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👩‍💻 LEARNING PATH 👨‍💻

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֍ Pre requisites for starting your Machine Learning journey:

֍ Basics:

• Matrices and Linear Algebra Fundamentals
• Database Basics:-
  • Relational vs Non Relational Databases
  • SQL + Joins
  • NoSql
• Tabular Data
• DataFrames and Series
• Extract, Transform, Load(ETL)
• Reporting vs BI vs Analytics
• Data Formats:-
  • json
  • xml
  • csv
• Regular Expressions (RegEx)

֍ Python Programming:

• Python Basics:-
  • Expressions
  • Variables
  • Data Structures
  • Functions
  • Install packages (via pip, conda, e.t.c)
• Important Libraries:-
  • Numpy
  • Pandas
• Virtual Environments
• Jupyter Notebooks

֍ Python Programming:

• Data Mining
• Web Scraping
• Public Datasets
• Kaggle

֍ Exploratory Data Analysis/Data Munging/Wrangling:-

• Principal Component Analysis (PCA)
• Dimensionalty and Numerosity Reduction
• Normalisation
• Data Scrubbing, Handling Missing Values
• Unbiased Estimators
• Binning Sparse Values
• Feature Extraction
• Denoising
• Sampling

֍ Choose your path:-

֍ Data Science:-

• Data Science
• Machine Learning
• Deep Learning

֍ Data Engineer:-

• Data Engineer
• Big Data Engineer

֍ Data Science:-

֍ Statistics:

• Probability Theory
  • Randomness, random variables and random sample
  • Probability distribution
  • Conditional probability and Bayes Theorem
  • Statistical Independence
  • Cumulative distribution function (cdf)
  • Probability density function (pdf)
  • Probability mass function (pmf)
• Continuous Distributions (pdf's)
  • Normal/Gaussian
  • Uniform (continuous)
  • Beta
  • Dirichlet
  • Exponential
  • chi-squared
• Discrete Distributions (pmf's)
  • Uniform (discrete)
  • Binomial
  • Multinomial
  • Hypergeometric
  • Poisson
  • Geometric
• Summary statistics
  • Expectation and mean
  • Variance and standard deviation
  • Covariance and Correlation
  • Median, quartile
  • Interquartile range
  • Percentile/quantile
  • Mode
• Important Laws
  • Law of large numbers
  • Central limit theorem
• Estimation
  • Maximum Likelihood estimation
  • Kernel Density Estimation
• Hypothesis Testing
  • p-Value
  • chi-square test
  • F test
  • t test
• Confidence Interval
• Monte Carlo Method

֍ Visualisation:

• Chart suggestions thought starter
• Python
  • Matplotlib
  • plotnine
  • Bokeh
  • seaborn
  • ipyvolume
• Web
  • Vega-Lite
  • D3.js
• Dashboards
  • Dash
• BI
  • Tablaeu
  • PowerBI

֍ Machine Learning:-

֍ General:

• Concepts, Inputs and Attributes
  • Categorical Variables
  • Ordinal Variables
  • Numerical Variables
• Cost functions and gradient descent
• Overfitting/ Underfitting
• Training, validation and test data
• Precision vs Recall
• Bias and Variance
• Lift

֍ Methods:

• Supervised Learning
  • Regression
    • Linear Regression
    • Poisson Regression
  • Classification
    • Classification Rate
    • Decision Trees
    • Logistic Regression
    • Naive Bayes Classifiers
    • K Nearest Neighbour
    • Support Vector Machines
    • Gaussian Mixture Models
• Unsupervised Learning
  • Clustering
    • Hierarchical Clustering
    • K Means Clustering
    • DBSCAN
    • HDBSCAN
    • Fuzzy C Means
    • Mean Shift
    • Agglomerative
    • OPTICS
  • Association Rule Learning
    • Apriori Algorithm
    • ECLAT Algorithm
    • FP Trees
  • Dimensionality Reduction
    • Principal Component Analysis
    • Random Projection
    • NMF
    • T-SNE
    • UMAP
• Ensemble Learning
  • Boosting
  • Bagging
  • Stacking
• Reinforcement Learning
  • Q Learning

֍ Use Cases:

• Sentiment Analysis
• Collaborative Filtering
• Tagging
• Prediction

֍ Tools:

• Important libraries
  • scikit-learn
  • spacy(NLP)

֍ Deep Learning:-

֍ Papers:

• Read DL Papers with concepts
• Read DL Papers with code

֍ Neural Networks:

• Understanding Neural Networks
• Loss functions
• Activation functions
• Weight initialisation
• Vanishing/Exploding gradient Problem

֍ Architectures:

• Feedforward Neural Network
• Autoencoder
• Convolutional Neural Network
  • Pooling
• Recurrent Neural Network
  • LSTM
  • GRU
• Transformer
  • Encoder
  • Decoder
  • Attention
• Siamese Network
• Generative Adversarial Network (GAN)
• Evolving Architectures/ NEAT
• Residual Connections

֍ Training:

• Optimizers
  • SGD
  • Momentum
  • AdaGrad
  • AdaDelta
  • Nadam
  • RMSProp
• Learning Rate Schedule
• Batch Normalisation
• Batch Size Effects
• Regularisation
  • Early Stopping
  • Dropout
  • Parameter Penalties
  • Data Augmentation
  • Adversarial Training
• Multitask Learning
• Transfer Learning
• Curriculum Learning

֍ Tools:

• Important Libraries
  • Awesome Deep Learning
  • Huggingface Transformers
• Tensorflow
• PyTorch
• Tensorboard
• MLFlow

֍ Model Optimisation:

• Distillation
• Quantization
• Neural Architecture Search (NAS)

֍ Data Engineer:-

• Summary of Data Formats
• Data Discovery
• Data Source and Acquisition
• Data Integration
• Data Fusion
• Transformation and Enrichment
• Data Survey
• OpenRefine
• How much Data
• Using ETL
• Data Lake vs Data Warehouse
• Dockerize your Python Application

֍ Big Data Engineer:-

֍ Big Data Architecture:

• Architectural Patterns and Best Practices

֍ Principles:

• Horizontal vs Vertical Scaling
• Map reduce
• Data replication
• Name and Data Nodes
• Job and Task Tracker

֍ Tools:

• Check the awesome big data list
• Hadoop (large data)
  • HDFS
  • Loading data with Sqoop and Pig
  • Storm: Hadoop Realtime
• Spark (in memory)
• RAPIDS (on GPU)
• Flume, Scribe: For unstruct Data
• Data Warehouse with Hive
• Elastic (EKL) Stack
  • to get data (e.g logging) search, analyze and visualise it in realtime
• Avro
• Flink
• Dask
• Numba
• Onnx
• OpenVino
• MLFlow
• Kafka and KSQL
• Databases
  • Cassandra
  • MongoDB, Neo4j
• Scalability
  • ZooKeeper
  • Kubernetes
• Cloud Services
  • AWS Sagemaker
  • Google ML Engine
  • Microsoft Azure Machine Learning Studio
• Awesome Production ML