This course covers the basics of machine learning, various ML algorithms for regression and classification, feature engineering, and real-life end-to-end projects. The tools taught include sklearn, Python, pandas, NumPy, Jupyter Notebook, Excel, TensorFlow, and scikit-learn. The teaching method includes tutorials on different ML algorithms, feature engineering, and projects. The course is designed for total beginners interested in learning machine learning with Python.
Overview
Syllabus
Machine Learning Tutorial Python -1: What is Machine Learning?.
Machine Learning Tutorial Python - 2: Linear Regression Single Variable.
Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables.
Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function.
Machine Learning Tutorial Python - 5: Save Model Using Joblib And Pickle.
Machine Learning Tutorial Python - 6: Dummy Variables & One Hot Encoding.
Machine Learning Tutorial Python - 7: Training and Testing Data.
Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification).
Machine Learning Tutorial Python - 8 Logistic Regression (Multiclass Classification).
Machine Learning Tutorial Python - 9 Decision Tree.
Machine Learning Tutorial Python - 10 Support Vector Machine (SVM).
Machine Learning Tutorial Python - 11 Random Forest.
Machine Learning Tutorial Python 12 - K Fold Cross Validation.
Machine Learning Tutorial Python - 13: K Means Clustering Algorithm.
Machine Learning Tutorial Python - 14: Naive Bayes Classifier Algorithm Part 1.
Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 2.
Machine Learning Tutorial Python - 16: Hyper parameter Tuning (GridSearchCV).
Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression.
Machine Learning & Data Science Project - 1 : Introduction (Real Estate Price Prediction Project).
Machine Learning & Data Science Project - 2 : Data Cleaning (Real Estate Price Prediction Project).
Machine Learning & Data Science Project - 3 : Feature Engineering (Real Estate Price Prediction).
Machine Learning & Data Science Project - 4 : Outlier Removal (Real Estate Price Prediction Project).
Machine Learning & Data Science Project - 5 : Model Building (Real Estate Price Prediction Project).
Machine Learning & Data Science Project - 6 : Python Flask Server (Real Estate Price Prediction).
Machine Learning & Data Science Project - 7 : Website or UI (Real Estate Price Prediction Project).
Deploy machine learning model to production AWS (Amazon EC2 instance).
Data Science & Machine Learning Project - Part 1 Introduction | Image Classification.
Data Science & Machine Learning Project - Part 2 Data Collection | Image Classification.
Data Science & Machine Learning Project - Part 3 Data Cleaning | Image Classification.
Data Science & Machine Learning Project - Part 4 Feature Engineering | Image Classification.
Data Science & Machine Learning Project - Part 5 Training a Model | Image Classification.
Data Science & Machine Learning Project - Part 6 Flask Server | Image Classification.
Data Science & Machine Learning Project - Part 7 Build Website | Image Classification.
Data Science & Machine Learning Project - Part 8 Deployment & Exercise | Image Classification.
What is feature engineering | Feature Engineering Tutorial Python # 1.
Outlier detection and removal using percentile | Feature engineering tutorial python # 2.
Outlier detection and removal: z score, standard deviation | Feature engineering tutorial python # 3.
Outlier detection and removal using IQR | Feature engineering tutorial python # 4.
Introduction | Deep Learning Tutorial 1 (Tensorflow Tutorial, Keras & Python).
Why deep learning is becoming so popular? | Deep Learning Tutorial 2 (Tensorflow2.0, Keras & Python).
What is a neuron? | Deep Learning Tutorial 3 (Tensorflow Tutorial, Keras & Python).
What is a Neural Network | Deep Learning Tutorial 4 (Tensorflow2.0, Keras & Python).
Install tensorflow 2.0 | Deep Learning Tutorial 5 (Tensorflow Tutorial, Keras & Python).
Pytorch vs Tensorflow vs Keras | Deep Learning Tutorial 6 (Tensorflow Tutorial, Keras & Python).
Neural Network For Handwritten Digits Classification | Deep Learning Tutorial 7 (Tensorflow2.0).
Activation Functions | Deep Learning Tutorial 8 (Tensorflow Tutorial, Keras & Python).
Derivatives | Deep Learning Tutorial 9 (Tensorflow Tutorial, Keras & Python).
Matrix Basics | Deep Learning Tutorial 10 (Tensorflow Tutorial, Keras & Python).
Loss or Cost Function | Deep Learning Tutorial 11 (Tensorflow Tutorial, Keras & Python).
Gradient Descent For Neural Network | Deep Learning Tutorial 12 (Tensorflow2.0, Keras & Python).
Implement Neural Network In Python | Deep Learning Tutorial 13 (Tensorflow2.0, Keras & Python).
Stochastic Gradient Descent vs Batch Gradient Descent vs Mini Batch Gradient Descent |DL Tutorial 14.
Chain Rule | Deep Learning Tutorial 15 (Tensorflow2.0, Keras & Python).
Tensorboard Introduction | Deep Learning Tutorial 16 (Tensorflow2.0, Keras & Python).
GPU bench-marking with image classification | Deep Learning Tutorial 17 (Tensorflow2.0, Python).
Customer churn prediction using ANN | Deep Learning Tutorial 18 (Tensorflow2.0, Keras & Python).
Precision, Recall, F1 score, True Positive|Deep Learning Tutorial 19 (Tensorflow2.0, Keras & Python).
Dropout Regularization | Deep Learning Tutorial 20 (Tensorflow2.0, Keras & Python).
Handling imbalanced dataset in machine learning | Deep Learning Tutorial 21 (Tensorflow2.0 & Python).
Applications of computer vision | Deep Learning Tutorial 22 (Tensorflow2.0, Keras & Python).
Simple explanation of convolutional neural network | Deep Learning Tutorial 23 (Tensorflow & Python).
Image classification using CNN (CIFAR10 dataset) | Deep Learning Tutorial 24 (Tensorflow & Python).
Convolution padding and stride | Deep Learning Tutorial 25 (Tensorflow2.0, Keras & Python).
Data augmentation to address overfitting | Deep Learning Tutorial 26 (Tensorflow, Keras & Python).
Transfer Learning | Deep Learning Tutorial 27 (Tensorflow, Keras & Python).
Image classification vs Object detection vs Image Segmentation | Deep Learning Tutorial 28.
Popular datasets for computer vision: ImageNet, Coco and Google Open images | Deep Learning 29.
Sliding Window Object Detection | Deep Learning Tutorial 30 (Tensorflow, Keras & Python).
What is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python).
Object detection using YOLO v4 and pre trained model | Deep Learning Tutorial 32 (Tensorflow).
What is Recurrent Neural Network (RNN)? Deep Learning Tutorial 33 (Tensorflow, Keras & Python).
Types of RNN | Recurrent Neural Network Types | Deep Learning Tutorial 34 (Tensorflow & Python).
Vanishing and exploding gradients | Deep Learning Tutorial 35 (Tensorflow, Keras & Python).
Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python).
Simple Explanation of GRU (Gated Recurrent Units) | Deep Learning Tutorial 37 (Tensorflow & Python).
Bidirectional RNN | Deep Learning Tutorial 38 (Tensorflow, Keras & Python).
Converting words to numbers, Word Embeddings | Deep Learning Tutorial 39 (Tensorflow & Python).
Word embedding using keras embedding layer | Deep Learning Tutorial 40 (Tensorflow, Keras & Python).
What is Word2Vec? A Simple Explanation | Deep Learning Tutorial 41 (Tensorflow, Keras & Python).
Word2Vec Part 2 | Implement word2vec in gensim | | Deep Learning Tutorial 42 with Python.
Distributed Training On NVIDIA DGX Station A100 | Deep Learning Tutorial 43 (Tensorflow & Python).
Tensorflow Input Pipeline | tf Dataset | Deep Learning Tutorial 44 (Tensorflow, Keras & Python).
Optimize Tensorflow Pipeline Performance: prefetch & cache | Deep Learning Tutorial 45 (Tensorflow).
What is BERT? | Deep Learning Tutorial 46 (Tensorflow, Keras & Python).
Text Classification Using BERT & Tensorflow | Deep Learning Tutorial 47 (Tensorflow, Keras & Python).
tf serving tutorial | tensorflow serving tutorial | Deep Learning Tutorial 48 (Tensorflow, Python).
Quantization in deep learning | Deep Learning Tutorial 49 (Tensorflow, Keras & Python).
Taught by
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