Machine Learning & Artificial Intelligence | Data Science Free Courses

Important Topics to become a data scientist [Advanced Level] 👇👇 1. Mathematics Linear Algebra Analytic Geometry Matrix Vector Calculus Optimization Regression Dimensionality Reduction Density Estimation Classification 2. Probability Introduction to Probability 1D Random Variable The function of One Random Variable Joint Probability Distribution Discrete Distribution Normal Distribution 3. Statistics Introduction to Statistics Data Description Random Samples Sampling Distribution Parameter Estimation Hypotheses Testing Regression 4. Programming Python: Python Basics List Set Tuples Dictionary Function NumPy Pandas Matplotlib/Seaborn R Programming: R Basics Vector List Data Frame Matrix Array Function dplyr ggplot2 Tidyr Shiny DataBase: SQL MongoDB Data Structures Web scraping Linux Git 5. Machine Learning How Model Works Basic Data Exploration First ML Model Model Validation Underfitting & Overfitting Random Forest Handling Missing Values Handling Categorical Variables Pipelines Cross-Validation(R) XGBoost(Python|R) Data Leakage 6. Deep Learning Artificial Neural Network Convolutional Neural Network Recurrent Neural Network TensorFlow Keras PyTorch A Single Neuron Deep Neural Network Stochastic Gradient Descent Overfitting and Underfitting Dropout Batch Normalization Binary Classification 7. Feature Engineering Baseline Model Categorical Encodings Feature Generation Feature Selection 8. Natural Language Processing Text Classification Word Vectors 9. Data Visualization Tools BI (Business Intelligence): Tableau Power BI Qlik View Qlik Sense 10. Deployment Microsoft Azure Heroku Google Cloud Platform Flask Django Join @datasciencefun to learn important data science and machine learning concepts ENJOY LEARNING 👍👍

Most Machine Learning articles on Medium are really very bad quality and repetitive. Titles are usually clickbaits. Most start with a story which is utter nonsense and totally not required. In some 5-10% content is useful but most are fully useless. Sorry if I hurt feelings. Agree 👍

Python Workout 50 Essential Exercises 📓 book

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems 📚 book

To become a Machine Learning Engineer: • Python • numpy, pandas, matplotlib, Scikit-Learn • TensorFlow or PyTorch • Jupyter, Colab • Analysis > Code • 99%: Foundational algorithms • 1%: Other algorithms • Solve problems ← This is key • Teaching = 2 × Learning • Have fun!

DISADVANTAGE OF MACHINE LANGUAGE Here are some of the main disadvantages of machine languages: • Machine Dependent - the internal design of every computer is different from every other type of computer, machine language also differs from one computer to another. Hence, after becoming proficient in the machine language of one type of computer, if a company decides to change to another type, then its programmer will have to learn a new machine language and would have to rewrite all existing program. • Difficult to Modify - it is difficult to correct or modify this language. Checking machine instructions to locate errors is very difficult and time consuming. • Difficult to Program - a computer executes machine language program directly and efficiently, it is difficult to program in machine language. A machine language programming must be knowledgeable about the hardware structure of the computer.

ADVANTAGE OF MACHINE LANGUAGE The only advantage of machine language is that the program of machine language runs very fast because no translation program is required for the CPU.

MACHINE LANGUAGE The instructions in binary form, which can be directly understood by the computer (CPU) without translating them, is called a machine language or machine code. Machine language is also known as first generation of programming language. Machine language is the fundamental language of the computer and the program instructions in this language is in the binary form (that is 0's and 1's). This language is different for different computers. It is not easy to learn the machine language.

Where to get data for your next machine learning project? An overview of 8 amazing resources to accelerate your next project with data! 📌 Google Datasets Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset. 📌 Papers with Code Datasets An exclusive collection of 4053 machine learning datasets with a supreme search and a good composition of datasets . 📌 Kaggle Dataset Explore, analyze, and share quality data. 📌 Big Bad NLP Datasets One of the best sources for sophisticated Natural Language Processing datasets 📌 Hugging Face Datasets Well known for NLP but good news hugging face is expanding and they can add datasets for machine learning soon, they have 921 datasets. 📌 Open Data on AWS This registry exists to help people discover and share datasets that are available via AWS resources 📌 Awesome Public Datasets A topic-centric list of HQ open datasets. 📌 Azure public data sets This one has public data sets for testing and prototyping. 📌 Carnegie Mellon University A listing of 750 databases, datasets, and research support tools. Bonus: This articles on Kdnuggets covers around 80 datasets sources of the datasets. Enjoy machine learning.

Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE https://developers.google.com/machine-learning/crash-course https://www.kaggle.com/learn/overview https://forums.fast.ai/t/recommended-python-learning-resources/26888 https://www.fast.ai/ https://imp.i115008.net/JrBjZR https://ern.li/OP/1qvkxbfaxqj ENJOY LEARNING 👍👍