Learning Path for Machine Learning
2019, Jan 21
Expected Effort: 1012 hours per week.
The path to becoming a ML expert is a marathon journey, not a sprint to the finish. Itâ€™s a diverse space with an everchanging landscape.
But to become an expert, you need to begin from the groundup. 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 PreProcessing
 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 PreProcessing & 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