techniques to scale data to a common range or standardize it with mean 0 and standard deviation 1. Common methods include min-max scaling and z-score standardization.

Interview questions with answers for Statistics 👇 1. Describe the central limit theorem and its importance in statistics. How does it relate to data analysis?    2. Explain the difference between descriptive and inferential statistics. Provide examples of each. 3. What is the purpose of hypothesis testing? Can you walk me through the steps involved in hypothesis testing? 4. What is p-value in hypothesis testing? How do you interpret p-values? 5. What is the difference between Type I and Type II errors? Can you provide examples of each? 6. How would you determine if a dataset is normally distributed? What graphical and statistical methods can you use? 7. Explain the difference between correlation and causation. How would you determine if there is a causal relationship between two variables? 8. What is the difference between population and sample? Why is it important to understand this difference in data analysis? 9. What are the measures of central tendency? When would you use each one (mean, median, mode)? 10. Describe a situation where you would use regression analysis. What are some common regression techniques, and how do you interpret their results? 11. Can you explain the concept of standard deviation? How is it related to variance, and what does it indicate about the data? 12. What is the purpose of ANOVA (Analysis of Variance)? How does it differ from regression analysis? 13. How would you deal with missing data in a dataset? What are some common imputation techniques? 14. Explain the difference between a parametric and non-parametric test. When would you choose one over the other? 15. What is the purpose of data normalization and standardization? Can you explain some common methods for achieving this? Below you can find the answers 😊 1. Central Limit Theorem (CLT): States that regardless of the distribution of the population, the distribution of sample means approaches a normal distribution as sample size increases. It's crucial for making reliable inferences from sample data. 2. Descriptive vs. Inferential Statistics: Descriptive statistics summarize data, like mean or median, while inferential statistics make predictions or inferences about a population based on sample data. 3. Hypothesis Testing: A method to test a claim about a population parameter using sample data. It involves formulating null and alternative hypotheses, collecting data, and drawing conclusions based on statistical analysis. 4. P-value: Probability of obtaining the observed results (or more extreme) if the null hypothesis is true. It helps determine the significance of results in hypothesis testing. 5. Type I and Type II Errors: Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis. 6. Normality Testing: Graphical methods like histograms or statistical tests like Shapiro-Wilk can be used to check if data is normally distributed. 7. Correlation vs. Causation: Correlation measures the relationship between variables, while causation indicates one variable causing changes in another. Establishing causation requires controlled experiments. 8. Population vs. Sample: Population includes all individuals of interest, while a sample is a subset of the population. Understanding this difference is crucial for making generalizations about the population. 9. Measures of Central Tendency: Mean, median, and mode represent the center of a dataset. Mean is suitable for normally distributed data, median for skewed data, and mode for categorical data. 10. Regression Analysis: Used to model the relationship between variables. Common techniques include linear regression, logistic regression, and polynomial regression. 11. Standard Deviation: Measures the spread of data around the mean. It's the square root of the variance and indicates the variability of data points. 12. ANOVA: Analyzes differences in means among multiple groups. It differs from regression by comparing means across groups instead of modeling relationships between variables.