How to become a Pro in Machine Learning & 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…