Machine Learning Course Content
The curriculum for the Machine Learning Course at HKR Trainings is designed on the recommendations of domain experts in the industry. It covers all the requirements to become an expert in Machine Learning. Please go through the below course modules to get an idea of various ML concepts.
Course Curriculum
Module 1: Introduction to Machine Learning
- Need of Machine Learning
- Introduction to Machine Learning
- Types of Machine Learning
- Supervised
- unsupervised
- reinforcement learning
- Machine Learning with Python
- The applications of Machine Learning
Module 2: Data Preprocessing
- Introduction to Data Exploration
- Data Exploration Techniques
- Seaborn
- Data Wrangling
- Missing values in a dataset
- Outlier values in a dataset
- Data Manipulation
- Different types of Joins
- Typecasting
Module 3: Feature Engineering
- Regression
- Factor Analysis Process
- Principal Component Analysis (PCA)
- First Principal component
- Eigen values and PCA
- Feature Reduction
- Linear Discriminant Analysis
- Maximum Seperate Line
Module 4: Supervised Learning
- Understanding the Algorithm
- Supervised Learning Flow
- Types of Supervised Learning
- Types of Classification Algorithms
- Types of Regression Algorithms
- Accuracy Metrics
- Cost Function
- Evaluating Coefficients
- Challenges in Prediction
Module 5: Linear Regression
- Introduction to regression
- Simple linear regression
- Multiple linear regression and assumptions in linear regression
- Math behind linear regression
Module 6: Classification and Logistic Regression
- Introduction to classification
- Linear regression vs logistic regression
- Math behind logistic regression
- Detailed formulas
- The logit function and odds
- Confusion matrix and accuracy
- True positive rate
- False positive rate
- Threshold evaluation with ROCR
Module 7: Decision Tree and Random Forest
- Introduction to tree-based classification
- Understanding a decision tree
- Impurity function
- Entropy
- Understanding the concept of information gain for the right split of node.
- Understanding the concepts of information gain
- Impurity function
- Gini index
- Overfitting
- Pruning
- Pre-pruning
- Post-pruning
- Cost-complexity pruning
- Introduction to ensemble techniques
- Bagging and random forests
- Finding out the right number of trees required in a random forest
Module 8: Unsupervised Learning
- Types of unsupervised learning
- Clustering
- Dimensionality reduction
- Types of clustering
- Introduction to k-means clustering
- Math behind k-means
- Dimensionality reduction with PCA
Module 9: Naive Bayes and Support Vector Machine
- Introduction to probabilistic classifiers
- Understanding Naïve Bayes and math behind the Bayes theorem
- Understanding a support vector machine (SVM)
- Kernel functions in SVM and math behind SVM
Module 10: Natural Language Processing and Text Mining
- Introduction to Natural Language Processing (NLP)
- Introduction to text mining
- Importance and applications of text mining
- How NLP works with text mining
- Writing and reading to word files
- Language Toolkit (NLTK) environment
- Text mining: Its cleaning, pre-processing, and text classification
Module 11: Introduction to Deep Learning
- Introduction to Deep Learning with neural networks
- Biological neural networks vs artificial neural networks
- Understanding perception learning algorithm
- Introduction to Deep Learning frameworks
- TensorFlow constants, variables, and place-holders
Module 12: Time Series Analysis
- What is a time series? Its techniques and applications
- Time series components
- Moving average, smoothing techniques, and exponential smoothing
- Univariate time series models
- Multivariate time series analysis
- ARIMA model and time series in Python
- Sentiment analysis in Python (Twitter sentiment analysis) and text analysis
Module 13: Ensemble Learning
- Overview of Ensemble Learning
- Working of AdaBoost
- AdaBoost Algorithm and Flowchart
- Gradient Boosting
- XGBoost
- Model Selection
- Common Splitting Strategies
- Cross Validation
