Roadmap to Becoming a Data Scientist

Why Machine Learning before Deep Learning?👨🏾‍🎓

Machine Learning (ML) stands as the bedrock upon which the edifice of modern artificial intelligence is constructed. But why delve into Machine Learning before taking the plunge into the depths of Deep Learning? The answer lies in establishing a solid foundation. Think of ML as the stepping stone, the precursor to the more intricate realms of artificial intelligence.

What are the resources required to get the gist of ML?📖

Here's 3 books I would strongly recommend:

Introduction to Machine Learning by Ethem Alpaydin. 

Learning with kernels by Scholkopf and Smola.

Foundations of Machine Learning by Rostamizadeh, Talwalkar, and Mohri.

Basic Unix commands 

Unix commands (case sensitive)
ls list contents of directory
mv<file1><file2>      rename file1 to file2
cp<file1><file2>      copy file1 to file2
rm<file>              delete file (difficult to recover so be careful)
mkdir<dir>            make a new directory called dir
rm –r<dir>            delete directory (use cautiously)
cd<dir>               change to directory called dir
cd ..                 go to parent directory
pwd                   path of current directory

File editing
pico<file>                             edit file with the pico editor

Text browser
lynx<url>                              open url with the lynx text browser

How to start thinking in Python with an ML perspective?  🐍 

Non-Linear Classification

When data is not linearly separable, use non-linear models like neural networks and decision trees.

Neural Networks

Inspired by the human brain, neural nets with hidden layers can model complex data.

Decision Trees

Decision trees split data recursively based on features. Capture non-linear patterns.

Combining Classifiers by Bagging

Combine multiple models to reduce variance. Build classification "committees".

Let multiple models vote to decide classification. Improves consistency.

Random Forests

Ensemble of decision trees via bagging. Very effective for a variety of data.

Decision Tree and Random Forest in Scikit-Learn

Python machine learning library with great decision tree and random forest support.

Overcoming Statistics and Probability Hurdles  🧗🏾‍♀️

Grasping core statistics and probability is crucial for data science success.

Key beginner concepts include:

• Random Variables - objects with probabilistic behavior.
• Probability Distributions - probability function of random var.
• Mean and Variance - expected value and spread of distribution.
• Correlation and Covariance - relationship between random vars.

Intermediate topics to master:

• Central Limit Theorem - distribution of sample means.
• Bayesian Inference - updating beliefs based on evidence.
• Hypothesis Testing - make decisions from stats significance.

Then level up your applied statistics skills:

• Expected Value for discrete distributions.
• Bayesian Decision Theory and Gaussian Models.
• Correlation Coefficients - Pearson and Spearman.
• Statistical Significance Testing - Chi Square, t-tests.

With this statistics base, complement your learning with essential linear algebra like vectors, dot products, and Euclidean spaces. Statistics and linear algebra form the bedrock for cutting edge machine learning approaches.

Top Python Machine Learning Libraries 🏢

Anaconda

Download the Anaconda distribution for essential data science libraries like Numpy, Scipy, scikit-learn, and more. Scikit-learn relies on Numpy and Scipy underneath. Anaconda comes prepacked with 150+ Python data tools.

Google Colab

Google Colab provides free access to GPUs and TPUs for running machine learning experiments through Jupyter notebooks on the cloud. Especially beneficial for deep learning with added compute requirements.

Take advantage of these incredible free resources to hit the ground running with real data science workflows for exploration, visualization, modeling, backtesting, and more right from your browser!!

How soon can you dive in Deep Learning? 🤿

Having laid the groundwork with Machine Learning, the natural progression is towards the intricate landscapes of Deep Learning (DL). But the question remains: How soon can one dive into this complex realm? The answer lies in leveraging the foundations established in ML.

Once you have built a solid machine learning foundation, you can progress to advanced deep learning techniques like neural networks and AI.

But how soon can you make the leap?

The key is to first establish core competencies from machine learning:

• Probability and Statistics.
• Linear Algebra.
• Python Programming.
• Data Wrangling and Visualization.
• Classical ML Models like Regression and Random Forests.

Armed with this well-rounded skillset, you can begin specializing in:

• Neural Network Architectures.
• Deep Learning Frameworks like TensorFlow and PyTorch.
• GPU-Acceleration and Model Deployment.

Think of machine learning as constructing the necessary staircase before ascending to the complex and promising landscape of deep learning. Master the fundamentals comprehensively before moving up each step.

Core Deep Learning Concepts 🙇🏾‍♂️

Linear Models

• Regression analysis.
• Polynomial fits.
• Basis function expansions.

Support Vector Machines

• Maximal margin hyperplane classification.
• Kernels for non-linear decisions.

Neural Networks

• Non-linear activation functions.
• Backpropagation algorithm.
• Convolutional and sequence networks.

Ensembling Methods

• Bagging, boosting and stacking ensembles.
• Random forests.
• Reduce variance and bias.

Decision Trees

• Recursive binary splitting.
• Information gain and gini impurity.
• Ensemble foundation bricks.

Scikit-Learn Library

• Python machine learning toolbox.
• Linear models and tree implementations.
• Pipeline for workflow automation.

Parallel Computation Powers AI Advancements 🤖

Parallel computing enables solving massive problems by breaking them into concurrent smaller pieces. This facilitates tackling complex machine and deep learning workflows.

Key Benefits

• Speed.
• Scale.
• Complexity.

Types of Parallelism

• Data Parallelism.
• Task Parallelism.

Challenges

• Dividing problems appropriately.
• Managing inter-node communication.
• Synchronizing processes.
• Load balancing.

Platforms

• OpenMP.
• MPI.
• CUDA.
• OpenCL.

Significance

• Data wrangling and preprocessing.
• Model training and evaluation.
• Hyperparameter optimization.
• Deep learning and neural nets.

Conclusion 🤝

This covers the core components required to launch a successful data science career

Start with Python - Learn Pandas, NumPy, Scikit-Learn to manipulate data and build machine learning models.

Master Statistical Methods - Probability, descriptive & inference stats, regression, hypothesis testing.

Hone Linear Algebra Skills - Vectors, matrices, eigenvalues needed for ML algorithm math.

Progress to Advanced Techniques - Neural networks, deep learning, reinforcement learning.

Leverage Cloud Resources - GPUs for fast parallel computation via services like Google Colab.

Build an Impressive Portfolio - End-to-end projects to showcase SQL, visualization, coding abilities.

Stay Up-To-Date on Latest Trends - Natural language processing, recommender systems, robotics process automation

Learning is a continuous journey. Consistently upskill across these critical pillars to boost your capabilities and open up data science career opportunities.

The field continues to evolve rapidly. Flexibility to adapt and drive change will serve you well. Happy learning!

For more Guidance

I hope you found this overview of ML and DL helpful! Mastering above basics is critical for success in technical interviews and writing efficient AI models.

If you have any other questions or topics you'd like me to cover, feel free to reach out on

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Thanks again for reading!This is Aakash Sethi signing off until next time.