What is Python
Python training in Pune by AnalytIQ Learning is designed to you to acquire knowledge in every module with a clear understanding skill set. This training program will focus on providing you with the latest concepts and industry-relevant topics.
Why take this course?
Python is one of the hot and in trend skill with wide-ranging applications. Python Training is hands-on training for candidates to get better at their coding/programming skills along with building a strong foundation in Python Technology Stack - Django, Machine Learning, Artificial Intelligence and DevOps.
Our proficient & well experienced trainer and well planned course materials ensure for 100% success in interviews.
• The company offers assured placement assistance to its certified students
• Students get to work on Live Projects
• Small batch size
• Weekend batches for working professionals
• Online training of the technology also available
• Course Content created by leading industry professionals.
• Small batch size for personalized training.
• Study Material and Worksheets included.
• Lifetime access to mentoring and revision classes.
• 250+ network of recruiters in Pune for placements.
• All our training programs are based on current industry standards.
• Training will be conducted on daily& weekly basis and also, we can customize the training schedule as per the candidate requirements.
• Live Project based training with trainers having 5 to 15 years of Industry Experience.
• Training will be conducted by certified professionals.
• Our classrooms are fully geared up with projectors &Wi-Fi access.
• 100 % free personality development classes which includes Spoken English, Group Discussions, Mock Job interviews & Presentation skills.
Placement Preparation Process:
1. HR team conducts grooming sessions in grooming session HR team focuses on personality development, how to interact with interviewers, how to speak English, how to handle & control nervousness & how to represent your point of view in front of interviewer.
2. After completion of 60% training course content, we will arrange the interview calls to students & prepare them to F2F interaction.
3. Corporate Study Material along with Assignments.
4. Trained Candidates on Aptitude & Test Papers.
5. CV Designing as per the JD (Job Description).
6. Prepare Candidates for HR Interview (HR Q&A).
7. Schedule Mock Exams and Mock Interviews.
8. Schedule Interview with Companies till Placements
- What is ML?
- Types of ML
- ML package :scikit-learn
- How to install anaconda
- Introduction to NumPy
- Creating an array
- Class and Attributes of ndarray
- Basic Operations
- Stack operations
- Mathematical Functions of NumPy
- Introduction to Pandas
- Understanding DataFrame
- Concatenating and appending DataFrames
- loc and iloc
- Drop columns or rows
- Map and apply
- Dealing with missing data
- Handling categorical data
- Encoding class labels
- Split data into training and testing sets
- Bringing Features onto same scale
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Evaluate Performance of a linear regression model
- Overfitting and underfitting
- K-means Clustering
- Elbow method
- Principal components analysis(PCA)
- PCA step by step
- Implementing PCA with scikit-learn
- LDA with scikit-learn
- KNN theory
- Implementing KNN with scikit-learn
- KNN Parameters
- How to find Nearest Neighbors
- Writing Own KNN classifier from scratch
- Logistic Regression theory
- Implementing Logistic regression with scikit-learn
- Logistic Regression Parameters
- Multi-class classification
- MNIST digit dataset with Logistic Regression
- Predictive modeling on adult income dataset
- SVM theory
- Implementing SVM with scikit-learn
- SVM Parameters:
- C and gamma
- Plot hyperplane for linear classification
- Decision function
- Theory behind decision tree
- Implementing decision tree with scikit-learn
- Decision tree parameters
- Combining multiple decision trees via Random forest
- How random forest works..?
- Theory Naive Bayes Algorithm
- Features extraction
- Text Classification
- Cross validation via K-Fold
- Tuning hyperparameters via grid search
- Confusion matrix
- Recall and Precision
- ROC and AUC
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