Get Ready for DS & ML Career

Think Different - Dhiraj Patra
4 min readJul 30, 2023
unplush

Simply follow the steps below.

Step 1:

Probability & Stats

Variables & Linear Algebra (Tensors)

Python, TensorFlow (Tensor operations)

Data Visualization (Tabular Data)

Step 2:

Learn about some concept.

Exploring Advanced Computer Architectures and Programming Techniques:

1. Performance Enhancement Techniques: Diving into concepts like Pipelining, Super-scalar Processors, SIMD Vectorization, and Caches for optimizing computer architectures.

2. Harnessing Parallel Processing: Understanding the power of Multicore Architectures, GPUs, and Data Access Optimization to leverage parallelism.

3. Parallel Programming Paradigms: Exploring Shared Memory Programming with OpenMP, Message-Passing with MPI, CUDA for GPUs, and the distributed computing framework, MapReduce.

Step 3:

Learn The ML Process — How to solve a problem using data and algorithms? What are the problems solvable by ML/AI? What cannot be solved? Data Types and State of the Art Models

Tabular Data — Gradient Boosted Models Image Data — Convolutional Neural Networks Sequential and Time Series Data — Recurrent Neural Networks Text Data — Transformers Cool Applications — Generative Models, GANs Robotics and other niche areas — Reinforcement Learning

Decision Tree and Gradient Boosted Models — State of the Art for Tabular Datasets The first neural network — A very shallow sigmoidal NN (or Logistic Regression) The Mathematics of ML and AI — Empirical Risk Minimization, Gradient Descent and Back Propagation A Deep Neural Network: Neurons, Layers, Activation Function, Loss Function, Weights and Biases, Minibatch, Training Algorithms (Momentum, AdaGrad, ADAM), Weight Initialization Keras: Finding data, building a model, training a model, model evaluation Deep Dive into model selection, evaluation and fine-tuning.

Step 4:

Do deep with CNN [computer vision]

Key Topics in Computer Vision:

1. Convolutional Operation: Understanding kernels, padding, and feature maps.

2. Pooling Operation: Utilizing pooling in CNN for improved performance.

3. Image Classification: Exploring CNN for accurate image classification.

4. Transfer Learning: Leveraging pre-trained models for efficient training.

5. Residual Connection and Batch Normalization: Techniques for training deeper networks.

6. Depthwise Separable Convolution and Xception: Advanced convolutional methods.

7. Object Localization and Detection: Overview of algorithms like YOLO.

8. Image Segmentation: Deep learning models UNet and DeepLab for image segmentation.

Step 5:

Deep into Natural Language Processing.

Exploring Neural Networks in Time Series and NLP:

1. Time Series Solutions: Understanding Recurrent Neural Networks (RNNs) for time series problems.

2. Model Evaluation: Addressing modeling challenges and evaluating common sense baselines.

3. LSTM and GRU: Leveraging LSTM and GRU for long time series analysis.

4. NLP Essentials: Tackling NLP tasks with crucial steps like data preprocessing and text vectorization.

5. Text Vectorization Techniques: Exploring standardization, vocabulary indexing, and word embedding with Word Vectors and TF-IDF.

6. Bag of Words Models: Examining a wide range of Bag of Words Models from Naive Bayes to Deep Neural Networks.

7. Attention Mechanism: Understanding the Attention Mechanism and its significance in NLP tasks.

8. Transformer Encoder and Decoder: Implementing Neural Machine Translation with Transformer Encoder and Decoder.

Step 6:

Deep dive into RNN.

Delving into Modern AI Techniques:

1. Representation Learning: Unraveling the essence of modern AI through Autoencoders, Variational Autoencoders, Generative Adversarial Networks, and Generative Large Language Models.

2. Keeping Up with Research Trends: An introduction to Reinforcement Learning and its significance in cutting-edge AI research.

Step 7:

Distributed Machine Learning with TensorFlow: Leveraging TensorFlow’s distributed training capabilities to train models across multiple nodes, accelerating the learning process.

Step 8:

Learn about cloud and CI/CD.

Empowering Seamless Development:

1. Version Control for Code and Data: Ensuring organized and efficient collaboration with version control systems for code and data, keeping track of changes and maintaining data integrity.

2. Adopting DevOps Methodology: Integrating development and operations to streamline software delivery, improve deployment frequency, and enhance overall productivity.

3. Scaling with Cloud Computing Solutions: Leveraging the power of cloud computing to deploy and manage applications at scale, providing flexibility, reliability, and cost-efficiency.

4. Data Engineering for Cloud: Implementing robust data engineering pipelines in the cloud to efficiently process, transform, and store large volumes of data, supporting data-driven decision-making and analytics.

Step 8:

Learn ML Ops.

MLOps Unleashed:

1. Embracing MLOps: A comprehensive introduction to MLOps, the amalgamation of machine learning and DevOps principles, streamlining the ML development lifecycle.

2. Containerization for Efficiency: Leveraging containers for ML models, enabling seamless deployment, scalability, and consistent performance across diverse environments.

3. CI/CD for ML: Implementing Continuous Integration and Continuous Deployment to automate model building, testing, and deployment, ensuring rapid iteration and faster time-to-market.

4. Harmonizing Jenkins and Docker: Integrating CI/CD pipelines with Jenkins and Docker, optimizing the development process and enhancing code consistency.

5. Continuous Monitoring: Establishing robust monitoring mechanisms for ML models in production, ensuring real-time insights into model performance and reliability.

6. Ongoing Training and Feedback: Enabling Continuous Training to update models with new data and feedback, ensuring models remain up-to-date and effective.

And last but not least, regularly read the latest news in AI, ML, Data Science field. Learn new concept and if get time try to participate in Kaggle competition.

Hope this help you. I am a Software Architect | AI, ML, Python, Data Science, IoT, Cloud ⌨️ 👨🏽 💻

I love to learn and share knowledge.

Thank you.

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Think Different - Dhiraj Patra

I am a Software architect for AI, ML, IoT microservices cloud applications. Love to learn and share. https://dhirajpatra.github.io