How to become a Pro in Machine Learning and Deep Learning Concepts ??

Source: Deep Learning on Medium

How to become a Pro in Machine Learning and Deep Learning Concepts

Here goes the learning path to become a pro in solving machine learning problems,

  1. Learn any programming language (Python is highly preferable)
  2. EDA concepts (2D plots, 3D plots, pair plots, PDF, CDF, univariate analysis, Mean, Median, Mode, variance, Std-var, Percentiles, Quantiles, Box plot, Violin plot, Multivariate analysis)
  3. Probability and statistics (Gaussian/Normal distribution, Symmetric distribution, Skewness and Kurtosis, Standard normal variate (z) and standardization, Kernel density estimation, Sampling distribution & Central Limit theorem, Q-Q Plot, Uniform Distribution, Bernoulli and Binomial distribution, Log-normal and power-law distribution, Co-variance, Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Correlation vs Causation, Confidence Intervals, Hypothesis testing, Re-sampling and permutation test, K-S Test)
  4. Linear Algebra (Point/Vector (2-D, 3-D, n-D) , Dot product and the angle between 2 vectors, Projection, unit vector, Equation of a line (2-D), plane(3-D) and hyperplane (n-D) , Distance of a point from a plane/hyperplane, half-spaces, The equation of a circle (2-D), sphere (3-D) and hypersphere (n-D), Equation of an ellipse (2-D), ellipsoid (3-D) and hyper-ellipsoid (n-D), Square, Rectangle, Hyper-cube and Hyper-cubid)
  5. Dimensionality reduction (PCA & T-SNE)
  6. Miscellaneous Topics (Imbalanced vs balanced dataset, co-occurrence matrix, a similarity matrix, Train and test set differences, LOF, Normalization, Handling categorical and numerical features., Handling missing values by imputation, Curse of dimensionality, Bias-Variance tradeoff, Accuracy, Confusion matrix, TPR, FPR, FNR, TNR, Precision & recall, F1-score, Receiver Operating Characteristic Curve (ROC) curve and AUC, Log-loss, R-Squared/ Coefficient of determination, Median absolute deviation (MAD), Distribution of errors, Gradient descent, Learning rate, SGD algorithm, Feature engineering, Hyperparameter tuning, K-fold cross-validation)
  7. Machine learning( Supervised learning(KNN, SVM, Logistic Regression, Linear regression, Naive Bayes, Decision Trees, Ensemble Models) Unsupervised learning(K-Means, K-Means++, K-Medoids, Hierarchical clustering, DBSCAN, NMF, SVD))
  8. Deep Learning(Neural Networks, RNN, LSTMs, GANs, DCGANs, Transfer Learning and many more…)
  9. Practice in kaggle Knowledge competitions to get hands-on practice

These are the things you need to learn and do to become pro in machine learning.

If you have questions related to any concept comment below.

Happy Machine learning!

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