Machine Learning with Python Training
4.3 /5- Offered byCognixia
Machine Learning with Python Training at Cognixia Overview
Duration | 48 hours |
Total fee | ₹35,400 |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Machine Learning with Python Training at Cognixia Highlights
- Course Completion Certificate, Live Instructor-LED Online Training
- 24/7 Support, Lifetime LMS access
- 48 hours of online training with a live point of contact and great hands-on assignments
Machine Learning with Python Training at Cognixia Course details
- Data Analysts or Financial Analysts from the non-IT industry who want to make a transition to the IT industry
- Individuals, students and corporate professionals who want to upgrade their technical skill set
- Live and interactive online sessions with an industry-expert instructor
- 48 hours of online training with a live point of contact and great hands-on assignments
- A technical team that’s dedicated to answer your questions at any time, regardless of where you’re located
- Provides lifetime Learning Management System (LMS) access, which you can access from across the globe
- After you successfully complete the training program, you will get a course completion certificate
- 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.
Machine Learning with Python Training at Cognixia Curriculum
Introduction to Python Programming
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
Introduction to various packages and related functions.
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
Data Wrangling using Python
NumPy – Arrays
Data Operations (selection , append , concat , joins)
Univariate Analysis
Multivariate Analysis
Handling Missing Values
Handling Outliers
Introduction to Machine Learning with Python
What is Machine Learning?
Introduction to Machine Learning
Types of Machine Learning
Basic Probability required for Machine Learning
Linear Algebra required for Machine Learning
Supervised Learning - Regression
Simple Linear Regression
Multiple Linear Regression
Assumptions of Linear Regression
Polynomial Regression
R2 and RMSE
Supervised Learning – Classification
Logistic Regression
Decision Trees
Random Forests
SVM
Naïve Bayes
Confusion Matrix
Dimensionality Reduction
PCA
Factor Analysis
LDA
Unsupervised Learning- Clustering
Types of Clustering
K-means Clustering
Agglomerative Clustering
Additional Performance Evaluation and Model Selection
AUC / ROC
Silhouette coefficient
Cross Validation
Bagging
Boosting
Bias v/s Variance
Recommendation Engines
Need of recommendation engines
Types of Recommendation Engines
Content Based
Collaborative Filtering
Association Rules Mining
What are Association Rules?
Association Rule Parameters
Apriori Algorithm
Market Basket Analysis
Time Series Analysis
What is Time Series Analysis?
Importance of TSA
Understanding Time Series Data
ARIMA analysis
Reinforcement Learning
Understanding Reinforcement Learning
Algorithms associated with RL
Q-Learning Model
Introduction to Artificial Intelligence
Artificial Neural Networks and Introduction to Deep Learning
History of Neural Network
Perceptron
Forward Propagation
Introduction to Deep Learning
Deep insights into Deep Learning
Multi Layer Perceptron
Backward Propagation
Hyper parameters v/s Parameters
Activation Functions
Programming with Tensor flow
Introduction to Tensorflow
Programming Structures in Tensorflow
Classification and Regression in Tensorflow
Deep Learning model using Tensorflow
Convolutional Neural Network
Basics of Convolutional Neural Network
Transfer Learning
Object Detection using CNN
Recurrent Neural Network
Basics to Recurrent Neural Network
LSTM
Word Embedding
Text Analytics using RNN
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