Learning Path for Machine Learning

Expected Effort: 10-12 hours per week.

The path to becoming a ML expert is a marathon journey, not a sprint to the finish. It’s a diverse space with an ever-changing landscape.

But to become an expert, you need to begin from the ground-up. So where should you start? What are the essential techniques and concepts you should learn before jumping into more advanced machine learning topics?

This learning path has been curated with these questions in mind.

  • January: Understanding Data Science and getting started with Python
    • Understanding Data Science
    • What does a data scientist do?
    • Setting up the system
    • Getting started with Python
    • Why is statistics important?
    • Statistics: Descriptive
    • Introduction to Numpy/Pandas
  • February: A bit of Mathematics and Statistics
    • Introduction to Probability
    • Statistics: Inferential
    • Data Pre-Processing
    • Exploratory Data Analysis (EDA)
    • Projects On EDA
    • Linear Algebra Basics

    Start Joining Data Science Communities

  • March: Machine Learning Tools & Techniques (Basics)
    • Understanding Data Science Pipeline
    • Linear Regression
    • Decision Tree Algorithm
    • Naive Bayes
    • Support Vector Machine (SVM)
    • Logistic Regression
    • Regression Project
    • Classification Project
    • Unsupervised Learning
    • Unsupervised Learning Project

    Getting Familiar With Linux Command Line

  • April: Machine Learning Tools & Techniques (Advanced)
    • Understanding Ensemble Learning
    • Random Forest
    • Boosting Algorithm (XGBoost, LightGBM, Catboost)
    • Time Series
    • Time Series Project
  • May: Machine Learning Tools & Techniques (Hyperparameter Tuning & Validation)
    • Validation Strategies
    • Hyperparameter Tuning
    • Feature Engineering
    • Ensemble Learning - Stacking & Blending

    Subscribe to Data Science News Letters

  • June : Machine Learning Tools & Techniques (Recommender Systems)
    • Matrix Algebra
    • SVD & PCA
    • Working with different types of Data
    • Recommender Systems
    • Recommender Systems Project
  • July : Getting Started with Neural Networks / Deep Learning
    • Setting up the system for deep learning
    • Introduction To Deep Learning (MLP)
    • Introduction To Keras

    Start Profile & Resume Building

    • Participate In Competitions & Hackathons
    • Learn Github
    • Write Blogs
  • August : Computer Vision
    • Understanding DL
    • Architectures -1 (CNN)
  • September : Computer Vision
    • Projects On Computer Vision
    • Take a computer vision course
  • October: Natural Language Processing
    • Understanding DL Architectures - 2 (RNN, LSTM, GRU)
    • Text Pre-Processing & Cleaning
    • Text Classification
  • November: Natural Language Processing (Advanced)
    • Topic Modeling
    • Text Summarization
    • Word Embeddings
    • NLP Project
    • NLP Course
  • December: Apply For Internships
    • Search For Internships
    • Up Level your Data Science Resume Course
    • Ace Data Science Interview Course
    • Way Forward

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