Course Overview
Curriculum
- 13 Sections
- 85 Lessons
- 60 Hours
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- Introduction to Machine Learning3
- Statistics, Probability with Python13
- 2.1Types of Data
- 2.2Introduction and usage of the following :
- 2.3Introduction and usage of – Mean, Median, Mode,
- 2.4Introduction and usage of – Variation and Standard Deviation
- 2.5Introduction and usage of – Probability Density Function
- 2.6Introduction and usage of – Probability Mass Function
- 2.7Introduction and usage of – Common Data Distributions
- 2.8Introduction and usage of – Percentiles and Moments
- 2.9Introduction and usage of – Introduction to matplotlib
- 2.10Introduction and usage of – Advanced Visualization with Seaborn
- 2.11Introduction and usage of – Covariance and Correlation
- 2.12Introduction and usage of – Conditional Probability
- 2.13Introduction and usage of – Bayes’ Theorem
- Predictive Modelling4
- Machine Learning with Python9
- Recommender Systems3
- More Machine Learning techniques8
- Real world data understanding9
- 7.1Bias-Variance Trade off
- 7.2K-Fold Cross-Validation to avoid overfitting
- 7.3Data Cleaning and Normalization
- 7.4Normalizing numerical data
- 7.5Detecting outliers
- 7.6Feature Engineering & Curse of Dimensionality
- 7.7Imputation Techniques for Missing Data
- 7.8Handling Unbalanced Data Oversampling, Undersampling, and SMOTE
- 7.9Binning, Transforming, Encoding, Scaling & Shuffling
- Machine Learning on Big Data8
- More Designs – Experimentation6
- Visualization with Tableau3
- Deep Learning and Neural Networks15
- 11.1Deep Learning Prerequisites
- 11.2The History of Artificial Neural Networks
- 11.3Tensorflow Playground
- 11.4Deep Learning features
- 11.5Introducing Tensorflow
- 11.6Using Tensorflow
- 11.7Introducing Keras
- 11.8Using Keras
- 11.9Convolutional Neural Networks (CNN’s)
- 11.10Using CNN’s
- 11.11Recurrent Neural Networks (RNN’s)
- 11.12Using RNN
- 11.13Transfer Learning
- 11.14Tuning Neural Networks Learning Rate and Batch Size Hyperparameters
- 11.15Deep Learning Regularization with Dropout and Early Stopping
- Data Science in Cloud2
- Project2
