Overview
Python as a programming language plays a crucial role in the adoption of Machine Learning (ML) in the business environment. The application of the Python language in AI-based technologies is vast, and is used prominently in data science technologies.
Machine Learning with Python course discusses concepts of the Python language such as file operations, sequences, object-oriented concepts, etc. along with some of the most commonly leveraged Python libraries like Numpy, Pandas, Matplotlib, etc. The course will then move on to introduce learners to the detailed mechanisms of Machine Learning. Learners will understand in detail the significance of the implementation of Machine Learning in the Python programming language, and leverage this knowledge in their role as data scientists.
After completing the course, one would have learnt about tools to train machines based on real-world situations using Machine Learning algorithms, as well as to create complex algorithms based on concepts related to deep learning and neural networks. During the latter stage of the course, learners will be introduced to real-world use cases of Machine Learning with Python for a holistic learning experience which would prepare them to create applications efficiently.
Schedule Classes
What You'll learn
This course will help learners understand –
Curriculum
- Introduction to Python Programming
- Introduction to various packages and related functions
- Data Wrangling using Python
- Introduction to Machine Learning with Python
- Supervised Learning - Regression
- Supervised Learning – Classification
- Dimensionality Reduction
- Unsupervised Learning- Clustering
- Additional Performance Evaluation and Model Selection
- Recommendation Engines
- Association Rules Mining
- Time Series Analysis
- Reinforcement Learning
- Artificial Neural Networks and Introduction to Deep Learning
- Overview of Python
- History of Python
- Python Baiscs – variables, identifiers, indentation
- Data Structures in Python (list , string, sets, tuples, dictionary)
- Statements in Python (conditional, iterative, jump)
- OOPS concepts
- Exception Handling
- Regular Expression
- Numpy,Pandas and Matplotlib
- Pandas Module
- Series
- Data Frames
- Numpy Module
- Numpy arrays
- Numpy operations
- Matplotlib module
- Plotting information
- Bar Charts and Histogram
- Box and Whisker Plots
- Heatmap
- Scatter Plots
- NumPy – Arrays
- Data Operations (selection , append , concat , joins)
- Univariate Analysis
- Multivariate Analysis
- Handling Missing Values
- Handling Outliers
- What is Machine Learning?
- Introduction to Machine Learning
- Types of Machine Learning
- Basic Probability required for Machine Learning
- Linear Algebra required for Machine Learning
- Simple Linear Regression
- Multiple Linear Regression
- Assumptions of Linear Regression
- Polynomial Regression
- R2 and RMSE
- Logistic Regression
- Decision Trees
- Random Forests
- SVM
- Naïve Bayes
- Confusion Matrix
- PCA
- Factor Analysis
- LDA
- Types of Clustering
- K-means Clustering
- Agglomerative Clustering
- AUC / ROC
- Silhouette coefficient
- Cross Validation
- Bagging
- Boosting
- Bias v/s Variance
- Need of recommendation engines
- Types of Recommendation Engines
- Content Based
- Collaborative Filtering
- What are Association Rules?
- Association Rule Parameters
- Apriori Algorithm
- Market Basket Analysis
- What is Time Series Analysis?
- Importance of TSA
- Understanding Time Series Data
- ARIMA analysis
- Understanding Reinforcement Learning
- Algorithms associated with RL
- Q-Learning Model
- Introduction to Artificial Intelligence
- History of Neural Network
- Perceptron
- Forward Propagation
- Introduction to Deep Learning
Prerequisites
Interested in this course? Let’s connect!
Course features
FAQs
- Programmers, Developers, Architects, Technical Leads
- Developers who want to be a ‘Machine Learning Engineer’
- Analytics Managers leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Business Analysts who want to understand Artificial Intelligence (AI) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- ‘Python’ professionals who want to design automatic predictive models
A minimum internet speed of 2 Mbps is recommended.
All our sessions are recorded. Even if you miss a class, you can access a recorded video of the session in your LMS.
You get lifetime access to LMS and all the learning material in it.
Yes. Once the course is completed, you need to appear for an objective question-based assessment conducted by Cognixia. Based on your performance on different parameters such as attendance in the sessions, assessment scores, etc. you will be awarded a certificate by Cognixia.
