Machine Learning with Python Training
4.8 /5- Offered byCognixia
Machine Learning with Python Training at Cognixia Overview
Duration | 48 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Machine Learning with Python Training at Cognixia Highlights
- Course completion certificate from Cognixia
- 24/7 Support, Lifetime LMS access
- Key Topics covered : Introduction to Python programming, Machine learning, Regression, Classification, Neural networks and more
- Live Instructor-LED Online Training
Machine Learning with Python Training at Cognixia Course details
- Live and interactive online sessions with an industry-expert instructor
- 48 hours of online training
- Dedicated query resolution support
- Lifetime LMS access
- Globally recognized course completion certificate by Cognixia
- Data analysts or financial analysts from a non-IT industry who want to make a transition to an IT role
- Individuals, students and corporate professionals who want to upgrade their technical skill set
- This Machine Learning with Python course discusses the 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 also introduces learners to the detailed mechanisms of machine learning. Through this course, learners will gain an in-depth understanding of the significance of implementing machine learning in the Python programming language, and leverage this knowledge in their role as data scientists.
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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