Machine Learning (Python)

Machine learning with python is a comprehensive training course that helps you take a deep dive into machine learning basics and understand how Python programming language integrates to the core. The machine learning with python course is focused on delivering best-in-class learning on statistical modelling, regression and clustering algorithms and have an in-depth understanding of the interoperability between python and machine learning and the magical applications it can create.

Technofine24 is a prominent leader in offering you an opportunity to transform your theoretical knowledge on machine learning and python, into the much preferred and job-oriented practical skill through multiple projects included in machine learning using python training and certification course. Our certified and seasoned instructors work to build a solid foundation on developing algorithms, creating functions, exception handling, data analysis etc.

Our diverse training delivery modes of machine learning using python course, offered in custom schedules, help corporate and individual learners find a best-fit learning solution mapping their career and training goals.

1. Simple Linear Regression
2. Simple Linear Regression In Python
3. Multiple Linear Regression
4. Multiple Linear Regression In Python
5. Industry Relevance Of Linear Regression

1. Data mining classifier technique
2. Application of KNN classifier
3. Lazy learner classifier
4. Altering hyperparameter(k) for better accuracy

1. Rule based classification method
2. Different nodes for develop decision trees
3. Discretization
4. Entropy
5. Greedy approach
6. Information gain

1. Difference between cross sectional and time series data
2. Different component of time series data
3. Visualization techniques for time series data
4. Model based approach
5. Data driven based approach

1. Dimension reduction
2. Application of PCA
3. PCA & its working
4. SVD & its working

1. Univariate Logistic Regression
2. Multivariate Logistic Regression: Model
3. Building And Evaluation
4. Logistic Regression:
5. Industry Applications

1. Black box
2. SVM hyperplane
3. Max margin hyperplane
4. Kernel tricks for non linear spaces

1. Challenges with standalone model
2. Reliability and performance of a standalone model
3. Homogeneous & Heterogeneous Ensemble Technique
4. Bagging & Boosting
5. Random forest
6. Stacking
7. Voting & Averaging technique

1. Difference between Supervised and Unsupervised Learning
2. Prelims of clustering
3. Measuring distance between record and groups
4. Linkage functions
5. Dendrogram

Projects on Machine Learning focused on implementing the concepts of Machine Learning Algorithms in real world problems.