Professionals are prepared with a variety of abilities after completing this online Machine Learning with Python course, including;
- Programming in Python, includes distribution, user-defined functions, dataset import, and more
- utilizing the Pandas library to manipulate and analyze data
- use Python libraries to visualize data Seaborn, ggplot, and Matplotlib
- Construction of interquartile range, variance, and standard deviation models for data distribution
- Hypothesis testing for conditional probability calculation
- Making use of analysis of variance (ANOVA)
- constructing, assessing, and measuring performance indicators for linear regression models
- Dimensionality reduction is used
- constructing logical regression models, assessing model inputs, and tracking performance metrics
- Performing K-means clustering and hierarchical clustering
- Building KNN algorithm models to find the optimum value of K
- Building decision tree models for both regression and classification problems
- Using ensemble techniques like averaging, weighted averaging and max voting
- Using techniques of bootstrap sampling, bagging and boosting
- Building random forest models
- Finding the optimum number of components/factors using scree plot and one-eigenvalue criterion
- Performing PCA/Factor Analysis
- Building appropriate algorithms with key metrics like support, confidence and lift
Building recommendation engines using UBCF and IBCF
Module 1: Introduction to Machine Learning
- Basics of Machine Learning
- What and why Machine Learning
- Applications of Machine Learning
- Types of Machine Learning
- Main Challenges of Machine Learning
Module 2: Scikit Learn
- Introduction to Scikit Learn
- Features of Scikit-Learn
- Conventions
- Implemenation steps
Module 3: Linear Algebra
- Vectors (2D,3D)
- Dot Product
- Hyperplane
- Square, Rectangle
- Hypercube
Module 4: Probability
- Data types and its measures
- Random Variables, its application with variables
- Probability-Application with examples
- Probability distribution with examples
- Sampling Funnel-why and how
Module 5: Statistics
- What is Statistics
- Basic Terminologies in Statistics
- Types of Statistics
- Descriptive Statistics
- Measure of Central Tendency (Mean, median, mode)
- Measures of dispersion (Variance, Standard Deviation, Range-its derivation)
- Measures of Skewness & kurtosis
- Inferential Statistics
Module 6: Data pre-processing
- Is your data clean?
- What is Data Pre-processing?
- Data cleaning techniques
Module 7: Exploratory Data Analysis (EDA)
- Introduction
- 2D Scatter-plot
- 3D Scatter-plot
- Pair plots
- Univariate, Bivariate and Multivariate
- Histogram
- Box-plot
- Variance, Standard Deviation
- Median
- IQR (InterQuartile Range)
Module 8: Feature Engineering
- Introduction
- Need for Feature Engineering in Machine Learning
- Steps in Feature Engineering
- Feature Engineering Techniques
Module 9: Performance Metrics & Parameter Tuning
- Confusion Matrix
- ROC Curve
- Cross Validation in Machine Learning
- K fold Cross Validation & Grid search
Module 10: Supervised Learning – Regression I
- Linear Regression – Mathematical Intuition
- Programming of Linear Regression in Python-scikit learn
Module 11: Supervised Learning – Classification
- Difference between regression and classification
- Various Algorithms in Classification
- Logistic Regression
Module 12: Unsupervised Learning – Clustering
- Unsupervised Learning
- Types of Unsupervised Learning
- Applications of Unsupervised Learning
- Introduction to Clustering Algorithms
- Types of Clustering Algorithms
- What is K-Means Clustering?
- Implementation of K-Means Clustering
- Improving Models
Module 13: Unsupervised Learning – Association Rule Mining
- What is Association Rule Mining?
- Algorithms in Association Rule Mining
- Implementation of Apriori in Python
