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From Linear Regression to Deep Neural Networks. Master the math, algorithms, and code behind AI.
12+
Core Algorithms
Deep
Math Concepts
5+
Python Projects
Hero
Neural Networks
What is ML? AI vs ML vs DL, and the learning pipeline.
NumPy, Pandas, Matplotlib & Seaborn essentials for ML.
Statistics, distributions, correlations — understanding your data.
Missing values, outliers, encoding, and feature scaling.
Splitting data, K-Fold cross-validation, avoiding leakage.
Simple & Multiple Regression, Cost Function, Gradient Descent.
Non-linear data, Bias-Variance Tradeoff, vs Linear models.
Preventing overfitting with L1/L2 penalties & feature selection.
Binary classification, Sigmoid function, Log loss, Decision boundaries.
Hyperplanes, Margins, Kernel Trick (RBF, Poly) for complex classification.
Distance metrics (Euclidean, Manhattan), Lazy learning, K-selection.
Probabilistic classifiers, Bayes Theorem, Gaussian & Multinomial variants.
Tree construction, Entropy, Information Gain, Gini Impurity, Pruning.
Ensemble learning, Bagging, Bootstrapping, Feature randomness.
Precision, Recall, F1 Score, ROC-AUC, Confusion Matrix.
Unsupervised learning, Elbow method, hierarchical clustering, DBSCAN.
Principal Component Analysis (PCA) and t-SNE basics.
Perceptrons, MLP, Activation Functions (ReLU, Sigmoid), Backprop.
Convolutional Neural Networks for image recognition.
Recurrent Neural Networks for sequential data, time series.
Text preprocessing, Bag of Words, TF-IDF, Sentiment Analysis.
High Demand
AI Engineers are the most sought-after roles today.
Future Proof
AI is transforming every industry rapidly.
Solve Real Problems
Build prediction models, recommenders, and more.
Creative Innovation
Generate art, music, and text with Generative AI.